code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistep... | 517 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( a__) -> Union[str, Any]:
"""simple docstring"""
def decorator(a__):
_snake_case : Tuple = getattr(a__ , 'handle_key' , [])
h... | 517 | 1 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.defaul... | 138 | import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import Sta... | 138 | 1 |
'''simple docstring'''
from math import sqrt
def UpperCamelCase__ ( __magic_name__ : int ) -> int:
'''simple docstring'''
snake_case__ : Dict = 0
for i in range(1 , int(sqrt(__magic_name__ ) + 1 ) ):
if n % i == 0 and i != sqrt(__magi... | 38 |
'''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 trans... | 41 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int , _snake_case : int ):
if exponent == 1:
return base
if exponent % 2 == 0:
lowerCAmelCase : Dict = _modexpt(_snake_case , exponent // 2 , _snake... | 637 |
"""simple docstring"""
class snake_case_:
def __init__( self : Union[str, Any] , UpperCamelCase_ : str ):
lowerCAmelCase : Dict = val
lowerCAmelCase : str = None
lowerCAmelCase : Dict = None
def ... | 637 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_fl... | 533 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : int = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
rais... | 533 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 710 |
import heapq
import sys
import numpy as np
_lowerCAmelCase : str = tuple[int, int]
class __snake_case :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ = set()
def SCREAMING_SNAKE_CASE_ ( ... | 604 | 0 |
"""simple docstring"""
def snake_case_ ( A_ : Optional[Any], A_ : Tuple, A_ : Dict ):
'''simple docstring'''
if len(__lowercase ) != len(__lowercase ):
raise ValueError('''The length of profit and weight must be same.''' )
if... | 83 |
"""simple docstring"""
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'nielsr/canine-s': 2_048,
}
# Unicode define... | 357 | 0 |
"""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_u... | 706 |
"""simple docstring"""
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If us... | 468 | 0 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
UpperCAmelCase = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh'... | 84 |
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,
PNDMSched... | 272 | 0 |
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_availabl... | 710 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
fro... | 458 | 0 |
def _snake_case ( __snake_case = 10**12 ):
_UpperCamelCase = 1
_UpperCamelCase = 0
_UpperCamelCase = 1
_UpperCamelCase = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_num... | 10 |
'''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_pr... | 207 | 0 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
A_ :List[Any] = logging.get_logger(__name__)
def A ( a_ ,a_ ) -> Optional[in... | 154 |
def A ( a_ = 600_851_475_143 ) -> int:
try:
__UpperCamelCase : int =int(a_ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must b... | 154 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
... | 116 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
f... | 116 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...util... | 718 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __A(lowerCAmelCase ) -> Any:
"""simple docstring"""
_UpperCamelCase = [
"""decoder.version""",
"""decoder.output_projection.weight""",... | 202 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from ... | 79 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class UpperCAmelCase_ :
def __init__( self , _lowerCAmelCase ):
UpperCAmelCase__ : Any = data
UpperCAmelCase__ : List[Any] ... | 79 | 1 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _lowercase ( unittest.TestCase ):
def _UpperCamelCase ( self ) -> None:
lowerCame... | 133 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
_A = 'us-east-1' # defaults region
@dataclass
class _lowercase :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'task_name... | 133 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataC... | 74 |
'''simple docstring'''
from __future__ import annotations
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : List[Any] = len(lowerCAmelCase_ )
