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'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Any = {
"t5-small": "https://huggingface.... | 715 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ... | 5 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Tuple = {
"configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_... | 716 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : Optional[int] = {
"facebook/xmod-base": "https://huggin... | 5 | 0 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common ... | 717 |
import unittest
from transformers import 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 import ModelTesterMixin, ids_t... | 5 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_u... | 718 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Dict = {
"kssteven/ibert-roberta-base": "ht... | 5 | 0 |
'''simple docstring'''
def lowercase_ ( _A : str ):
"""simple docstring"""
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def lowercase_ ( _A : str ):
"""simple docstring"""
lowerCamelCase__ : str ... | 719 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Union[str, Any] = {
"roberta-base": "https://huggingfa... | 5 | 0 |
import torch
def lowercase_ ( ):
"""simple docstring"""
if torch.cuda.is_available():
lowerCamelCase__ : int = torch.cuda.device_count()
else:
lowerCamelCase__ : int = 0
print(F"Successfully ran on {num_gpus} GPUs" )
if __name_... | 720 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tok... | 5 | 0 |
import random
from typing import Any
def lowercase_ ( _A : list ):
"""simple docstring"""
for _ in range(len(_A ) ):
lowerCamelCase__ : str = random.randint(0 , len(_A ) - 1 )
lowerCamelCase__ : Optional[int] = ... | 721 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, loggi... | 5 | 0 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
A : Union[str, Any] = {
"vocab_file": "vocab.txt",
"merg... | 700 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, ... | 5 | 0 |
from __future__ import annotations
def lowercase_ ( _A : list[int] , _A : list[int] , _A : list[int] , _A : list[list[str]] , _A : int , ):
"""simple docstring"""
lowerCamelCase__ : str = l... | 701 |
def lowercase_ ( _A : int , _A : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowerCamelCase__ : List[str] = str(bin(_A ) )[2:] # remove the leading "0b"
... | 5 | 0 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
A : List[Any] = {
... | 702 |
import os
from pathlib import Path
def lowercase_ ( ):
"""simple docstring"""
from torch.utils.cpp_extension import load
lowerCamelCase__ : Any = Path(_A ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
lowerCamelCase__ : Optiona... | 5 | 0 |
def lowercase_ ( _A : Optional[int] ):
"""simple docstring"""
return "".join(chr(ord(__SCREAMING_SNAKE_CASE ) - 32 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 703 |
import os
from datetime import datetime as dt
from github import Github
A : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def lowercase_ ( ):
... | 5 | 0 |
import sys
A : Any = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"6689664895044524452316173185640... | 704 |
from __future__ import annotations
def lowercase_ ( _A : str , _A : list[str] | None = None , _A : dict[str, float] | None = None , _A : bool = False , ):
"""simple docstring"""
lowerCamelCase__ : Tuple = ... | 5 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
A : Any = ''
A : List[Any] = ''
A : Dict = ''
A : Tuple = 1 # (0 is vertical, 1 is horizontal)
def lowercase_ ( ):
"""simple docstring"""
lowerCamelCase__ : ... | 705 |
def lowercase_ ( _A : int ):
"""simple docstring"""
if not isinstance(_A , _A ):
lowerCamelCase__ : List[str] = F"Input value of [number={number}] must be an integer"
raise TypeError(_A )
if number < 0:
retu... | 5 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class _lowercase ( lowercase__):
... | 706 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
A : Optional[int] = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT... | 5 | 0 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import ... | 707 |
from __future__ import annotations
import time
import numpy as np
A : Dict = [8, 5, 9, 7]
A : Optional[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A : Any = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1... | 5 | 0 |
from __future__ import annotations
from math import gcd
def lowercase_ ( _A : int , _A : int = 2 , _A : int = 1 , _A : int = 3 , ):
"""simple docstring"""
if num < 2:
raise ValueError("The input value cannot be less tha... | 708 |
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_sent... | 5 | 0 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_s... | 709 |
import cva
import numpy as np
class _lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCamelCase : float , __lowerCamelCase : int ):
'''simple docstring'''
... | 5 | 0 |
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import loggi... | 710 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 5 | 0 |
def lowercase_ ( _A : Optional[Any] ):
"""simple docstring"""
if num <= 0:
raise ValueError("Input must be a positive integer" )
lowerCamelCase__ : str = [True] * (num + 1)
lowerCamelCase__ : Any = 2
while p * ... | 711 |
import os
def lowercase_ ( _A : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_A ) , _A ) ) as input_file:
lowerCamelCase__ : List[Any] = [
[int(_A ) for element in line.split("," ... | 5 | 0 |
import numpy as np
