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 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase = {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeTowerConfig''',
'''BridgeTowerT... | 195 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vi... | 195 | 1 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
f... | 248 |
"""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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassificatio... | 248 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class lowerCamelCase_ :
"""simple docs... | 208 | """simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threade... | 44 | 0 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbedding... | 85 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if not is_torch_... | 85 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase__ ( snake_case__, uni... | 144 |
"""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 _snake_case ( lowerCamelCase__ : T... | 144 | 1 |
import warnings
from functools import wraps
from typing import Callable
def __lowerCamelCase ( _lowercase ) -> Callable:
@wraps(_lowerCamelCase )
def _inner_fn(*_lowercase , **_lowercase ):
warnings.warn(
(F'''\'{fn.__name__}\' is experimenta... | 363 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> ... | 338 | 0 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTester... | 32 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.ut... | 17 | 0 |
'''simple docstring'''
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : List[str] = len(lowerCAmelCase_ )
for i in range(n - 1 ):
for j in range(i + 1 , lowerCAmelCase_ ):
if arr[i] > arr[j]:
num_inversions... | 170 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 170 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case : Union[str, Any] = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"... | 248 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snak... | 248 | 1 |
import argparse
import os
import re
import packaging.version
lowerCAmelCase_ = "examples/"
lowerCAmelCase_ = {
"examples": (re.compile(R'^check_min_version\(\"[^\"]+\"\)\s*$', re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R'^__version__\s+=\s+\"([^\... | 350 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MA... | 116 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE ... | 85 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
class _snake_case ( lowercase_ ):
lower... | 85 | 1 |
'''simple docstring'''
import argparse
import datetime
def SCREAMING_SNAKE_CASE_ ( __A : str ) -> str:
_SCREAMING_SNAKE_CASE = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Satur... | 356 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
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]' wh... | 111 | 0 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
... | 13 | lowercase__ : Optional[int] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def SCREAMING_SNAKE_CASE_ ( ) -> None:
lowerCAmelCase = input('''Enter message: ''' )
lowerCAmelCase = input('''Enter key [alphanumeric]: ''' )
lowerCAmelCase = input('''Encrypt/Decrypt [... | 338 | 0 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
... | 352 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class snake_case ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , *__UpperCAmelCase , **__Uppe... | 303 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] ={
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_availa... | 170 |
# 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 | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
A = logging... | 351 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import Patchi... | 227 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ : Optional[Any] = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE... | 63 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
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 impo... | 116 | 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
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""microsoft/bei... | 352 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
# Check if the input is valid
if not len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE ) == 3:
raise ValueError('''Please enter a valid equation.''' )
if equationa[0] == equationa[1] == equationa[0] == equationa... | 65 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
_a = len(SCREAMING_SNAKE_CASE__)
for i in range(SCREAMING_SNAKE_CASE__):
for j in range(i + 1 , SCREAMING_SNAKE_CASE__):
if numbers[j] < numbers[i]:
_a = numbe... | 211 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
class __snake_case ( __lowerCamelCase ):
'''simple docstring'''
def __i... | 111 | 0 |
"""simple docstring"""
def __lowercase ( _a = "The quick brown fox jumps over the lazy dog" , ):
snake_case_ : str = set()
# Replace all the whitespace in our sentence
snake_case_ : Union[str, Any] = input_str.replace(''' ''' , '''''' )
for alpha ... | 155 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __lowercase ( _a = 3 ):
if isinstance(_a , _a ):
raise TypeError('''number of qubits must be a integer.''' )
if num... | 155 | 1 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer... | 154 |
import math
import os
import sys
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = ''''''
try:
with open(snake_case , '''rb''' ) as binary_file:
__SCREAMING_SNAKE_CASE : int = binary_file.read()
for dat in data:
... | 303 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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_comm... | 115 |
from __future__ import annotations
def __UpperCamelCase ( _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Tuple = 2
A : List[Any] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 115 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_v... | 248 |
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 torch
from ... | 227 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docs... | 356 |
"""simple docstring"""
import math
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = int(math.floor(math.sqrt(_SCREAMING_SNAKE_CASE ) ) )
UpperCamelCase ... | 244 | 0 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_lowerCamelCase : Optional[int] = TypeVar("KEY")
_lowerCamelCase : int = TypeVar("VAL")
