code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowerCAmelCase ( __A ):
def __init__( self :Tuple , *__magic_name__ :List[str] ... | 359 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Optional[Any] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config"... | 347 | 0 |
"""simple docstring"""
def __A ( __lowerCamelCase ) -> Dict:
if len(__lowerCamelCase ) <= 1:
return [tuple(__lowerCamelCase )]
a = []
def generate(__lowerCamelCase , __lowerCamelCase ):
if k == 1:
res.append(tuple(arr[:] ) )
re... | 360 |
def __A ( __lowerCamelCase ) -> bool:
if num < 0:
return False
a = num
a = 0
while num > 0:
a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod... | 347 | 0 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversat... | 361 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
... | 347 | 0 |
def __A ( __lowerCamelCase = 10 ) -> str:
if not isinstance(lowercase__ , lowercase__ ) or n < 0:
raise ValueError("""Invalid input""" )
a = 10**n
a = 2_8433 * (pow(2 , 783_0457 , lowercase__ )) + 1
return str(number %... | 362 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__UpperCamelCase : str = TypeVar("T")
def __A ( __lowerCamelCase ) -> int:
return (position - 1) // 2
def __A ( __lowerCamelCase ) -> int:
return (2 * position) + 1
def ... | 363 |
def __A ( __lowerCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of ... | 347 | 0 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __lowerCAmelCase ( a_ ):
def __init__(... | 364 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP... | 347 | 0 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available(... | 365 |
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... | 347 | 0 |
def __A ( __lowerCamelCase ) -> int:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise TypeError("""Input value must be an 'int' type""" )
a = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "_... | 366 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( __lowerCamelCase ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def __A ( __lowerCamelCase , __lowerCamelCase , ... | 347 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowerCAmelCase :
def __init__( self :Optional[int] , __magic_name__ :Any ):
'''simple docstring'''
a = ... | 367 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as... | 347 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __A ( __lowerCamelCase ) -> Tuple:
return 1 / (1 + np.exp(-z ))
def __A ( __lowerCamelCase , __lowerCamelCase ) -> Optional[int]:
return (-y * np.log(_Upp... | 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_blenderbot": [
... | 347 | 0 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
... | 369 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : int = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerConfig"... | 370 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 0 |
import os
import string
import sys
__UpperCamelCase : Any = 1 << 8
__UpperCamelCase : Optional[Any] = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW... | 371 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from ... | 347 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_... | 350 |
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
__UpperCamelCase : Any = datasets.ut... | 347 | 0 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Confi... | 351 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 | 0 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassificatio... | 352 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 347 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Union[str, Any] = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecode... | 353 |
from copy import deepcopy
class __lowerCAmelCase :
def __init__( self :Union[str, Any] , __magic_name__ :list[int] | None = None , __magic_name__ :int | None = None ):
'''simple docstring'''
if arr is None and size i... | 347 | 0 |
__UpperCamelCase : List[str] = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
__UpperCamelCase... | 354 |
from __future__ import annotations
from typing import Generic, TypeVar
__UpperCamelCase : Union[str, Any] = TypeVar("T")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self :Tuple , __magic_name__ :T ):
'''simple docstring''... | 347 | 0 |
import requests
from bsa import BeautifulSoup
def lowercase__ ( __lowerCamelCase = "https://www.worldometers.info/coronavirus" ) -> int:
a = BeautifulSoup(requests.get(snake_case_ ).text , """html.parser""" )
a = soup.findAll("""h1""" ... | 355 |
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, BertTokenizer, ... | 347 | 0 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids... | 356 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"shi-labs/n... | 347 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
f... | 357 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transforme... | 347 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determini... | 358 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCamelCase : Union[str, Any] = (720, 1_280) # Height, Width
__UpperCamelCase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__Upp... | 347 | 0 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiec... | 359 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Optional[Any] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config"... | 347 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/marku... | 360 |
def __A ( __lowerCamelCase ) -> bool:
if num < 0:
return False
a = num
a = 0
while num > 0:
a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod... | 347 | 0 |
import torch
from torch import nn
class __lowerCAmelCase ( nn.Module ):
def __init__( self :Any , __magic_name__ :Optional[int] , __magic_name__ :Dict , __magic_name__ :Optional[Any] , __magic_name__ :int , __magic_name__ :List[Any... | 361 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
... | 347 | 0 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