def lowercase_ ( _A : np.ndarray , _A : float ):
return np.where(vector > 0 , a_ , (alpha * (np.exp(a_ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 712 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
A : Tuple = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Autom... | 5 | 0 |
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_constant_schedule,
get... | 713 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
A : str = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
A : Optional[int] = requests.get(url... | 5 | 0 |
A : Tuple = [
"DownloadConfig",
"DownloadManager",
"DownloadMode",
"StreamingDownloadManager",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 714 |
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 TFCa... | 5 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_... | 715 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ... | 5 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers... | 716 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : Optional[int] = {
"facebook/xmod-base": "https://huggin... | 5 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A : Optional[Any] = {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_available():
raise OptionalDepende... | 717 |
import unittest
from transformers import 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 import ModelTesterMixin, ids_t... | 5 | 0 |
import argparse
import datetime
def UpperCamelCase__ ( _A : str ):
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wedne... | 718 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Dict = {
"kssteven/ibert-roberta-base": "ht... | 5 | 0 |
'''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_input... | 719 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Union[str, Any] = {
"roberta-base": "https://huggingfa... | 5 | 0 |
from math import pi
def lowercase_ ( _A : Optional[Any] , _A : Tuple ):
"""simple docstring"""
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 720 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tok... | 5 | 0 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowercase_ ( _A : Union[str, Any] , _A : Any=None ):
"""simple docstring"""
lowerCamelCase__ : Opt... | 721 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, loggi... | 5 | 0 |
def lowercase_ ( _A : str ):
lowerCamelCase__ : int = len(_A )
while cur > 1:
# Find the maximum number in arr
lowerCamelCase__ : Any = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
lowerCamelCase__... | 700 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, ... | 5 | 0 |
import cva
import numpy as np
class _lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCamelCase : float , __lowerCamelCase : int ):
'''simple docstring'''
... | 701 |
def lowercase_ ( _A : int , _A : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowerCamelCase__ : List[str] = str(bin(_A ) )[2:] # remove the leading "0b"
... | 5 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A : List[str] = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "Res... | 702 |
import os
from pathlib import Path
def lowercase_ ( ):
"""simple docstring"""
from torch.utils.cpp_extension import load
lowerCamelCase__ : Any = Path(_A ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
lowerCamelCase__ : Optiona... | 5 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
... | 703 |
import os
from datetime import datetime as dt
from github import Github
A : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def lowercase_ ( ):
... | 5 | 0 |
def lowercase_ ( _A : int = 2000000 ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] = [0 for i in range(n + 1 )]
lowerCamelCase__ : Union[str, Any] = 1
lowerCamelCase__ : Dict = 1
for i in ra... | 704 |
from __future__ import annotations
def lowercase_ ( _A : str , _A : list[str] | None = None , _A : dict[str, float] | None = None , _A : bool = False , ):
"""simple docstring"""
lowerCamelCase__ : Tuple = ... | 5 | 0 |
from manim import *
class _lowercase ( lowercase__):
"""simple docstring"""
def lowerCAmelCase ( self : Any ):
'''simple docstring'''
lowerCamelCase__ : List[str] = Rectangle(height=0.5 , width=0.5 )
... | 705 |
def lowercase_ ( _A : int ):
"""simple docstring"""
if not isinstance(_A , _A ):
lowerCamelCase__ : List[str] = F"Input value of [number={number}] must be an integer"
raise TypeError(_A )
if number < 0:
retu... | 5 | 0 |
import os
def lowercase_ ( _A : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_A ) , _A ) ) as input_file:
lowerCamelCase__ : List[Any] = [
[int(_A ) for element in line.split("," )]
for line in input_fi... | 706 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
A : Optional[int] = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT... | 5 | 0 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
A : Tuple = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Autom... | 707 |
from __future__ import annotations
import time
import numpy as np
A : Dict = [8, 5, 9, 7]
A : Optional[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A : Any = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1... | 5 | 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 TFCamembertModel
... | 708 |
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_sent... | 5 | 0 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
def lowercase_ ( _A : int ):
"""simple docstring"""
... | 709 |
import cva
import numpy as np
class _lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCamelCase : float , __lowerCamelCase : int ):