# We need to create solution object to save path.
_UpperCAmelCase : Optional[int] = [[0 for _ in... | 414 | 0 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
snake_case__ : Optional[int] = collections.namedtuple("""_Datasets""... | 655 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowerc... | 655 | 1 |
def _SCREAMING_SNAKE_CASE ( lowercase : dict ):
'''simple docstring'''
lowerCamelCase_ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowerCamelCase_ = set()
return any(
... | 70 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 | 0 |
def lowerCamelCase__ ( A__ : str , A__ : str ):
'''simple docstring'''
__lowerCamelCase = len(A__ ) + 1
__lowerCamelCase = len(A__ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_strin... | 80 |
class lowerCamelCase__: # Public class to implement a graph
def __init__( self: Dict , UpperCamelCase_: int , UpperCamelCase_: int , UpperCamelCase_: list[list[bool]] ):
__lowerCamelCase = row
__lowerCamelCase = col
__lo... | 80 | 1 |
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
snake_case = logging.get_logger(__name__)
snake_case = {
... | 67 |
'''simple docstring'''
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_axis_d... | 405 | 0 |
"""simple docstring"""
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lz... | 701 |
"""simple docstring"""
import operator as op
def _lowerCAmelCase ( lowerCamelCase__ : Tuple ) -> List[str]:
_SCREAMING_SNAKE_CASE : Optional[int] = []
_SCREAMING_SNAKE_CASE : str = lambda lowerCamelCase__, lowerCamelCase__ : int(x / ... | 295 | 0 |
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 snake_case__ :
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 42... | 478 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 122 | 0 |
'''simple docstring'''
from typing import Any
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
if not input_list:
return []
lowerCAmelCase__ : List[str] = [input_list.count(UpperCamelCase ) for value in input_list]
lowerC... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCAmelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapanes... | 160 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 505 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__(self , _... | 60 | 0 |
'''simple docstring'''
from math import pi, sqrt, tan
def a_ ( lowerCamelCase : float ):
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def a_ ( lowerCamelCas... | 700 |
'''simple docstring'''
import math
def a_ ( lowerCamelCase : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not prim... | 513 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class lowerCamelCase_ :
... | 17 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase: List[Any] = logging.get_logger(__name__)
__UpperCamelCase: Dict = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""studio-ousi... | 266 | 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 t... | 710 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def low... | 95 | 0 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
_lowerCamelCase ={
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """attention.self""",
"""self.proj""... | 681 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def __init__( self : List[A... | 61 | 0 |
import argparse
import datetime
def a_ ( _A ) -> str:
"""simple docstring"""
snake_case__ = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
... | 372 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __SCREAMING_SNAKE_CASE:
def __init__( self: int , UpperCamelCase: Any ) -> List[Any]:
snake_case__ = data
snake_case__ ... | 372 | 1 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class A__ ( lowercase_):
"""simple docstring"""
snake_case__ : Tuple =['''image_processor''', '''tokenizer''']
snake_case__ ... | 222 |
'''simple docstring'''
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import T... | 379 | 0 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_comm... | 713 |
"""simple docstring"""
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,
... | 374 | 0 |
'''simple docstring'''
UpperCAmelCase = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD,... | 119 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_seq... | 436 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlipConfig""... | 702 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""camembert-base""": """https://huggingface.co/camembert... | 488 | 0 |
from __future__ import annotations
from cmath import sqrt
def _a ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
if a == 0:
raise ValueError("""Coefficient 'a' must not be zero.""" )
lowerCamelCase : str = b * b - 4 * a * c
lowerCamelCase : str ... | 681 |
import pytest
_lowerCamelCase ="""__dummy_dataset1__"""
_lowerCamelCase ="""
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn... | 681 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
UpperCamelCase = {
'sample_size': 32,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 2,
'num_class_embeds': ... | 125 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase = logging.getLogger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple... | 125 | 1 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not... | 521 |
'''simple docstring'''
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomR... | 466 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
class _A ( ... | 175 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
a ... | 175 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class A_ :
def __init__( self: Tuple ,__lowerCAmelCase: int = 6 ):
'''simple docstring'''
_lowerCamelCase : Node | None = None