... | 362 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 0 |
def __A ( __lowerCamelCase ) -> str:
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise TypeError("""\'float\' object cannot be interpreted as an integer""" )
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise TypeError("""\'str\' object cannot b... | 363 |
def __A ( __lowerCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of ... | 347 | 0 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelFo... | 364 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP... | 347 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __A ( __lowerCamelCase ) -> List[str]:
return "".join(sorted(_UpperCAmelCase ) )
def __A ( __lowerCamelCase ) -> List[Any]:
return word_by_signature[signa... | 365 |
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... | 347 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __A ( __lowerCamel... | 366 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( __lowerCamelCase ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def __A ( __lowerCamelCase , __lowerCamelCase , ... | 347 | 0 |
from functools import lru_cache
@lru_cache
def __A ( __lowerCamelCase ) -> 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__ == "__main__":
import doctest
doctest.testm... | 367 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as... | 347 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)... | 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_blenderbot": [
... | 347 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .to... | 369 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 0 |
def __A ( __lowerCamelCase = 100_0000 ) -> Dict:
a = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , __lowerCamelCase ):
phi[j] -= phi[j] // i
retur... | 370 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 0 |
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 (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
t... | 371 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from ... | 347 | 0 |
from __future__ import annotations
class __lowerCAmelCase :
def __init__( self :Optional[int] , __magic_name__ :int ):
'''simple docstring'''
a = data
a = None
a = None
def __A (... | 350 |
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
__UpperCamelCase : Any = datasets.ut... | 347 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCAmelCase ( __UpperCamelCase ):
UpperCamelCase__ = ['''image_processor''', '''tokenizer''']
UpperCamelCase__ = '''ViTImageProcessor'''... | 351 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 | 0 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __A ( __lowerCamelCase , __lowerCamelCase ) ->... | 352 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 347 | 0 |
from statistics import mean, stdev
def __A ( __lowerCamelCase , __lowerCamelCase = 3 ) -> str:
a = min(lowerCamelCase__ )
a = max(lowerCamelCase__ )
# normalize data
return [round((x - x_min) / (x_max - x_min) , lowerCamelC... | 353 |
from copy import deepcopy
class __lowerCAmelCase :
def __init__( self :Union[str, Any] , __magic_name__ :list[int] | None = None , __magic_name__ :int | None = None ):
'''simple docstring'''
if arr is None and size i... | 347 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCame... | 354 |
from __future__ import annotations
from typing import Generic, TypeVar
__UpperCamelCase : Union[str, Any] = TypeVar("T")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self :Tuple , __magic_name__ :T ):
'''simple docstring''... | 347 | 0 |
__UpperCamelCase : List[Any] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
_... | 355 |
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, BertTokenizer, ... | 347 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( sna... | 356 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"shi-labs/n... | 347 | 0 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__UpperCamelCase : List[str] = datasets.load_iris()
__UpperCamelCase : Optional[Any] = np.array(data["data"])
__UpperCamelCase : str = np.array(data["t... | 357 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transforme... | 347 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
UpperCamelCase__ = 42
UpperCamelCase__ = None
UpperCamelCase__ = None
def __A ( __lowerCamelCase ) -> str:
def is_v... | 358 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCamelCase : Union[str, Any] = (720, 1_280) # Height, Width
__UpperCamelCase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__Upp... | 347 | 0 |
import uuid
from typing import Any, Dict, List, Optional, Union
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
if is_torch_available():
import torch
__Uppe... | 359 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Optional[Any] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config"... | 347 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch... | 360 |
def __A ( __lowerCamelCase ) -> bool:
if num < 0:
return False
a = num
a = 0
while num > 0:
a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod... | 347 | 0 |
from __future__ import annotations
import unittest
from transformers import 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, random_attention_mask
fro... | 361 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
... | 347 | 0 |
import math
import os
import sys
def __A ( __lowerCamelCase ) -> str:
a = """"""
try:
with open(__lowerCAmelCase , """rb""" ) as binary_file:
a = binary_file.read()
for dat in data:
a = f'{dat:08b}'
result... | 362 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 0 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__UpperCamelCase : Dict = logging.getLogger(__name__)
_... | 363 |
def __A ( __lowerCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of ... | 347 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import f... | 364 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP... | 347 | 0 |
