code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepar... | 702 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : str = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolv... | 2 | 0 |
"""simple docstring"""
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : int = logging... | 703 |
"""simple docstring"""
from math import factorial
def _a ( _SCREAMING_SNAKE_CASE = 100 ) -> int:
return sum(int(_SCREAMING_SNAKE_CASE ) for x in str(factorial(_SCREAMING_SNAKE_CASE ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').s... | 2 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Any = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
... | 704 |
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class ... | 2 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(snake_case__) ... | 705 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, sl... | 2 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[Any] = {
'configuration_roberta': ['... | 706 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class __A :
'''simple docstring'''
def __init__( self : List[Any] , UpperCAmelCase_ : list[str] ) ->List[Any]:
"""simple docstring"""
... | 2 | 0 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __A (ctypes.Structure):
'''simple docstring'''
__lowercase: Optional[Any] = [("""size"... | 707 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_visio... | 2 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInp... | 708 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 2 | 0 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> bool:
snake_case_ = [int(_SCREAMING_SNAKE_CASE ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(_SCREAMING_SNAKE_CASE ) == 4 and all(0 <= int(_SCREAMING_SNAKE_CASE ) <= 254 for o... | 709 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if index == number_of_items:
return 0
snake_case_ = 0
snake_case_ ... | 2 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
loggin... | 710 |
"""simple docstring"""
from math import factorial
def _a ( _SCREAMING_SNAKE_CASE = 20 ) -> int:
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(fact... | 2 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__SCREAMING_SNAKE_CASE : List[str] = '\\n@misc{chen2021evaluating,... | 711 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _a ( _SCREAMING_SNAKE_CASE = 8 ) -> str:
snake_case_ = ascii_letters + digits + punctuation
return "".joi... | 2 | 0 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> tuple[float | int, list[tuple[int, int]]]:
snake_case_ , snake_case_ ... | 712 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common i... | 2 | 0 |
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
__SCREAMING_SNAKE_CASE... | 713 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 2 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from... | 714 |
"""simple docstring"""
from __future__ import annotations
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> list[int]:
snake_case_ = 0
snake_case_ = len(_SCREAMING_SNAKE_CASE ) - 1
while i < j:
if nums[i] + nums[j... | 2 | 0 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def ... | 715 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
't... | 2 | 0 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline... | 716 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = 'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE : Dict = 'TRWAGMYFPDXBNJZSQVHLCKE'
def _a ( _SCREAMING_SNAKE_CASE ) -> bool:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCR... | 2 | 0 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__SCREAMING_SNAKE_CASE : List[Any] = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 S... | 717 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[str] = logging.... | 2 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'microsoft/unispeech-sat-base-100h-li... | 718 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("""The given input must be positive""" )
# get the generated string sequence
snake_case_ ... | 2 | 0 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __A (tf.keras.layers.Layer):
'''simple docstring'''
def __init_... | 719 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
... | 2 | 0 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
__SCREAMING_SNAKE_CASE : Union[str, Any] = 'src/transformers'
# Matches is_xxx_available()
__SCREAMING_SNAKE_CASE : List[str] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_st... | 720 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__... | 2 | 0 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurati... | 721 |
"""simple docstring"""
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
im... | 2 | 0 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def A ( lowercase , lowercase ) -> float:
'''simple docstring'''
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowercase , lowercase ) ) )
def A ( lowerc... | 3 |
def A ( lowercase , lowercase ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCamelCase = str(bin(lowercase ) )[2:] # remove the leading "0b"
UpperCamelCase = str(bin(lowercase ) )[... | 3 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from tran... | 3 |
import re
def A ( lowercase ) -> str:
'''simple docstring'''
if len(re.findall('[ATCG]' , lowercase ) ) != len(lowercase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doc... | 3 | 1 |
from collections.abc import Callable
def A ( lowercase , lowercase , lowercase ) -> float:
'''simple docstring'''
UpperCamelCase = a