_lowerCamelCase ... | 46 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf... | 331 | 0 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversa... | 172 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def lowerCAmelCase__ ( UpperCAmelCase ):
"""simple docstring"""
snake_case__ : typing.Counter[int] = Counter()
for base ... | 172 | 1 |
from math import factorial
def __SCREAMING_SNAKE_CASE ( a__ : int = 100 ) -> int:
return sum(int(a__ ) for x in str(factorial(a__ ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 17 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common ... | 325 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__lowerCAmelCase : Any = logging.get_logger(__name__)
class A ( UpperCAmelCase__ ):
def __init__( self ... | 710 | '''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCAmelCase ( UpperCamelCase__ : str = "AAPL" ):
"""simple docstring"""
__UpperCAmelCase = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__UpperCAmelCase = Beautif... | 654 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cas... | 2 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
class __lowerCamelCase ( __lowercase ):
def __init__(self , ... | 156 | 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
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {... | 720 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase_ = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def __magic_name__ ( ... | 86 | 0 |
import gc
import threading
import time
import psutil
import torch
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = psutil.Process()
SCREAMING_... | 25 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
Autoenco... | 102 | 0 |
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 = {
'''distilbert-base-uncased''': '''https://hu... | 455 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_M... | 455 | 1 |
def a_ ( __magic_name__ , __magic_name__ ) -> Dict:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 598 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a : Dict = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMT... | 447 | 0 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test... | 520 |
from __future__ import annotations
from math import gcd
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase = 2 , _lowerCAmelCase = 1 , _lowerCAmelCase = 3 , ) -> int | None:
"""simple docstring"""
if num < 2:
raise ValueError("""The input value cannot b... | 520 | 1 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def UpperCamelCase__ ( __magic_name__ : str , __magic_name__ : List[Any]=None ) -> Union[str, Any]:
''... | 38 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import ca... | 532 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a= {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
a= _LazyModule(__name__, globals()['''__file_... | 701 | '''simple docstring'''
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 PreTrainedTokenizer
from ...utils import logging
a= logging.get_logger(__name__)
a= '''▁'''
a= {
... | 287 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a... | 76 | """simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> np.ndarray:
_SCREAMING_SNAKE_CASE : List[Any... | 338 | 0 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = '▁'
lowercase_ ... | 710 |
"""simple docstring"""
lowercase_ = '\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'
lowercase_ ... | 215 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
i... | 567 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase : Optional[Any] = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig... | 567 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__lowercase = ['''small''', '''medium''', '''large''']
__lowercase = '''lm_head.decoder.weight'''
__lowercase = '''lm_head.weight'''
def snake_case__ ... | 605 | '''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def snake_case__ ( _A: np.ndarray , _A: np.ndarray , _A: np.ndarray , _A: int , _A: int ) -> np.ndarray:
'''simple do... | 605 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _lowercase ( __lowerCAmelCase = "https://www.worldometers.info/coronavirus" ) -> dict:
SCREAMING_SNAKE_CASE__ : Any = BeautifulSoup(requests.get(_A ).text , """html.parser""" )
S... | 680 |
def UpperCamelCase ( _A : int )-> int:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() ... | 491 | 0 |
import math
def _UpperCAmelCase ( __lowerCamelCase : int ) -> list[int]:
_snake_case = []
_snake_case = 2
_snake_case = int(math.sqrt(lowerCAmelCase__ ) ) # Size of every segment
_snake_case = [True] * (end + 1)
_snake_case = []
while start... | 721 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : float , __lowerCamelCase : float ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"{price_plus_tax(100, 0.25) = }")
print(F"{price_plus_tax(1_25.50, 0.05) = }")
| 430 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : Dict = 10 , lowercase : str = 22 ) -> int:
"""simple docstring"""
snake_case : Tuple = range(1 , lowercase )
snake_case : Tuple = range(1 , lowercase )
ret... | 178 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_ava... | 678 | 0 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = [0] * len(SCREAMING_SNAKE_CASE_ )
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
# use last results for better performance - dynamic programming
lowercase__ = prefix_result[i - 1]
while j > 0 an... | 714 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .toke... | 37 | 0 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _P... | 154 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''xlm-mlm-en-2048''': '''https://huggingfa... | 154 | 1 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ):
