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 |
|---|---|---|---|---|
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class __lowercase ( _lowercase ):
lowerCamelCase : Union[str, Any] = ["image_processor", "feature_extractor"]
lowerCamelCase : Dict = "TvltImageProcessor"
lowerCamelCase : Optional[int] = "TvltFea... | 318 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from t... | 318 | 1 |
def lowercase_ ( _lowerCamelCase : int):
lowercase__ : List[str] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowercase_ ( _lowerCamelCase : int = 100):
lowercase__ : Any = 1
low... | 356 | import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__A )
class snake_case_ ( __A ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output f... | 333 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase = 2 , __lowerCAmelCase = 1 , __lowerCAmelCase = 3 , ) -> int | None:
# A value less than 2 can cause an infinite loop in the algor... | 132 |
"""simple docstring"""
a :dict[tuple[int, int, int], int] = {}
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late ... | 132 | 1 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
UpperCAmelCase__ = False
UpperCAmelCase__ = True
UpperCAmelCase__ = False
if __name__ == "_... | 360 |
"""simple docstring"""
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 imp... | 40 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( A__ ):
def __init__( self : str , *_a : Optional[in... | 47 |
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ... | 266 | 0 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import loggi... | 358 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput... | 277 | 0 |
"""simple docstring"""
class a :
def __init__( self : Tuple ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: List[str] ={}
def lowerCamelCase__ ( self : List[str] ) -> List[str]:
'''simple do... | 173 |
def a( A : int ) -> str:
"""simple docstring"""
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 ... | 227 | 0 |
'''simple docstring'''
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__snake_case = abspath(join(dirname(dirname(__fi... | 367 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormat... | 219 | 0 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __A( _a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (KDPMaDiscreteScheduler,)
SCREAMING_SNAKE_CASE__ = 10
def ... | 244 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[Any] = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https... | 333 | 0 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
snake_case__ : Optional[Any] = [
'''encoder.version... | 44 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 | 1 |
"""simple docstring"""
from typing import Any
class snake_case_:
def __init__( self : Any , UpperCamelCase_ : Any ):
lowerCAmelCase : Optional[Any] = data
lowerCAmelCase : Optional[int] = None
def __repr__( self ... | 60 |
"""simple docstring"""
def lowercase ( A_ )-> str:
'''simple docstring'''
if isinstance(A_ , A_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(A_ , A_ ):
raise TypeError("'str' obj... | 40 | 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
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : int ) -> int:
Upp... | 99 |
from __future__ import annotations
import queue
class __UpperCAmelCase :
def __init__( self : str, __A : Union[str, Any] ):
UpperCAmelCase : Dict = data
UpperCAmelCase : Tuple = None
UpperCAmelCase : Any = None
... | 99 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_: Optional[Any] ={
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',... | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ :Optional[int] = logging.get_logger(__name__)
a_ :Dict = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class snake_case__ ( lowerCAmelCase_ ):
... | 277 | 0 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def SCREAMING_SNAKE_CASE_ ( __A : Tuple , __A : int , __A : List[str]=10_24 , __A : Tu... | 355 |
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 SCREAMING_SNAKE_CASE_ ... | 120 | 0 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __UpperCAmelCase ( a_: Any, a_: float = 0.0, a_: float = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
... | 145 | import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTes... | 219 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class _UpperCamelCase ( A ... | 363 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCas... | 48 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
_a : Dict = {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', '... | 44 | """simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __A ( unittest.TestCase ):
def __A ( self ):
... | 44 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase : Tuple = {
... | 362 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self : str , _lowercase : list ):
__UpperCAmelCase = set_counts
__UpperCAmelCase = max(_lowercase )
__UpperCAmelCase = len(_lowercase )
__UpperCAmelCase ... | 86 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorTy... | 99 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_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_co... | 99 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import loggin... | 83 |
'''simple docstring'''
class snake_case__ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : list[int] ) -> None:
"""simple docstring"""
snake_case : List[Any] = len(UpperCamelCase... | 83 | 1 |