@dataclass(frozen=UpperCAmelCase_ , slots=UpperCAmelCase_ ... | 336 | 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(UpperCAmelCase_ ) , '... | 65 | 0 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (UnCLIPScheduler,)
def A ( ... | 362 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_lowerCamelCase : Dict = [
# tf -> hf
("/", "."),
("la... | 249 | 0 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( _a ):
_a ... | 155 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from tran... | 155 | 1 |
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = logging.get_l... | 3 |
'''simple docstring'''
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,
)
fr... | 3 | 1 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,... | 115 |
"""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 lowerCamelCase__ ( unittest.TestCase ):
"""sim... | 115 | 1 |
"""simple docstring"""
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__magic_name__ = re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class SCREAMING_SNAKE_CASE_... | 365 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__magic_name__ = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
de... | 255 | 0 |
"""simple docstring"""
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 lowerCamelCase ( _UpperCamelCase : Dict[str, torch.Tensor] ) -> str:
'''simple docstring'''
... | 115 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __magic_name__ ( __a : Optional[int] , __a : Union[str, Any] , __a : Union[str, Any]=1_024 , __... | 244 | 0 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCAmelCase__ ( _a : int = 8 ):
snake_case_ : Any = ascii_letters + digits + punctuation
return "".join(secrets.choice(_a ) for ... | 36 |
import argparse
import copy
def lowerCAmelCase__ ( _a : List[Any] ):
snake_case_ : List[Any] = {}
with open(_a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case_ : int = []
_list.append([line.spl... | 36 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common impor... | 99 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCAmelCas... | 249 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
fro... | 352 |
'''simple docstring'''
from __future__ import annotations
def lowercase__( __UpperCamelCase: list[int] ,__UpperCamelCase: int ,__UpperCamelCase: int ,__UpperCamelCase: int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > a... | 246 | 0 |
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase : Optional[Any] = logging.get_logg... | 3 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
A : str = BeautifulSoup(requests.get(snake_case__ , params=snake_case__ ).content ... | 3 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
"google/bit-50": "https://huggingface.co/google/bit-50/resolve/mai... | 343 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
# Load checkp... | 343 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : int = {}
try:
if not is_sentencepiece_available():
... | 31 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: List[Any] = logging.get_logger(__name__)
_UpperCamelCase: int = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
... | 255 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> Dict:
"""simple docstring"""
_UpperCamelCase = F'''{file}_{class_name}_{tes... | 350 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowerCamelCase__ ( __snake_case ) -> None:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase ... | 100 | 0 |
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
_snake_case = logging.get_logger(__name__)
_snake_case = {
"google/efficientnet-b7": "https:... | 36 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import... | 36 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-wind... | 174 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def __lowercase ( __lowercase = 150_0000 ) -> int:
'''simple docstring'''
_A = defaultdict(__lowercase )
_A = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:... | 174 | 1 |
"""simple docstring"""
def lowercase ( A_ , A_ )-> List[str]:
'''simple docstring'''
a : str = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowercase ( A_ , A_ , A... | 40 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 246 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Any = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
if not is_torch_available():... | 225 |
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 : List[str] = {
"""configuration_clip""": [
... | 225 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""google/bit-50""": """https:/... | 343 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
f... | 343 | 1 |
"""simple docstring"""
__UpperCamelCase = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 100_0000,
"gigajoule": 10_0000_0000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 360_0000,
"newtonmeter": 1.0,
"calorie_nutr": 4186.8,
"kilocalorie_nutr": 418_6800.00... | 312 | """simple docstring"""
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_i... | 312 | 1 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__magic_name__ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "|... | 100 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__magic_name__ = logging.get_logger(__name_... | 100 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaF... | 164 |
'''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 | 1 |
'''simple docstring'''
from __future__ import annotations
class a__ :
"""simple docstring"""
def __init__(self , __lowercase , __lowercase ):
__lowerCAmelCase , __lowerCAmelCase = text, pattern
__lowerCAmelCase , __l... | 174 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae ... | 174 | 1 |
"""simple docstring"""
from __future__ import annotations
def _A (__a , __a , __a ) -> List[Any]:
"""simple docstring"""
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('''One and only one argument must be ... | 365 |
"""simple docstring"""
from itertools import permutations
def _A (__a ) -> bool:
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
SCREAMING_SNAK... | 318 | 0 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowerCamelCase__ : str = tra... | 225 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
lowerCamelCase__ : List[str] = {
'vocab_file': 'vocab.json',
'toke... | 225 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,... | 222 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'L... | 222 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__a :List[str] = get_tests_dir(... | 312 |