... | 368 |
'''simple docstring'''
import functools
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = len(SCREAMING_SNAKE_CASE__ )
_snake_case = len(SCREAMING_SNAKE_CASE__ )
@functools.cache
d... | 368 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_SCREAMING_SNAKE_CASE : str = "\nimport os\n"
_SCREAMING_SNAKE_CASE : List[Any] = "\ndef foo():\n import os\n return False\n"
_SCREAMING_SNAKE_CASE : str = ... | 550 |
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
_SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
_SCR... | 550 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
UpperCamelCase_ = ['''image_processor''', '''tokenizer''']
UpperCamelCa... | 8 |
'''simple docstring'''
def lowercase_ ( __A : float , __A : int ) -> float:
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A ) , __A )
return number - int(__A )
if __name__ == "__main__":
print(decimal_i... | 8 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__A = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__A = _LazyModule(__name__... | 484 | def snake_case__ ( lowercase , lowercase ):
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testm... | 613 | 0 |
'''simple docstring'''
import math
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Any = [True] * n
UpperCAmelCase : Optional[Any] = False
UpperCAmelCase : Tuple = False
UpperC... | 716 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : str =... | 609 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
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 impor... | 98 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoCon... | 275 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', '... | 340 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : list ):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
a... | 340 | 1 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _a ( unittest... | 86 |
"""simple docstring"""
import flax.linen as nn
import jax
import jax.numpy as jnp
class UpperCamelCase_ ( nn.Module ):
_A : int
_A : jnp.dtype = jnp.floataa
def UpperCamelCase_ ( self ) -> Dict:
"""simple docstring""... | 673 | 0 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[Any]... | 720 |
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=UpperCAmelCase_ ):
__lowerCAmelCase : Any = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_):
'''simple docstring'''
requires_bac... | 495 | 0 |
'''simple docstring'''
import numpy as np
__snake_case : Optional[Any] = [
["a", "b", "c", "d", "e"],
["f", "g", "h", "i", "k"],
["l", "m", "n", "o", "p"],
["q", "r", "s", "t", "u"],
["v", "w", "x", "y", "z"],
]
class A :
def __init__( self ) -> N... | 131 |
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ : int | str ):
_a = str(lowerCamelCase__ )
return n == n[::-1]
def _lowercase ( lowerCamelCase__ : int = 1_000_000 ):
_a = 0
for i i... | 131 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}... | 705 |
"""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 ( snake_case :... | 439 | 0 |
import itertools
import string
from collections.abc import Generator, Iterable
def _snake_case ( lowerCAmelCase : Iterable[str] , lowerCAmelCase : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = iter(lowerCAmelCase )
while True:
SCREAMI... | 216 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Optional[Any] = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not is_torch_avail... | 216 | 1 |
'''simple docstring'''
import os
def _lowerCAmelCase ( ):
with open(os.path.dirname(lowerCamelCase_ ) + '''/p022_names.txt''' ) as file:
__lowercase = str(file.readlines()[0] )
__lowercase = names.replace('''"''' , ''... | 56 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
i... | 56 | 1 |
def lowerCAmelCase_ ( _snake_case : int ) -> bool:
'''simple docstring'''
return str(_snake_case ) == str(_snake_case )[::-1]
def lowerCAmelCase_ ( _snake_case : int ) -> int:
'''simple docstring'''
return int(_snake_case ) + int(str(_snake_cas... | 124 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_te... | 124 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
_SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
def __a():
'''simple docstring'''
_lowerCAmelCase = argparse.ArgumentParser(
... | 489 |
'''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,
)
_SCREAMING_SNAKE_CASE = {"configuration... | 489 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.... | 108 | import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCAmelCase ( )-> List[Any]:
"""simple docstring"""
... | 604 | 0 |
from collections.abc import Callable
import numpy as np
def __lowerCamelCase ( __a : Callable , __a : float , __a : float , __a : float , __a : float ) -> np.ndarray:
_lowercase =int(np.ceil((x_end - xa) / step_size ) )
_... | 709 | 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_manager import xopen
from ..table import ar... | 594 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a_ = 6_378_137.0
a_ = 6_356_752.314_245
a_ = 6378137
def __lowerCAmelCase ( A_ : float , A_ : float , A_ : float , A_ : float ) -> float:... | 221 | import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
a_ = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
print("""Googling... | 221 | 1 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
A_ : Tuple = logging.get_logger(__name__)
def __UpperCamelCase ( a, a) ->Any:
lowerCamelCase__ = nn.functional.normaliz... | 701 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A_ = {"tokenization_byt5": ["ByT5Tokenizer"]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure,... | 360 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