UpperCamelCase = b
if function(lowercase ) == 0: # one of the a or b is a root for the function
return a
elif function(lowerc... | 3 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : Dict = (DDPMScheduler,)
def __UpperCamelCase ( self , **A_ ) -> Dict:
"""simple docstring"""
Up... | 3 | 1 |
class lowercase :
def __init__( self , A_ ) -> List[str]:
"""simple docstring"""
# we need a list not a string, so do something to change the type
UpperCamelCase = arr.split(',' )
def __UpperCamelCase ( self ) -> int:
"""simple docstring"... | 3 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert impor... | 3 | 1 |
def A ( lowercase , lowercase ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCamelCase = str(bin(lowercase ) )[2:] # remove the leading "0b"
UpperCamelCase = str(bin(lowercase ) )[... | 3 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Union[str, Any] = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:... | 3 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_UpperCAmelCase : Any = get_logger(__name__)
_UpperCAmelCase : Tuple = R"\n Args:\n input_ids (`jnp.ndarray` of shape... | 3 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
"facebook... | 3 | 1 |
def A ( lowercase = 50_000_000 ) -> int:
'''simple docstring'''
UpperCamelCase = set()
UpperCamelCase = int((limit - 24) ** (1 / 2) )
UpperCamelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_... | 3 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A ( lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = int(lowercase )
assert noofclusters < len(lowercase )
# Find out the dimensionality
UpperCamelCase ... | 3 | 1 |
def A ( lowercase ) -> str:
'''simple docstring'''
return "".join([hex(lowercase )[2:].zfill(2 ).upper() for byte in list(lowercase )] )
def A ( lowercase ) -> bytes:
'''simple docstring'''
if (len(lowercase ) % 2) != 0:
raise ValueError(
'... | 3 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_UpperCAmelCase : Tuple ... | 3 | 1 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils... | 3 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
... | 3 | 1 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : Optional[int] = True
_UpperCAmelCase : Dict = False
... | 3 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memor... | 3 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Any = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://h... | 3 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 3 | 1 |
from collections import defaultdict
def A ( lowercase , lowercase ) -> bool:
'''simple docstring'''
UpperCamelCase = first_str.lower().strip()
UpperCamelCase = second_str.lower().strip()
# Remove whitespace
UpperCamelCase = first_str.replace(' ' , '' )
U... | 3 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
"vocab_file": ... | 3 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, r... | 3 |
def A ( lowercase ) -> str:
'''simple docstring'''
UpperCamelCase = int(lowercase )
if decimal in (0, 1): # Exit cases for the recursion
return str(lowercase )
UpperCamelCase , UpperCamelCase = divmod(lowercase , 2 )
return binary_recursive(lowercase ... | 3 | 1 |
import requests
_UpperCAmelCase : List[Any] = "" # <-- Put your OpenWeatherMap appid here!
_UpperCAmelCase : str = "https://api.openweathermap.org/data/2.5/"
def A ( lowercase = "Chicago" , lowercase = APPID ) -> dict:
'''simple docstring'''
return req... | 3 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)... | 3 | 1 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impo... | 3 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
_UpperCAmelCase : Any = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and ... | 3 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A ( lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = int(lowercase )
assert noofclusters < len(lowercase )
# Find out the dimensionality
UpperCamelCase ... | 3 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_UpperCAmelCase : str = "scheduler_config.json"
class lowercase ( _SC... | 3 | 1 |
from __future__ import annotations
def A ( lowercase , lowercase ) -> tuple[int, int]:
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCamelCase) , (UpperCamelCase)) = extended_euclid(lowercase , a % b )
UpperCamelCase = a // b
return (y, x - k ... | 3 |
from abc import ABC, abstractmethod
from typing import List, Optional
class lowercase ( _SCREAMING_SNAKE_CASE ):
def __init__( self ) -> Optional[Any]:
"""simple docstring"""
# test for the above condition
self.test()
def __UpperCamelCase ( self ) -> ... | 3 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
"vocab_file": ... | 3 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils im... | 3 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase ( _SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def __UpperCamelCase ( A_ ) -> List[Any]:
"""simple docstring"""
raise NotImplementedError()
@abstractmethod
def __U... | 3 |
from string import ascii_uppercase
_UpperCAmelCase : Dict = {char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase : Tuple = dict(enumerate(ascii_uppercase))
def A ( lowercase , lowercase ) -> str:
'''simple docstring'''
UpperCamelCase ... | 3 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : Optional[Any] = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONFIG_... | 3 |
from collections.abc import Callable
def A ( lowercase , lowercase , lowercase ) -> float:
'''simple docstring'''
UpperCamelCase = a
UpperCamelCase = b
if function(lowercase ) == 0: # one of the a or b is a root for the function