"""simple docstring"""
from typing import List
import numpy as np
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Optional[Any] = {key: len(A__ ) for key, value in gen_kwargs.items() if isinstance(A__, A__ )}
if len(set(lists_lengths.values() ) ) > 1:
... | 66 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : int ):
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def UpperCamelCase_ ( A__ : int = 50_00 ):
''... | 120 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __lowerCamelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = args.pruning_method
lowerCAmelCase__ = args.threshold
lowerCA... | 363 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['TransfoXLCorpus', '... | 119 | 0 |
def __a ( SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('''Input valu... | 333 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers... | 48 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 107 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __magic_name__ :
lowerCAmelCase : str = field(
... | 107 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testin... | 145 |
"""simple docstring"""
from __future__ import annotations
import bisect
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 0 , _UpperCamelCase = -1 ):
if hi < 0:
__lowerCAmelCase : Tuple = len(_UpperCamelCase )
while lo < hi:
__l... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 367 |
"""simple docstring"""
from __future__ import annotations
import queue
class __lowerCamelCase :
def __init__(self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = data
_lowerCAmelCase = None
_lowerCAmelCase = ... | 317 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
... | 83 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ ):
if num < 0:
return False
_UpperCamelCase : int = num
_UpperCamelCase : int = 0
while num > 0:
_UpperCamelCase : str = rev_num * 1_0 + (num % 1_0)
num //= 1_0... | 83 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : str = """M-CLIP"""
def __init__( self , __lowerCAmelCase=1024 , __lowerCAmelCase=768 , **__lowerCAmelCase ... | 351 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftFormerOnnxConfig",
... | 87 | 0 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def __UpperCamelCase ( UpperCAmelCase ):
if not isinstance(snake_case__ , snake_case__ ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
raise ValueError('''Undefin... | 198 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCAmelCase = {
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
'''tokenization_transfo_xl'''... | 119 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
lowercase__ = True
except (ImportError, ModuleNotFoundError):
lowercase__ = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True... | 368 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase = ["""image_processor""", """to... | 83 | 0 |
# Function to print upper half of diamond (pyramid)
def __magic_name__ ( A : Dict ):
'''simple docstring'''
for i in range(0, A ):
for _ in range(0, n - i - 1 ): # printing spaces
print(" ", end="" )
for _ in range(0, i + 1 ): # printing stars
... | 107 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class snake_case__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str ... | 107 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :list , __lowerCamelCase :list , __lowerCamelCase :int ):
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError("""The length of profit and weight must be same.""" )
if max_weight <= 0:
rai... | 361 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModel... | 229 | 0 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class snake_case ( __lowerC... | 53 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
"""google/efficientnet-b7""": ... | 317 | 0 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def UpperCAmelCase ( a_ , a_ , **a_ ) -> Tuple:
"""simple docstring"""
__A = AutoConfig.from_pretrained(a_ , **a_ )
__A = AutoMod... | 124 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : int ,*A : D... | 124 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : List[Any] ={
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',... | 9 | from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def lowercase_ ( _lowerCamelCase : Dict[str, torch.Tensor]):
lowercase__ : Any = []
lowercase__ : Optional[int] ... | 87 | 0 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from d... | 304 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import Con... | 304 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _SCREAMING_SNAKE_CASE ( ... | 38 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ = 1_0_0_0 ):
_UpperCamelCase : Dict = 3
_UpperCamelCase : Any = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
... | 83 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from tr... | 312 | """simple docstring"""
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase = get_tests_di... | 312 | 1 |
from __future__ import annotations
import numpy as np
def __lowerCAmelCase ( a__ ) -> tuple[np.ndarray, np.ndarray]:
__a , __a = np.shape(a__ )
if rows != columns:
__a = (
'''\'table\' has to be of square shaped array but got a '''
... | 6 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAtt... | 229 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from ... | 216 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'''vocab_file''': '''vocab.json''',