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 _a ( snake_case_ ):
"""simple doc... | 312 | 1 |
'''simple docstring'''
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class a__ ( a__ ):
'''simple docstring'''
lowercase__ : List[str] = "openai/whisper-... | 352 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class a__ ( a__ ):
'''simple docstring'''
lowercase__ : ... | 228 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from tran... | 164 |
'''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 | 1 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedT... | 363 |
from __future__ import annotations
from typing import Any
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : list[Any] ):
create_state_space_tree(SCREAMING_SNAKE_CASE__ , [] , 0 )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : list[Any] , SCR... | 117 | 0 |
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
if... | 29 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 318 | 0 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import cl... | 355 |
"""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,
)
if is_sentencepiece_available():
from ..ta... | 195 | 0 |
import sys
def A ( lowercase ) -> str:
'''simple docstring'''
UpperCamelCase = len(lowercase )
UpperCamelCase = [[0 for x in range(lowercase )] for x in range(lowercase )]
UpperCamelCase = [[0 for x in range(lowercase )] for x in range(lowercase )]
for chain_length in ra... | 222 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requir... | 222 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tenso... | 350 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__UpperCamelCase : Any = datasets.ut... | 347 | 0 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_... | 128 |
import math
from datetime import datetime, timedelta
def __A ( __lowerCamelCase ) -> datetime:
a = year % 19
a = year % 4
a = year % 7
a = math.floor(year / 100 )
a = math.floor((13 + 8 * leap_... | 228 | 0 |
import torch
from diffusers import DiffusionPipeline
class lowercase ( _UpperCamelCase ):
'''simple docstring'''
def __init__(self , __a , __a ) -> List[Any]:
"""simple docstring"""
super().__init__()
self.register_modules(unet=__a , scheduler=__a... | 353 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ... | 335 | 0 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
imp... | 10 |
import os
from datetime import datetime as dt
from github import Github
snake_case__ : Union[str, Any] = [
'good first issue',
'feature request',
'wip',
]
def _a ( ) -> List[Any]:
'''simple docstring'''
... | 117 | 0 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ ) -> Union[str... | 367 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {... | 116 | 0 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__UpperCAmelCas... | 67 |
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
UpperCAmelCase = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE... | 195 | 0 |
# Copyright 2023 The HuggingFace Inc. 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 re... | 120 |
def SCREAMING_SNAKE_CASE_ ( __A : list ) -> list:
"""simple docstring"""
a_ : int = len(__A )
for _ in range(__A ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
... | 120 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class A ( ... | 305 |
"""simple docstring"""
from __future__ import annotations
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
print(f"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(_SCREAMING_SNAKE_CASE ):
print(f"""{i}\t\t{d}""" )
... | 347 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__A : List[s... | 89 |
'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def UpperCamelCase_ ( A__ : np.ndarray , A__ : np.ndarray , A__ : np.ndarray , A__ : int , A__ : ... | 89 | 1 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
@requir... | 227 |
"""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, ImageClassifierOutputWithNoAttenti... | 335 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class snake_case_ ( __A ):
def __UpperCamelCase ( self : int , lowercase_ : str ) -> Optional[Any]:
with open(lowercase_ , en... | 333 | from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils impo... | 333 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : Union[str, Any] ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Any = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
lowerCAmelCase_... | 84 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
"""simple docstring"""
while second != 0:
A : int = first & second
first ^= second
A : Tuple = c << 1
return first
if __name__ == "__main__":
im... | 116 | 0 |
"""simple docstring"""
# Copyright 2022 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
#
# Unle... | 54 | """simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_ava... | 54 | 1 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__A : Dict ... | 120 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
__A : Dict = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is... | 120 | 1 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
__a :Union[str, Any] = 100
__a :Union[str, Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__a :int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
... | 329 |
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_co... | 329 | 1 |
'''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
__l... | 89 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __magic_name__ ( _UpperCamelCase ):
@require_torch
def __lowercase ( ... | 89 | 1 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ... | 368 |
from ..utils import DummyObject, requires_backends
class A( metaclass=UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = ['''keras_nlp''']
def __init__( self : Optional[int] , *A_ : Any , **A_ : Dict ) -> ... | 208 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class A_ ( _a ):
'''simple docstring'''
def lowerCAmelCase_ (self , lowercase__ ) -> Optional[int]:
with open(lowercase__ , enco... | 333 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 333 | 1 |
from ..utils import DummyObject, requires_backends
class __A( metaclass=a ):
snake_case_ = ['''keras_nlp''']