return a
elif function(lowerc... | 3 | 1 |
from collections import defaultdict
from math import ceil, sqrt
def A ( lowercase = 1_000_000 , lowercase = 10 ) -> int:
'''simple docstring'''
UpperCamelCase = defaultdict(lowercase )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width ... | 3 |
import os
_UpperCAmelCase : int = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def A ( lowercase ) -> int:
'''simple docstring'''
UpperCamelCase = 0
UpperCamelCase = 0
while index < len(lowercase ) - 1:
UpperCamelCase = SY... | 3 | 1 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 3 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def A ( lowercase , lowe... | 3 | 1 |
import math
def A ( lowercase ) -> str:
'''simple docstring'''
UpperCamelCase = 0
UpperCamelCase = 0
while num > 0:
UpperCamelCase = num % 8
UpperCamelCase = octal + (remainder * math.floor(math.pow(10 , lowercase ) ))
counter += 1
UpperC... | 3 |
def A ( lowercase , lowercase ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCamelCase = str(bin(lowercase ) )[2:] # remove the leading "0b"
UpperCamelCase = str(bin(lowercase ) )[... | 3 | 1 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import con... | 3 |
import re
def A ( lowercase ) -> str:
'''simple docstring'''
if len(re.findall('[ATCG]' , lowercase ) ) != len(lowercase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doc... | 3 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memor... | 3 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : Dict = (DDPMScheduler,)
def __UpperCamelCase ( self , **A_ ) -> Dict:
"""simple docstring"""
Up... | 3 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTok... | 3 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert impor... | 3 | 1 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, r... | 3 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Union[str, Any] = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:... | 3 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_b... | 3 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
"facebook... | 3 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import Tok... | 3 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A ( lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = int(lowercase )
assert noofclusters < len(lowercase )
# Find out the dimensionality
UpperCamelCase ... | 3 | 1 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ... | 3 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_UpperCAmelCase : Tuple ... | 3 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt m... | 3 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
... | 3 | 1 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
def A ( lowercase , lowercas... | 3 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memor... | 3 | 1 |
import tensorflow as tf
from ...tf_utils import shape_list
class lowercase ( tf.keras.layers.Layer ):
def __init__( self , A_ , A_ , A_ , A_ , A_=1 , A_=False , **A_ ) -> Union[str, Any]:
"""simple docstring"""
super()... | 3 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 3 | 1 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase : List[Any] = get_tests_dir("fixtur... | 3 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
"vocab_file": ... | 3 | 1 |
import operator as op
def A ( lowercase ) -> str:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = lambda lowercase , lowercase : int(x / y ) # noqa: E731 integer division operation
UpperCamelCase = {
'^': op.pow,
'*': op.mul,
'/'... | 3 |
def A ( lowercase ) -> str:
'''simple docstring'''
UpperCamelCase = int(lowercase )
if decimal in (0, 1): # Exit cases for the recursion
return str(lowercase )
UpperCamelCase , UpperCamelCase = divmod(lowercase , 2 )
return binary_recursive(lowercase ... | 3 | 1 |
from __future__ import annotations
from collections import deque
class lowercase :
def __init__( self , A_ ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = []
self.adlist.append(
{'value': '', 'next_states': [], 'fail_state': 0, 'output': []} )
... | 3 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)... | 3 | 1 |
from string import ascii_uppercase
_UpperCAmelCase : Dict = {char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase : Tuple = dict(enumerate(ascii_uppercase))
def A ( lowercase , lowercase ) -> str:
'''simple docstring'''
UpperCamelCase ... | 3 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
_UpperCAmelCase : Any = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and ... | 3 | 1 |
from typing import List
import numpy as np
def A ( lowercase ) -> int:
'''simple docstring'''
UpperCamelCase = {key: len(lowercase ) for key, value in gen_kwargs.items() if isinstance(lowercase , lowercase )}
if len(set(lists_lengths.values() ) ) > 1:
raise Runti... | 3 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_UpperCAmelCase : str = "scheduler_config.json"
class lowercase ( _SC... | 3 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_M... | 3 |
from abc import ABC, abstractmethod
from typing import List, Optional
class lowercase ( _SCREAMING_SNAKE_CASE ):
def __init__( self ) -> Optional[Any]:
"""simple docstring"""
# test for the above condition
self.test()
def __UpperCamelCase ( self ) -> ... | 3 | 1 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_UpperCAmelCase : Dict = logging.get_logger(__name__)
class lowercase ( _SCREAMING_SNAKE_CASE ):
def __init__( self , *A_ , **A_ ) -> None... | 3 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils im... | 3 | 1 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