'''simple docstring'''
... | 5 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ... | 710 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 5 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_ten... | 711 |
import os
def lowercase_ ( _A : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_A ) , _A ) ) as input_file:
lowerCamelCase__ : List[Any] = [
[int(_A ) for element in line.split("," ... | 5 | 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_x... | 712 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
A : Tuple = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Autom... | 5 | 0 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
clas... | 713 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
A : str = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
A : Optional[int] = requests.get(url... | 5 | 0 |
import os
def lowercase_ ( ) -> Union[str, Any]:
"""simple docstring"""
with open(os.path.dirname(_A ) + "/p022_names.txt" ) as file:
lowerCamelCase__ : List[Any] = str(file.readlines()[0] )
lowerCamelCase__ : List[Any] = ... | 714 |
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 TFCa... | 5 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
A : Union[str, Any] = logging.get_logger(__name__)
class _lowercase ( lowercase__):
"""simple docstring"""
... | 715 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ... | 5 | 0 |
import pytest
A : Tuple = "__dummy_dataset1__"
A : str = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL ... | 716 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : Optional[int] = {
"facebook/xmod-base": "https://huggin... | 5 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowercase_ ( _A : NDArray[floataa] , _A : NDArray[floataa] , _A : list[int] , _A : int , ):
"""simple docstring"""
... | 717 |
import unittest
from transformers import 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 import ModelTesterMixin, ids_t... | 5 | 0 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
A : Optional[Any] = logging.get_logger(__name... | 718 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Dict = {
"kssteven/ibert-roberta-base": "ht... | 5 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Optional[int] = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
... | 719 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Union[str, Any] = {
"roberta-base": "https://huggingfa... | 5 | 0 |
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,
MusicgenProcessor,
... | 720 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tok... | 5 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
A : Optional[int] = datasets.utils.logging.get_logger(__name__)
class _lowercase ( folder_based_builder.FolderBasedBuild... | 721 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, loggi... | 5 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_... | 700 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, ... | 5 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Dict = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available():
rai... | 701 |
def lowercase_ ( _A : int , _A : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowerCamelCase__ : List[str] = str(bin(_A ) )[2:] # remove the leading "0b"
... | 5 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : str = logging.get_logger(__name__)
A : Dict = {
"facebook/d... | 702 |
import os
from pathlib import Path
def lowercase_ ( ):
"""simple docstring"""
from torch.utils.cpp_extension import load
lowerCamelCase__ : Any = Path(_A ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
lowerCamelCase__ : Optiona... | 5 | 0 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
A : Optional[Any] = logging.get... | 703 |
import os
from datetime import datetime as dt
from github import Github
A : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def lowercase_ ( ):
... | 5 | 0 |
from typing import Dict
from .base import GenericTensor, Pipeline
class _lowercase ( lowercase__):
"""simple docstring"""
def lowerCAmelCase ( self : Tuple , __lowerCamelCase : str=None , __lowerCamelCase ... | 704 |
from __future__ import annotations
def lowercase_ ( _A : str , _A : list[str] | None = None , _A : dict[str, float] | None = None , _A : bool = False , ):
"""simple docstring"""
lowerCamelCase__ : Tuple = ... | 5 | 0 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseT... | 705 |
def lowercase_ ( _A : int ):
"""simple docstring"""
if not isinstance(_A , _A ):
lowerCamelCase__ : List[str] = F"Input value of [number={number}] must be an integer"
raise TypeError(_A )
if number < 0:
retu... | 5 | 0 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.mode... | 706 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
A : Optional[int] = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT... | 5 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A : List[Any] = {
"configuration_clip": [
... | 707 |
from __future__ import annotations
import time
import numpy as np
A : Dict = [8, 5, 9, 7]
A : Optional[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A : Any = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1... | 5 | 0 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slo... | 708 |
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_sent... | 5 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A : Dict = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
... | 709 |
import cva
import numpy as np