def __init__( self , *_snake_case , **_snake_case ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''k... | 33 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __A( a ):
... | 33 | 1 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_tor... | 54 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = None ):
'''simple docstring'''
if start is None:
__SCREAMING_SNAKE_CASE = 0
if end is None:
... | 54 | 1 |
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_sentencepiece
@slow # ... | 357 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
i... | 323 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowerCAmelCase__ :Tuple = 1_0_0
lowerCAmelCase__ :Tuple = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__ :int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
... | 329 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ :List[Any] = logging.get_logger(__name__)
lowerCAmelCase__ :Tuple = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __a ( UpperCAmelCase ):
_a : ... | 329 | 1 |
import string
import numpy
def snake_case (__lowercase , __lowercase ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _lowercase )
class lowercase_ :
_lowerCamelCase = string.ascii_uppercase + string.digits
... | 359 | from __future__ import annotations
def snake_case (__lowercase , __lowercase ) -> float:
'''simple docstring'''
_snake_case : Any = sorted(numsa + numsa )
_snake_case ,_snake_case : Any = divmod(len(__lowercase ) , 2 )
if mod... | 284 | 0 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaX... | 187 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCamelCase = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
... | 208 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def _lowerCAmelCase ( lowerCAmelCase = 1000000 , lowerCAmelCase = 10 ):
'''simple docstring'''
UpperCAmelCase = defaultdict(lowercase_ )
for outer_width in ra... | 362 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils impo... | 248 | 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=_A )
class _UpperCAmelCase ( _A ):
# `task` is not a Clas... | 33 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 33 | 1 |
'''simple docstring'''
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
if n == 1 or not isinstance(a__ , a__ ):
return 0
elif n == 2:
return 1
else:
lowercase_ ... | 361 |
'''simple docstring'''
import qiskit
def snake_case_ ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
lowercase_ : List[Any] = qiskit.Aer.get_backend('''aer_simulator''' )
# Cre... | 264 | 0 |
from random import randint, random
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = 5 , ):
'''simple docstring'''
__UpperCamelCase :Optional[int] =... | 43 |
'''simple docstring'''
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 UpperCamelCase__ ( lowercase_ ):
"""simple docst... | 323 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/dec... | 354 |
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = 8.3_1_4_4_5_9_8
def _A (__a , __a ) -> float:
"""simple docstring"""
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
... | 318 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
... | 68 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_snake_case : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_snake_case : list[int] = [ord(letter) for letter in string.ascii_l... | 284 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"facebook/... | 368 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 10_00 ) -> int:
return sum(2 * a * ((a - 1) // 2) for a in range(3, n + 1 ) )
if __name__ == "__main__":
print(solution())
| 101 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def __lowerCamelCase ( A__ ) -> None:
"""simple docstring"""
create_state_space_tree(A__ , [] , 0 )
def __lowerCamelCase ( A__ ... | 28 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_i... | 248 | 0 |
'''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 |
'''simple docstring'''
UpperCamelCase__ : int = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_ ) )
def Upper... | 164 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "htt... | 26 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfor... | 264 | 0 |
'''simple docstring'''
from __future__ import annotations
__lowercase: Any = 10
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : list[int] ) -> list[int]:
'''simple docstring'''
UpperCamelCase__ = 1
UpperCamelCase__ = max(lowercase__ )
while place... | 356 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_... | 31 | 0 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _... | 84 |
'''simple docstring'''
import numpy as np
def lowercase_ ( _lowercase ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowercase_ ( _lowercase ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(_lowercase )
if __nam... | 318 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""shi-labs... | 55 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DO... | 55 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
St... | 82 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ :List[Any] = get_tests_dir("fixtures/tes... | 101 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__A = logging.get_logger(__name__)
class snake_case ( __snake_case ):
def __init__( self : Any , *UpperCamel... | 108 |
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism... | 108 | 1 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# 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_d... | 164 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from... | 164 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 297 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 297 | 1 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Callable ):
@wraps(_SCREAMING_SNAKE_CASE )
def _inner_fn(*_SCREAMING_SNAKE_CASE : List[str] , **_SCREAMING_SNAKE_CASE ... | 27 | '''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_s... | 31 | 0 |
from __future__ import annotations
from statistics import mean
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> list[int]:
lowercase : Optional[Any] = [0] * no_of_processes
lowercase : Tuple = ... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
assert (
isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and number_of_steps > 0
), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
if number_of_steps == 1:
re... | 285 | 1 |
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