from collections.abc import Callable
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
a = a
a = b
if function(_a ) == 0: # one of the a or b is a root for the function
return a
elif functio... | 365 |
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... | 347 | 0 |
def __A ( __lowerCamelCase ) -> int:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" )
return sum(
divisor for diviso... | 366 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( __lowerCamelCase ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def __A ( __lowerCamelCase , __lowerCamelCase , ... | 347 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Optional[Any] = False
class __lowerCAmelCase ... | 367 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as... | 347 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __A ( __lowerC... | 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_blenderbot": [
... | 347 | 0 |
"""simple docstring"""
def __A ( __lowerCamelCase ) -> str:
if number > 0:
raise ValueError("""input must be a negative integer""" )
a = len(bin(a__ )[3:] )
a = bin(abs(a__ ) - (1 << binary_number_length) )[3:]
a = (
... | 369 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def __A ( ) -> Union[str, Any]:
a = 9
a = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
... | 370 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 0 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=1024 , __lowerCamelCase=1024 ... | 371 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from ... | 347 | 0 |
from __future__ import annotations
def __A ( __lowerCamelCase ) -> str:
if len(lowerCamelCase__ ) == 0:
return []
a , a = min(lowerCamelCase__ ), max(lowerCamelCase__ )
a = int(max_value - min_value ) + 1
a = [[] for _ in range(lo... | 350 |
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
__UpperCamelCase : Any = datasets.ut... | 347 | 0 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __A ( __lowerCamelCase ) -> list[list[float]]:
a = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matri... | 351 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class __lowerCA... | 352 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 347 | 0 |
from __future__ import annotations
from math import ceil, floor, sqrt
def __A ( __lowerCamelCase = 200_0000 ) -> str:
a = [0]
a = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_numbe... | 353 |
from copy import deepcopy
class __lowerCAmelCase :
def __init__( self :Union[str, Any] , __magic_name__ :list[int] | None = None , __magic_name__ :int | None = None ):
'''simple docstring'''
if arr is None and size i... | 347 | 0 |
def __A ( __lowerCamelCase ) -> None:
a = generate_pascal_triangle(_lowerCAmelCase )
for row_idx in range(_lowerCAmelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=""" """ )
# Print row values
for col_idx in range(row_idx +... | 354 |
from __future__ import annotations
from typing import Generic, TypeVar
__UpperCamelCase : Union[str, Any] = TypeVar("T")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self :Tuple , __magic_name__ :T ):
'''simple docstring''... | 347 | 0 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,... | 355 |
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, BertTokenizer, ... | 347 | 0 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_commo... | 356 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"shi-labs/n... | 347 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamember... | 357 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transforme... | 347 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Dict = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MA... | 358 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCamelCase : Union[str, Any] = (720, 1_280) # Height, Width
__UpperCamelCase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__Upp... | 347 | 0 |
import os
def __A ( ) -> Union[str, Any]:
a : Optional[Any] = os.path.join(os.path.dirname(_lowercase ) , """num.txt""" )
with open(_lowercase ) as file_hand:
return str(sum(int(_lowercase ) for line in file_hand ) )[:10]
if __name__ ==... | 359 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Optional[Any] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config"... | 347 | 0 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
Di... | 360 |
def __A ( __lowerCamelCase ) -> bool:
if num < 0:
return False
a = num
a = 0
while num > 0:
a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod... | 347 | 0 |
__UpperCamelCase : Dict = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ... | 361 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
... | 347 | 0 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
B... | 362 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 0 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
__UpperCamelCase : Optional[Any] = '''
import os
'''
__UpperCamelCase : List[str] = '''
def foo():
import os
return False
'''
__UpperCamelCase : List[Any] = '''
def foo():
def bar():
if Tru... | 363 |
def __A ( __lowerCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of ... | 347 | 0 |
from collections import defaultdict
from math import gcd
def __A ( __lowerCamelCase = 150_0000 ) -> int:
a = defaultdict(__lowerCamelCase )
a = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , ... | 364 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP... | 347 | 0 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from ... | 365 |
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... | 347 | 0 |
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
return round(float(moles / volume ) * nfactor )
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Tuple:
return round(... | 366 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( __lowerCamelCase ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def __A ( __lowerCamelCase , __lowerCamelCase , ... | 347 | 0 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets i... | 367 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as... | 347 | 0 |