class _lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCamelCase : float , __lowerCamelCase : int ):
'''simple docstring'''
... | 5 | 0 |
from __future__ import annotations
from collections.abc import Callable
A : Dict = list[list[float | int]]
def lowercase_ ( _A : Matrix , _A : Matrix ):
"""simple docstring"""
lowerCamelCase__ : int = len(_A )
lowerCamel... | 710 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 5 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowercase_ ( _A : dict ):
"""simple docstring"""
... | 711 |
import os
def lowercase_ ( _A : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_A ) , _A ) ) as input_file:
lowerCamelCase__ : List[Any] = [
[int(_A ) for element in line.split("," ... | 5 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lo... | 712 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
A : Tuple = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Autom... | 5 | 0 |
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
class _lowercase ( l... | 713 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
A : str = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
A : Optional[int] = requests.get(url... | 5 | 0 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : int = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-research/efficientformer-l1-300/resolv... | 714 |
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 TFCa... | 5 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( _A : str ):
"""simple docstring"""
return [ord(_A ) - 96 for elem in plain]
def lowercase_ ( _A : list[int] ):
"""simple docstring"""
return "".join(ch... | 715 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ... | 5 | 0 |
from datetime import datetime
import requests
def lowercase_ ( _A : str ):
"""simple docstring"""
lowerCamelCase__ : Any = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
lowerCamelCase__ : Tuple = requests.get(ba... | 716 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : Optional[int] = {
"facebook/xmod-base": "https://huggin... | 5 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models... | 717 |
import unittest
from transformers import 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 import ModelTesterMixin, ids_t... | 5 | 0 |
def UpperCamelCase__ ( _A : float ):
"""simple docstring"""
return 10 - x * x
def UpperCamelCase__ ( _A : float , _A : float ):
"""simple docstring"""
if equation(_A ) * equation(_A ) >= 0:
raise ValueError("Wr... | 718 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Dict = {
"kssteven/ibert-roberta-base": "ht... | 5 | 0 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUEST... | 719 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Union[str, Any] = {
"roberta-base": "https://huggingfa... | 5 | 0 |
import math
import unittest
def lowercase_ ( _A : int ):
"""simple docstring"""
assert isinstance(_A , _A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
eli... | 720 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tok... | 5 | 0 |
import unittest
from transformers import 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 import ModelTesterMixin, ids_t... | 721 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, loggi... | 5 | 0 |
def lowercase_ ( _A : List[Any] , _A : int , _A : Union[str, Any] , _A : Union[str, Any] , _A : List[str] , _A : int ):
if index == r:
for j in range(_A ):
print(data[j] ,... | 700 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, ... | 5 | 0 |
from collections import defaultdict
def lowercase_ ( _A : str , _A : str ):
"""simple docstring"""
lowerCamelCase__ : Optional[int] = first_str.lower().strip()
lowerCamelCase__ : Optional[Any] = second_str.lower().strip()
... | 701 |
def lowercase_ ( _A : int , _A : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowerCamelCase__ : List[str] = str(bin(_A ) )[2:] # remove the leading "0b"
... | 5 | 0 |
import os
from datetime import datetime as dt
from github import Github
A : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def lowercase_ ( ):
... | 702 |
import os
from pathlib import Path
def lowercase_ ( ):
"""simple docstring"""
from torch.utils.cpp_extension import load
lowerCamelCase__ : Any = Path(_A ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
lowerCamelCase__ : Optiona... | 5 | 0 |
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,
... | 703 |
import os
from datetime import datetime as dt
from github import Github
A : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def lowercase_ ( ):
... | 5 | 0 |
from __future__ import annotations
def lowercase_ ( _A : list[int] ):
"""simple docstring"""
return len(set(_A ) ) == len(_A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 704 |
from __future__ import annotations
def lowercase_ ( _A : str , _A : list[str] | None = None , _A : dict[str, float] | None = None , _A : bool = False , ):
"""simple docstring"""
lowerCamelCase__ : Tuple = ... | 5 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 705 |
def lowercase_ ( _A : int ):
"""simple docstring"""
if not isinstance(_A , _A ):
lowerCamelCase__ : List[str] = F"Input value of [number={number}] must be an integer"
raise TypeError(_A )
if number < 0:
retu... | 5 | 0 |
import functools
def lowercase_ ( _A : list[int] , _A : list[int] ):
"""simple docstring"""
if not isinstance(_A , _A ) or not all(isinstance(_A , _A ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
i... | 706 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
