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"""
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_SCREAMING_SNAKE_CASE ) )
def _a ... | 2 |
"""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 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SN... | 2 |
"""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 | 1 |
"""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': ['... | 2 |
"""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 | 1 |
"""simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokenize... | 2 |
"""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 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Dict = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/... | 2 |
"""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 | 1 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__SCREAMING_SNAKE_CASE : int = mode... | 2 |
"""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 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_... | 2 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
__SCREAMING_SNAKE_CASE : List[Any] = 100
__SCREAMING_SNAKE_CASE : Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__SCREAMING_SNAKE_CASE : int
for prime in range(3, ... | 2 |
"""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 | 1 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> list:
if any(not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len... | 2 |
"""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 | 1 |
"""simple docstring"""
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosit... | 2 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
class __A (snake_case__):
'''simple docstring'''
def __init__( self ... | 2 |
"""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 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 2 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__)
class __A (snake_case__):
'''simple docstring'''
__lowercase: ... | 2 | 1 |
"""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_ ... | 2 |
"""simple docstring"""
from functools import reduce
__SCREAMING_SNAKE_CASE : Tuple = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'1254069874715852386305071569329096329522... | 2 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> str:
return "".join([hex(_SCREAMING_SNAKE_CASE )[2:].zfill(2 ).upper() for byte in list(_SCREAMING_SNAKE_CASE )] )
def _a ( _SCREAMING_SNAKE_CASE ) -> bytes:
# Check data validity... | 2 |
"""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 | 1 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = 256
# Modulus to hash a string
__SCREAMING_SNAKE_CASE : int = 1_000_003
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
snake_case_ = len(_SCREAMING_SNAKE_CASE )
... | 2 |
"""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 | 1 |
"""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__) ... | 2 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils impo... | 2 |
"""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 | 1 |
"""simple docstring"""
import numpy as np
def _a ( _SCREAMING_SNAKE_CASE ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 |
"""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 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class __A (snake_case__):
'''simple docstring'''
def __init__( sel... | 2 |
"""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 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[int] = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
... | 2 |
"""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 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__SCREAMING_SNAKE_CASE : Any = logging.get_l... | 2 |
"""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 | 1 |
"""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... | 2 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
if len(_SCREAMING_SNAKE_CASE ) == 0:
return False
snake_case_ = len(_SCREAMING_SNAKE_CASE ) // 2
if a_list[m... | 2 |
"""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 | 1 |
"""simple docstring"""
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("""repo_id""" , ["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path""" , ["""filename.csv""", """filename with blanks.csv... | 2 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
def _a ( _SCREAMING_SNAKE_CASE = 4 ) -> list[list[int]]:
snake_case_ = abs(_SCREAMING_SNAKE_CASE ) or 4
return [[1 + x + y * row_size for x in range(_SCREAMING_SNAKE_CASE )] for y in range(_SCREAMIN... | 2 |
"""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 | 1 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from ... | 2 |
"""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 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data im... | 2 |
"""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 | 1 |
"""simple docstring"""
import math
__SCREAMING_SNAKE_CASE : Optional[int] = 10
__SCREAMING_SNAKE_CASE : Optional[Any] = 7
__SCREAMING_SNAKE_CASE : Dict = BALLS_PER_COLOUR * NUM_COLOURS
def _a ( _SCREAMING_SNAKE_CASE = 20 ) -> str:
snake_cas... | 2 |
"""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 | 1 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__SCREAMING_SNAKE_CASE : Optional[Any] = 'examples/'
__SCREAMING_SNAKE_CASE : int = {
'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
... | 2 |
"""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 | 1 |
"""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... | 2 |
"""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 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging... | 2 |
"""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 | 1 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
... | 2 |
"""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 | 1 |
"""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',
... | 2 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__)
class __A (snake_case__):
'''simple docstring'''
__lowercase: ... | 2 | 1 |
"""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 |
"""simple docstring"""
from functools import reduce
__SCREAMING_SNAKE_CASE : Tuple = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'1254069874715852386305071569329096329522... | 2 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Any = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
... | 2 |
"""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 | 1 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _a ( _SCREAMING_SNAKE_CASE ) -> int:
# A local function to see if a dot lands in the circle.
def is_in_circle(_SCREAMING_SNAKE_... | 2 |
"""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 | 1 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@m... | 2 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def _a ( _SCREAMING_SNAKE_CASE ) -> Any:
snake_case_ = test_file.split(... | 2 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _a ( _SCREAMING_SNAKE_CASE = "AAPL" ) -> str:
snake_case_ = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
snake_case_ = BeautifulSoup(requests.get(_SCREAMING_SNAK... | 2 |
"""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 | 1 |
"""simple docstring"""
import itertools
import math
def _a ( _SCREAMING_SNAKE_CASE ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even n... | 2 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import unittest
import numpy as np
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, prepare_image_inputs
if is_torch_avail... | 2 |
"""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 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertToken... | 2 |
"""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 | 1 |
"""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... | 2 |
"""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 | 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... | 2 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import datasets
__SCREAMING_SNAKE_CASE : Dict = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n ... | 2 |
"""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 | 1 |
"""simple docstring"""
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
__SCREAMING_SNAKE_CASE : Tuple = yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_empty_text: tru... | 2 |
"""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 | 1 |
"""simple docstring"""
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__SCREAMING_SNAKE_CASE : Union[str, Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__SCREAMING... | 2 |
"""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 | 1 |
"""simple docstring"""
import unittest
import numpy as np
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, prepare_image_inputs
if is_torch_avail... | 2 |
"""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 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[str] = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class... | 2 |
"""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 | 1 |
"""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... | 2 |
"""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 | 1 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accelerato... | 2 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__)
class __A (snake_case__):
'''simple docstring'''
__lowercase: ... | 2 | 1 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_g... | 2 |
"""simple docstring"""
from functools import reduce
__SCREAMING_SNAKE_CASE : Tuple = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'1254069874715852386305071569329096329522... | 2 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constan... | 2 |
"""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 | 1 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _a ( ) -> None:
print("""Making key files...""" )
make_key_files("""rsa""" , 1_024 )
... | 2 |
"""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 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_a... | 2 |
"""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 | 1 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _a ( _SCREAMING_SNAKE_CASE ) -> List[str]:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.... | 2 |
"""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 | 1 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 2 |
"""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 | 1 |
"""simple docstring"""
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
__SCREAMING_SNAKE_CASE : Optional[... | 2 |
"""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 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
im... | 2 |
"""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 | 1 |
"""simple docstring"""
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class __A :
... | 2 |
"""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 | 1 |
"""simple docstring"""
import os
def _a ( _SCREAMING_SNAKE_CASE ) -> List[str]:
snake_case_ = len(grid[0] )
snake_case_ = len(_SCREAMING_SNAKE_CASE )
snake_case_ = 0
snake_case_ = 0
snake_case_ ... | 2 |
"""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 | 1 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = range(2, 20 + 1)
__SCREAMING_SNAKE_CASE : Any = [10**k for k in range(ks[-1] + 1)]
__SCREAMING_SNAKE_CASE : dict[int, dict[int, list[list[int]]]] = {}
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE... | 2 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER... | 2 |
"""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 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _a ( _SCREAMING_SNAKE_CASE ) -> Tuple:
snake_case_ = [
"""encoder.version""",
"""decoder... | 2 |
"""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 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,... | 2 |
"""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 | 1 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _a ( *_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=2 ) -> List[str]:
from .... | 2 |
"""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 | 1 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
... | 2 |
"""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 | 1 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table impor... | 2 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked b... | 2 |
"""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 | 1 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__SCREAMING_SNAKE_CASE : str =... | 2 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: str = ["""torch""", """transformers""", """onnx"""]
def __init__( self : Union[str, Any] , ... | 2 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__)
class __A (snake_case__):
'''simple docstring'''
__lowercase: ... | 2 | 1 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __A (snake... | 2 |
"""simple docstring"""
from functools import reduce
__SCREAMING_SNAKE_CASE : Tuple = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'1254069874715852386305071569329096329522... | 2 | 1 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __A (unittest.TestCase):
'''simple docstring'''
__lowercase: Any = JukeboxTokenizer
__lowercase: Union[str, Any] ... | 2 |
"""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 | 1 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormat... | 2 |
"""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 | 1 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A (unittest.TestCase):
'''simple docstring'''
def lowerCAmelCase ( self : List[Any] ) ... | 2 |
"""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 | 1 |
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _a ( _SCREAMING_SNAKE_CASE ) -> t... | 2 |
"""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 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__SCREAMING_SNAKE_CA... | 2 |
"""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 | 1 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def _a ( _SCREAMING_SNAKE_CASE ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__... | 2 |
"""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 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_proces... | 2 |
"""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 | 1 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __A (snake_case__):
'''simple docstr... | 2 |
"""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 | 1 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__SCREAMING_SNAKE_CASE : Dict = pytest.mark.integration
@pytest.mark.... | 2 |
"""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 | 1 |
"""simple docstring"""
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
__SCREAMING_SNAKE_CASE : Optional[int] = get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = R'\n ... | 2 |
"""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 | 1 |
"""simple docstring"""
import os
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 : Optional[int] = logging.get_logger(__na... | 2 |
"""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 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
snake_case_ = Path(_SCREAMING_SNAKE_CASE )
snake_case_ = Path(_SCREAMING_SNAKE_CASE ... | 2 |
"""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 | 1 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __A (snake_case__):
... | 2 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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, ... | 2 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> set[str]:
snake_case_ , snake_case_ = set(_SCREAMING_SNAKE_CASE ), [start]
while stack:
snake_case_ = ... | 2 |
"""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 | 1 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 2 |
"""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 | 1 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_m... | 2 |
"""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 | 1 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
... | 2 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_a... | 2 |
"""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 | 1 |
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