lowercase__ = logging.ge... | 638 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
fro... | 638 | 1 |
'''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowercase__ : str = 2_99_79_24_58
# Symbols
lowercase__ : Dict = symbols('''ct x y z''')
def _lowerCAmelCase ( ... | 710 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : Any = {
'''microsoft/git-base'... | 338 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_visi... | 4 | def lowerCAmelCase_ ( __A ) -> list:
'''simple docstring'''
for i in range(len(__A ) - 1, 0, -1 ):
UpperCAmelCase__ = False
for j in range(__A, 0, -1 ):
if unsorted[j] < unsorted[j - 1]:
... | 486 | 0 |
"""simple docstring"""
UpperCAmelCase : str = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase : List[Any] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase : Union[str, Any] = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
... | 721 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> None:
'''simple docstring'''
lowercase_ , lowercase_ = ana... | 100 | 0 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB... | 279 |
# 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 req... | 279 | 1 |
def A ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interes... | 704 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_ear... | 561 | 0 |
'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
UpperCamelCase__ : int = logging.get_logger(__name__)
UpperCamelCase__ : Optional[Any] = '... | 578 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
... | 578 | 1 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] ):
'''simple docstring'''
print("""\nThe shortest path matrix using Floyd Warshall algorithm\n""" )
for i in range(__UpperCamelCase ... | 21 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
snake_case_ ... | 21 | 1 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 311 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_con... | 647 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 213 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__lowerCamelCase = {'vocab_file': 'vocab.txt', 'to... | 213 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizati... | 518 |
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : int = hex_num.strip()
if not hex_num:
raise ValueError("No value was passed to the function" )
lowerCamelCase : str = hex_num[0] == "-"
if is_negative... | 340 | 0 |
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = len(snake_case__ )
snake_case_ = [[0] * n for i in range(snake_case__ )]
for i in range(snake_case__ ):
snake_case_ = y_points[i]
for i in range(2 , snake_case__ ):
for j in r... | 700 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , low... | 46 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[str]:
'''simple docstring'''
snake_case : Union[str, Any] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
... | 638 |
'''simple docstring'''
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
lowercase__ = logging.get_logger(__name__)
lowercase__ ... | 638 | 1 |
'''simple docstring'''
def snake_case_ ( a__ : int = 10_00 ):
"""simple docstring"""
return sum(e for e in range(3 ,a__ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 163 |
'''simple docstring'''
import pprint
import requests
A : str = """https://zenquotes.io/api"""
def snake_case_ ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def snake_case_ ( ):
... | 163 | 1 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is... | 61 |
'''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_pr... | 116 | 0 |
"""simple docstring"""
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
__snake_case = logging.get_logger(__name__)
__snake_case = ... | 117 |
"""simple docstring"""
import argparse
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_dummies.py
__snake_case = """src/diffusers"""
# Matches is_xxx_available()
__snake_case = re.compile(... | 117 | 1 |
'''simple docstring'''
import sys
_UpperCAmelCase : Optional[int] = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290... | 107 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
a =logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' ... | 652 | 0 |
'''simple docstring'''
from typing import Any
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any ... | 508 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = [0] * len(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : ... | 508 | 1 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
for i in range(len(_lowercase ) - 1 , 0 , -1 ):
lowerCAmelCase = False
for j in range(_lowercase , 0 , -1 ):
... | 532 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( _lowercase ) -> list[int]:
'''simple docstring'''
lowerCamelCase_ : str = [True] * limit
lowerCamelCase_ : List[str] = False
lowerCamelCase_ : List[Any] = False
lowerCam... | 422 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import gcd
def __A ( lowerCAmelCase_ , lowerCAmelCase_ = 2 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
... | 711 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCAmelCase_ : List[Any] = {
'''fac... | 156 | 0 |
UpperCAmelCase_ = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6: "Saturday",
}
def A__ ( S... | 32 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
snake_case__ : Tuple = pd.read_csv('''sample_data.csv''', header=None)
... | 392 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise... | 406 |
"""simple docstring"""
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split(... | 406 | 1 |
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