A : Optional[int] = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT... | 5 | 0 |
from math import pi, sqrt
def lowercase_ ( _A : float ):
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
if num > 171.5:
raise OverflowError("math range error" )
elif num - int(_A ) not in (0, 0.5):
... | 707 |
from __future__ import annotations
import time
import numpy as np
A : Dict = [8, 5, 9, 7]
A : Optional[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A : Any = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1... | 5 | 0 |
import pprint
import requests
A : List[str] = "https://zenquotes.io/api"
def lowercase_ ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def lowercase_ ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + "/rand... | 708 |
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_sent... | 5 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( _A : Optional[int] , _A : int , ... | 709 |
import cva
import numpy as np
class _lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCamelCase : float , __lowerCamelCase : int ):
'''simple docstring'''
... | 5 | 0 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
fr... | 710 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 5 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable... | 711 |
import os
def lowercase_ ( _A : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_A ) , _A ) ) as input_file:
lowerCamelCase__ : List[Any] = [
[int(_A ) for element in line.split("," ... | 5 | 0 |
from __future__ import annotations
import time
import numpy as np
A : Dict = [8, 5, 9, 7]
A : Optional[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A : Any = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1... | 712 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
A : Tuple = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Autom... | 5 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _lowercase ( lowercase__ , lowercase__):
"""simple docstring"""
@register_... | 713 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
A : str = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
A : Optional[int] = requests.get(url... | 5 | 0 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
... | 714 |
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 TFCa... | 5 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.ut... | 715 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ... | 5 | 0 |
def lowercase_ ( _A : str ):
"""simple docstring"""
lowerCamelCase__ : Dict = [0 for i in range(len(_A ) )]
# initialize interval's left pointer and right pointer
lowerCamelCase__ : List[str] = 0, 0
for i in range(1 , ... | 716 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : Optional[int] = {
"facebook/xmod-base": "https://huggin... | 5 | 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,
to_channel_dim... | 717 |
import unittest
from transformers import 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 import ModelTesterMixin, ids_t... | 5 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _lowercase ( lowercase__):
"""simple docstring"""
A__ = "Speech2TextFeatureExtractor"
A__ = "Speech2TextTokenizer"
... | 718 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Dict = {
"kssteven/ibert-roberta-base": "ht... | 5 | 0 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modelin... | 719 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Union[str, Any] = {
"roberta-base": "https://huggingfa... | 5 | 0 |
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(lowercase__) , "Tatoeba d... | 720 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tok... | 5 | 0 |
def lowercase_ ( _A : list , _A : int = 0 ):
"""simple docstring"""
lowerCamelCase__ : List[Any] = length or len(_A )
lowerCamelCase__ : List[Any] = False
for i in range(length - 1 ):
if list_data[i] > li... | 721 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, loggi... | 5 | 0 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulat... | 700 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, ... | 5 | 0 |
import heapq as hq
import math
from collections.abc import Iterator
class _lowercase :
"""simple docstring"""
def __init__( self : List[str] , __lowerCamelCase : List[Any] ):
'''simple docstring'''
... | 701 |
def lowercase_ ( _A : int , _A : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowerCamelCase__ : List[str] = str(bin(_A ) )[2:] # remove the leading "0b"
... | 5 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : List[str] = {
"microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
... | 702 |
import os
from pathlib import Path
def lowercase_ ( ):
"""simple docstring"""
from torch.utils.cpp_extension import load
lowerCamelCase__ : Any = Path(_A ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
lowerCamelCase__ : Optiona... | 5 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IM... | 703 |
import os
from datetime import datetime as dt
from github import Github
A : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def lowercase_ ( ):
... | 5 | 0 |
import unittest
import numpy as np
from transformers import AlbertConfig, 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.nump... | 704 |
from __future__ import annotations
def lowercase_ ( _A : str , _A : list[str] | None = None , _A : dict[str, float] | None = None , _A : bool = False , ):
"""simple docstring"""
lowerCamelCase__ : Tuple = ... | 5 | 0 |
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