f... | 3 |
from string import ascii_uppercase
_UpperCAmelCase : Dict = {char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase : Tuple = dict(enumerate(ascii_uppercase))
def A ( lowercase , lowercase ) -> str:
'''simple docstring'''
UpperCamelCase ... | 3 | 1 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
... | 3 |
from collections.abc import Callable
def A ( lowercase , lowercase , lowercase ) -> float:
'''simple docstring'''
UpperCamelCase = a
UpperCamelCase = b
if function(lowercase ) == 0: # one of the a or b is a root for the function
return a
elif function(lowerc... | 3 | 1 |
import math
def A ( lowercase ) -> bool:
'''simple docstring'''
UpperCamelCase = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowercase )
def A ( lowercase = 1 / 12_345 ) -> int:
'''simple docstring'''
UpperC... | 3 |
import os
_UpperCAmelCase : int = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def A ( lowercase ) -> int:
'''simple docstring'''
UpperCamelCase = 0
UpperCamelCase = 0
while index < len(lowercase ) - 1:
UpperCamelCase = SY... | 3 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : Optional[int] = ["image_processor", "tokenizer"]
__lowercase : List[Any] = "ChineseCLIPImageProcessor"
... | 3 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def A ( lowercase , lowe... | 3 | 1 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
log... | 3 |
def A ( lowercase , lowercase ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCamelCase = str(bin(lowercase ) )[2:] # remove the leading "0b"
UpperCamelCase = str(bin(lowercase ) )[... | 3 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, An... | 3 |
import re
def A ( lowercase ) -> str:
'''simple docstring'''
if len(re.findall('[ATCG]' , lowercase ) ) != len(lowercase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doc... | 3 | 1 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_M... | 3 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : Dict = (DDPMScheduler,)
def __UpperCamelCase ( self , **A_ ) -> Dict:
"""simple docstring"""
Up... | 3 | 1 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
_UpperCAmelCase : Optional[Any] = logging.getLogger(__name__)
def A ( ) -> Dict:
'''simple docstring'''
UpperCamelCase = argparse.ArgumentParser(
... | 3 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert impor... | 3 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 3 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Union[str, Any] = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:... | 3 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : List[Any] = {
"configuration_longformer": [
"LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MA... | 3 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
"facebook... | 3 | 1 |
_UpperCAmelCase : Optional[Any] = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
... | 3 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A ( lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = int(lowercase )
assert noofclusters < len(lowercase )
# Find out the dimensionality
UpperCamelCase ... | 3 | 1 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
cla... | 3 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_UpperCAmelCase : Tuple ... | 3 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/conf... | 3 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
... | 3 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Any = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
... | 3 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memor... | 3 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
"facebook... | 3 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 3 | 1 |
import math
import qiskit
def A ( lowercase = 1 , lowercase = 1 , lowercase = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(lowercase , lowercase )
or isinstance(lowercase , lowercase )
or isinstance(lowercase , lowerc... | 3 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
"vocab_file": ... | 3 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQ... | 3 |
def A ( lowercase ) -> str:
'''simple docstring'''
UpperCamelCase = int(lowercase )
if decimal in (0, 1): # Exit cases for the recursion
return str(lowercase )
UpperCamelCase , UpperCamelCase = divmod(lowercase , 2 )
return binary_recursive(lowercase ... | 3 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowercase ( _SCREAMING_SNAKE_CASE ):
def __init__( self , A_ , A_ , A_ ) -> int:
"""simple docstring"""
UpperCamelCase... | 3 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)... | 3 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils ... | 3 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
_UpperCAmelCase : Any = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and ... | 3 | 1 |
from collections.abc import Generator
def A ( ) -> Generator[int, None, None]:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = 0, 1
while True:
UpperCamelCase , UpperCamelCase = b, a + b
yield b
def A ( lowercase = 1_000 ) -> in... | 3 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_UpperCAmelCase : str = "scheduler_config.json"
class lowercase ( _SC... | 3 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
_UpperCAmelCase : List[Any] ... | 3 |
from abc import ABC, abstractmethod
from typing import List, Optional
class lowercase ( _SCREAMING_SNAKE_CASE ):
def __init__( self ) -> Optional[Any]:
"""simple docstring"""