'''tokenizer_config_file''': '''tokenize... | 216 | 1 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __lowercase (tf.keras.layers.Layer ):
"""simple docstring"""
... | 124 |
from jiwer import compute_measures
import datasets
lowerCamelCase : str = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved ev... | 124 | 1 |
__A : Dict = '\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'
__A : Dict = [{'type': 'code',... | 363 |
from string import ascii_uppercase
__A : int = {str(ord(c) - 55): c for c in ascii_uppercase}
def __UpperCamelCase ( _A : int , _A : int ) ->str:
"""simple docstring"""
if isinstance(_A , _A ):
raise TypeError("""i... | 49 | 0 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def __UpperCAmelCase ( A : int ) -> datetime:
UpperCAmelCase_ : List[str] = year % 1_9
UpperCAmelCase_ : Dict = year % 4
UpperCAmelCase_ : int = year % 7
UpperCAmelCase_ : s... | 304 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test... | 304 | 1 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowerCamelCase ) -> Any:
A_ : Dict = data
A_ : ... | 370 |
'''simple docstring'''
def UpperCAmelCase ( a_ = 5_0_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
A_ : Union[str, Any] = set()
A_ : List[str] = int((limit - 2_4) ** (1 / 2) )
A_ : Dict = set(range(3 ... | 164 | 0 |
import numpy as np
def __snake_case ( __UpperCamelCase : np.ndarray ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __snake_case ( __UpperCamelCase : np.ndarray ):
"""simple docstring"""
return vector * sigmoid(__Up... | 312 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=snake_case_ )
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamel... | 312 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__snake_case = logging.get_logger(__name__)
class lowercase__ ( _UpperCAmelCase ):
def __init__( self : Dict , *UpperCAmelCase_ : ... | 361 |
import doctest
from collections import deque
import numpy as np
class lowercase__ :
def __init__( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE__ = [1, 2, 3, 4]
def A_ ( self : ... | 169 | 0 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowercase__ =WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
def __UpperCamelCase ( lowerCAmelCase__ : Optiona... | 216 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __UpperCamelCase ( lowerCAmelCase__ : Any ):
# vision encoder
if "img_encoder.pos_embed" in name:
__a : Any = name.replace('''img_encoder.pos... | 216 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase = {
'configuration_blenderbot': [... | 355 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStruct... | 93 | 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 requi... | 170 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _A ( __UpperCAmelCase ):
def __init__( self : Optional[int] ... | 49 | 0 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : Any = []
embed.ap... | 65 |
from manim import *
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def _snake_case ( self )->Tuple:
'''simple docstring'''
A_ : Optional[int] = Rectangle(height=0.5 , width=0.5 )
A_ : Union[str, Any] ... | 65 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def __a ( __lowerCam... | 61 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else... | 164 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
... | 370 |
import math
def __lowerCamelCase ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ):
"""simple docstring"""
return math.pow(UpperCAmelCase_ , 2 ) - a
def __lowerCamelCase ( UpperCAmelCase_ : float ):
"""simple docstring"""... | 281 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class lowerCamelCase_ ( __a ):
def __init__( self : Optional[... | 181 |
from ...processing_utils import ProcessorMixin
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAmelCase_ = ["""image_processor""", """feature_extractor"""]
UpperCAmelCase_ = """TvltImageProcessor"""
UpperCAmelCase_ = """TvltFeatureExtractor"""
def __i... | 169 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normaliz... | 264 |
'''simple docstring'''
from PIL import Image
def snake_case_ ( __SCREAMING_SNAKE_CASE : Image , __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
lowercase_ : Optional[int] = (259 * (level + 255)) / (255 * (259 - level))
... | 264 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = ["image_processor", "tokenizer"]
__Up... | 91 |
'''simple docstring'''
import argparse
import copy
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
lowercase_ : List[Any] = {}
with open(__SCREAMING_SNAKE_CASE ) as f:
... | 93 | 0 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase = "https://www.worldometers.info/coronavirus" ) -> dict:
lowercase__: List[str] = BeautifulSoup(requests.get(__UpperCAmelCase ).text , '''html.parser''' )
lower... | 2 | """simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWit... | 2 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCas... | 65 | import math
import random
def lowerCAmelCase_ ( __A, __A = False ) -> float:
'''simple docstring'''
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
UpperCamelCase__ = 0.0_... | 65 | 1 |
def __lowerCAmelCase ( a__ , a__ ) -> int:
__a = [1]
for i in range(2 , UpperCAmelCase_ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
__a = []
__a = list(range(UpperCA... | 356 |
import os
# Precomputes a list of the 100 first triangular numbers
A : List[Any] = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def __lowerCAmelCase ( ) -> Tuple:
__a = os.path.dirname(os.path.realpath(a__ ) )
__a = os.path.join(a__ , ... | 33 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class lowercase ( A__ ):
"""simple docstring"""
_a = 'bert-generation'
def __init__( self , UpperCamelCase_=50358 , UpperCamelCase_=1024 , UpperCamelCase_=24 , UpperCamelCase_=16 , Upp... | 97 |
def lowerCAmelCase_ ( _snake_case : str , _snake_case : str ) -> bool:
'''simple docstring'''
__magic_name__ : Union[str, Any] = len(_snake_case ) + 1
__magic_name__ : List[str] = len(_snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes wh... | 281 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 356 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
requir... | 220 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp... | 264 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def __lowercase ( _a ):
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... | 264 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase : Optional[int] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE__ ( ... | 160 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 160 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def _SCREAMING_SNAKE_CASE (A = "https://www.worldometers.info/coronavirus" ) -> dict:
"""simple docstring"""
lowercase__ = BeautifulSoup(requests.get(A ).text , '''html.parser''' ... | 2 |
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (A ) -> bool:
"""simple docstring"""
return len(set(A ) ) == len(A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 1 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : Union[str, Any] = [1]
for i in range(2 , __lowerCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of... | 134 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_snake_case : int = "scheduler_config.json"
class a (_lowerCAmelCase ):
... | 134 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : int = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_torch_available():
... | 94 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcess... | 33 | 0 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''... | 273 |
def lowerCAmelCase_ ( __a , __a ) -> Tuple:
"""simple docstring"""
assert x is not None
assert y is not None
lowerCamelCase__: Any =len(__a )
lowerCamelCase__: int =len(__a )
# declaring the array for storing the dp values
lowerCamelCase__: L... | 273 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import... | 230 |
"""simple docstring"""
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
_UpperCamelCase : Optional[Any] = TypeVar('T')
class a ( Generic[T] )... | 220 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
... | 69 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : str = {
'funnel-transformer/small': 'https://huggingface.co/funnel-trans... | 69 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_ima... | 160 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTea... | 160 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision... | 259 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCamelCase ( metaclass=lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] =["""transformers""", """torch""", """note_seq"""]
def __init__( self , ... | 259 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : List[Any] = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
... | 134 |
'''simple docstring'''
import os
from math import logaa
def __lowerCamelCase ( __snake_case : str = "base_exp.txt" ) -> int:
"""simple docstring"""
A__ : float =0
A__ : Optional[int] =0
for i, line in enumerate(open(os.pa... | 134 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
a_ = logging.get_logger(__name__) # pylint: disable=invalid-name
... | 366 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"co... | 163 | 0 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> str:
'''simple d... | 273 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Huggi... | 273 | 1 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class A ( __UpperCAmelCase ,... | 362 |
_A = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800.00,
"electronvolt": 1.602... | 167 | 0 |
"""simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> float:
if digit_amount > 0:
return round(number - int(UpperCAmelCase ) , UpperCAmelCase )
return number - int(UpperCAmelCase )
if __name__ == "__main__":
print(... | 69 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> list:
if len(UpperCAmelCase ) <= 1:
return [tuple(UpperCAmelCase )]
snake_case_ = []
def generate(UpperCAmelCase , UpperCAmelCase ):
snake_case_ = ... | 69 | 1 |
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 = logging.get_logger(__name__)
__UpperCAmelCase = {'''vocab_file''': '... | 103 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 103 | 1 |
from __future__ import annotations
def _A ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[list[str]] , SCREAMING_SNAKE_CASE__ : int , ):
UpperCamelCase :Dict = ... | 259 |
import sys
def _A ( SCREAMING_SNAKE_CASE__ : List[str] ):
UpperCamelCase :Any = len(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Any = [[0 for x in range(SCREAMING_SNAKE_CASE__ )] for x in range(SCREAMING_SNAKE_CASE__ )]
UpperCamelCase ... | 259 | 1 |
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase__ ( a , a , a ) -> float:
_A: List[str] = x
_A: Any = y
for step in range(a ): # noqa: B007
_A: List[Any] = a * a - b * b + x
_A: Tuple = 2 * a * b + y