__UpperCamelCase : int = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCamelCase : str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> list[int]:
a = ... | 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_blenderbot": [
... | 347 | 0 |
"""simple docstring"""
import os
__UpperCamelCase : Any = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def __A ( __lowerCamelCase ) -> Dict:
a = 0
a = 0
while index < len(__UpperCamelCase ) - 1:
a ... | 369 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__UpperCamelCase : Dict = logging.get_logger(__name__)
__UpperCamelCase : Any = Or... | 370 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 0 |
import os
from datetime import datetime as dt
from github import Github
__UpperCamelCase : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def __A ( ) -> Di... | 371 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from ... | 347 | 0 |
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_barthez import... | 350 |
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
__UpperCamelCase : Any = datasets.ut... | 347 | 0 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
__UpperCamelCase : int = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-research... | 351 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 | 0 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __lowerCAmelCase ( __lowercase ):
UpperCamelCase__ = "MCTCTFeatureExtractor"
UpperCamelCase__ = "AutoTokenizer"
def __init__( self :Any ... | 352 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 347 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoder... | 353 |
from copy import deepcopy
class __lowerCAmelCase :
def __init__( self :Union[str, Any] , __magic_name__ :list[int] | None = None , __magic_name__ :int | None = None ):
'''simple docstring'''
if arr is None and size i... | 347 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__UpperCamelCase : str = logging.get_log... | 354 |
from __future__ import annotations
from typing import Generic, TypeVar
__UpperCamelCase : Union[str, Any] = TypeVar("T")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self :Tuple , __magic_name__ :T ):
'''simple docstring''... | 347 | 0 |
def lowercase__ ( __lowerCamelCase ) -> Union[str, Any]:
a = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
a = set()
return any(
node not in visited and depth_first_search(__lowerCamelCase , __lowerCame... | 355 |
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, BertTokenizer, ... | 347 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : Optional[Any] = {
'Salesforce/blip-vqa-base': ... | 356 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"shi-labs/n... | 347 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureEx... | 357 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transforme... | 347 | 0 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__UpperCamelCase : Optional[int] = datasets.load_iris()
__UpperCamelCase : List[Any] = np.array(data["data"])
__UpperCamelCase : Union[str, Any] = ... | 358 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCamelCase : Union[str, Any] = (720, 1_280) # Height, Width
__UpperCamelCase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__Upp... | 347 | 0 |
import unittest
from knapsack import greedy_knapsack as kp
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
a : Dict = [10, 20, 30, 40, 50, 6... | 359 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Optional[Any] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config"... | 347 | 0 |
"""simple docstring"""
import math
import sys
def __A ( __lowerCamelCase ) -> int:
if number != int(__lowerCamelCase ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of input must not be a negati... | 360 |
def __A ( __lowerCamelCase ) -> bool:
if num < 0:
return False
a = num
a = 0
while num > 0:
a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod... | 347 | 0 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .lo... | 361 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
... | 347 | 0 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __lowerCAmelCase ( __magic_name__ ):
def __init__( self :Any , __magic_name__ :Dict="" , __magic_name__ :Dict="train" ):
'''simple d... | 362 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __lowerCAmelCase ( _snake_case ):
UpperCamelCase__ = '''Speech2TextFeatureExtractor'''
UpperCamelCase__ = '''Speech2TextTokenizer'''
def __init__( self ... | 363 |
def __A ( __lowerCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of ... | 347 | 0 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 364 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP... | 347 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : int = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-lar... | 365 |
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... | 347 | 0 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_a... | 366 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( __lowerCamelCase ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def __A ( __lowerCamelCase , __lowerCamelCase , ... | 347 | 0 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGener... | 367 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as... | 347 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerat... | 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_blenderbot": [
... | 347 | 0 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count... | 369 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 0 |
import sys
import turtle
def __A ( __lowerCamelCase , __lowerCamelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCam... | 370 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 0 |
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