# test for the above condition
self.test()
def __UpperCamelCase ( self ) -> ... | 3 | 1 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassi... | 3 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils im... | 3 | 1 |
from math import isqrt
def A ( lowercase ) -> list[int]:
'''simple docstring'''
UpperCamelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowercase , lowercase ):
UpperCamelCase ... | 3 |
from string import ascii_uppercase
_UpperCAmelCase : Dict = {char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase : Tuple = dict(enumerate(ascii_uppercase))
def A ( lowercase , lowercase ) -> str:
'''simple docstring'''
UpperCamelCase ... | 3 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-w... | 3 |
from collections.abc import Callable
def A ( lowercase , lowercase , lowercase ) -> float:
'''simple docstring'''
UpperCamelCase = a
UpperCamelCase = b
if function(lowercase ) == 0: # one of the a or b is a root for the function
return a
elif function(lowerc... | 3 | 1 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configurati... | 3 |
import os
_UpperCAmelCase : int = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def A ( lowercase ) -> int:
'''simple docstring'''
UpperCamelCase = 0
UpperCamelCase = 0
while index < len(lowercase ) - 1:
UpperCamelCase = SY... | 3 | 1 |
import numpy as np
def A ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
UpperCamelCase = int(np.ceil((x_end - xa) / h ) )
UpperCamelCase = np.zeros((n + 1,) )
UpperCamelCase = ya
UpperCamelCase ... | 3 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def A ( lowercase , lowe... | 3 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 3 |
def A ( lowercase , lowercase ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCamelCase = str(bin(lowercase ) )[2:] # remove the leading "0b"
UpperCamelCase = str(bin(lowercase ) )[... | 3 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def A ( lowercase , lowercase , lowercase ) -> Any:
'''simple docstring'''
UpperCamelCase = AutoConfig.from_pretrained(lowercase )
UpperCamelCase = FlaxAutoMo... | 3 |
import re
def A ( lowercase ) -> str:
'''simple docstring'''
if len(re.findall('[ATCG]' , lowercase ) ) != len(lowercase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doc... | 3 | 1 |
from numpy import exp, pi, sqrt
def A ( lowercase , lowercase = 0.0 , lowercase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 3 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : Dict = (DDPMScheduler,)
def __UpperCamelCase ( self , **A_ ) -> Dict:
"""simple docstring"""
Up... | 3 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp... | 3 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert impor... | 3 | 1 |
def A ( lowercase ) -> list[list[float]]:
'''simple docstring'''
UpperCamelCase = []
for data in source_data:
for i, el in enumerate(lowercase ):
if len(lowercase ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(lowercase ) )
return ... | 3 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Union[str, Any] = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:... | 3 | 1 |
def A ( lowercase ) -> list:
'''simple docstring'''
UpperCamelCase = [0] * len(lowercase )
for i in range(1 , len(lowercase ) ):
# use last results for better performance - dynamic programming
UpperCamelCase = prefix_result[i - 1]
while j > 0 and input_s... | 3 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
"facebook... | 3 | 1 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_UpperCAmelCase : Tuple = "sshleifer/bart-tiny-random"
... | 3 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A ( lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = int(lowercase )
assert noofclusters < len(lowercase )
# Find out the dimensionality
UpperCamelCase ... | 3 | 1 |
from __future__ import annotations
from typing import TypedDict
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : str
__lowercase : int
def A ( lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
raise TypeE... | 3 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_UpperCAmelCase : Tuple ... | 3 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_SCREAMING_SNAKE_CASE )
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : str = field(default="language-modeling" ... | 3 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
... | 3 | 1 |
_UpperCAmelCase : List[Any] = range(2, 20 + 1)
_UpperCAmelCase : List[str] = [10**k for k in range(ks[-1] + 1)]
_UpperCAmelCase : dict[int, dict[int, list[list[int]]]] = {}
def A ( lowercase , lowercase , lowercase , lowercase ) -> Op... | 3 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memor... | 3 | 1 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowercase ( _SCREAMING_SNAKE_CASE ):
def __lt__( self , A_ ) -> Dict:
"""simple docstring"""
return self[-1] < other[... | 3 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 3 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import ... | 3 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
"vocab_file": ... | 3 | 1 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logg... | 3 |
def A ( lowercase ) -> str:
'''simple docstring'''
UpperCamelCase = int(lowercase )
if decimal in (0, 1): # Exit cases for the recursion
return str(lowercase )
UpperCamelCase , UpperCamelCase = divmod(lowercase , 2 )
return binary_recursive(lowercase ... | 3 | 1 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
... | 3 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)... | 3 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.