... | 301 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __lt__( self : Dict , lowerCAmelCase_ :... | 301 | 1 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_337 , num_exa... | 35 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _UpperCamelCase ... | 163 | 0 |
"""simple docstring"""
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():
i... | 58 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Optional[Any] = logging.get_logger(__name__)
# TODO Update this
__snake_case ... | 58 | 1 |
'''simple docstring'''
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 n... | 53 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCa... | 167 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_availa... | 350 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
Uppe... | 198 | 0 |
def UpperCamelCase( __UpperCamelCase : list[list[float]] ):
lowerCAmelCase_ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(__UpperCamelCase ):
if len(__UpperCamelCase ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(__UpperCa... | 103 |
import argparse
import os
import re
import packaging.version
A__ : Dict = '''examples/'''
A__ : Any = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^__version__\s+=\s+"([^... | 103 | 1 |
"""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_device
from ...... | 202 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCAmelCase : Dict = "src/transformers"
# This is to make sure the... | 202 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
... | 113 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
def __snake_case ( _lowerCAmelCase : int , _lowerCAmelCase : Any ) ... | 300 | 0 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import requ... | 103 |
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 ...test_p... | 103 | 1 |
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : float , __lowerCamelCase : float ) ->float:
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
... | 58 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def lowerCamelCase ( __lowerCamelCase : str ) ->str:
if not sentence:
return ""
_SCREAMING_SNAKE_CASE = dict(zip(__lowerCamelCase , __lowerCamelCase ) )
return lower_t... | 58 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from di... | 247 |
from manim import *
class lowercase__ ( __lowerCamelCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase__ : int = Rectangle(height=0.5, width=0.5 ... | 247 | 1 |
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class _A ( a__):
SCREAMING_SNAKE_CASE : Optional... | 253 | '''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTo... | 198 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case_)
class SCREAMING_SNAKE_CASE__ ( snake_case_):
# `task` is not a ClassVar since we w... | 251 |
'''simple docstring'''
from itertools import product
def A_( A : int , A : int):
UpperCamelCase = sides_number
UpperCamelCase = max_face_number * dice_number
UpperCamelCase = [0] * (max_total + 1)
UpperCamelCase = ... | 251 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...t... | 118 |
from __future__ import annotations
def lowerCAmelCase_ ( __UpperCAmelCase: int , __UpperCAmelCase: int ) -> list[list[int]]:
UpperCamelCase__ : list[list[int]] = []
create_all_state(1 , __UpperCAmelCase , __UpperCAmelCase , [] , __... | 201 | 0 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase_ = arr.split("," )
def A__ ( self ) -> int:
... | 369 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTest... | 297 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteS... | 103 |
import unittest
from transformers import MPNetConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixi... | 103 | 1 |
"""simple docstring"""
import math
import os
import sys
def __A ( a_ :str) -> str:
__a : List[str] = ''''''
try:
with open(a_ , '''rb''') as binary_file:
__a : List[Any] = binary_file.read()
for d... | 188 |
"""simple docstring"""
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
... | 188 | 1 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils imp... | 247 |
"""simple docstring"""
import qiskit
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> qiskit.result.counts.Counts:
A__ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
A__ = qiskit.QuantumCircuit(lower... | 247 | 1 |
'''simple docstring'''
def a ( __a = 100 ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = set()
UpperCamelCase__ :Dict = 0
UpperCamelCase__ :List[str] = n + 1 # maximum limit
fo... | 366 |
'''simple docstring'''
from __future__ import annotations
import math
def a ( __a ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives,... | 219 | 0 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoA... | 251 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' ,[
['full:README.md', 'dataset_infos.json'],
['empty:README... | 251 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class _UpperCAmelCase ( snake_case_ ):
snake_case = ... | 356 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
def __init__( self : Dict , __UpperCAmelCase : Union[str, Any]="" , __Upper... | 174 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ = 100 ):
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__mai... | 100 |
'''simple docstring'''
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_... | 297 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from trans... | 366 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, Table... | 67 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.