code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
SCREAMING_SNAKE_CASE_: str =logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__(self : Optional[Any] , *__a : int ... | 1 |
"""simple docstring"""
def A__ ( UpperCamelCase ):
A = generate_pascal_triangle(UpperCamelCase )
for row_idx in range(UpperCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " ... | 292 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __lowerCAmelCase (lowercase_ , unittest... | 2 |
"""simple docstring"""
import math
import sys
def A__ ( UpperCamelCase ):
A = ""
try:
with open(UpperCamelCase , "rb" ) as binary_file:
A = binary_file.read()
for dat in data:
A = F"{dat:08b}... | 292 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : List[Any] = 0
A : Any = len(snake_case__ )
for i in range(n - 1 ):
for j in range(i + 1 , snake_case__ ):
... | 3 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self :List[str] , __UpperCamelCase :Union[str, Any] , __UpperCamelCase :Tuple ):
A = name
A = val
def __str__( self :str ):
return f"{self.__class__.__nam... | 292 | 0 |
'''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_sequence_... | 4 |
"""simple docstring"""
from __future__ import annotations
_snake_case : str = []
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
for i in range(len(UpperCamelCase ) ):
if board[row][i] == 1:
retur... | 292 | 0 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'''The converted tokenizer will b... | 5 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 292 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A : List[Any] = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'Perceiv... | 6 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 292 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> str:
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "_... | 7 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A__ ( UpperCamelCase = "laptop" ):
A = F"https://www.amazon.in/laptop/s?k={product}"
A = {
"User-Agent": "Mozilla/5.0 (X11; Li... | 292 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_torch_available():
raise... | 8 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler... | 292 | 0 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : List[str] = {}
__SCREAMING_SNAKE_CASE : Optional[Any] = job['''started_at''']
__SCR... | 9 |
"""simple docstring"""
_snake_case : Optional[int] = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager impor... | 292 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCAmelCase_ ( ) -> str:
"""simple docstring"""
lowerCamelCase__: Optional[int] =9
lowerCamelCase__: Union[str, Any] =[
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
... | 10 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A__ ( UpperCamelCase ):
A = [
"encoder.version",
"decoder.version",
"model.encoder.version"... | 292 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from tra... | 11 |
"""simple docstring"""
from math import isqrt, loga
def A__ ( UpperCamelCase ):
A = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase , UpperC... | 292 | 0 |
import argparse
import json
import os
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 A... | 12 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case : Union[str, Any] = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig... | 292 | 0 |
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 import Regre... | 13 |
"""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_ax... | 292 | 0 |
from __future__ import annotations
from fractions import Fraction
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> bool:
"""simple docstring"""
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
... | 14 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to... | 292 | 0 |
import math
def UpperCAmelCase ( a_ , a_ = 0 , a_ = 0 ) -> list:
"""simple docstring"""
__A = end or len(a_ )
for i in range(a_ , a_ ):
__A = i
__A = array[i]
while temp_index != start and temp_index_value < array[temp_inde... | 15 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _UpperCAmelCase (... | 292 | 0 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __UpperCAmelCase ( __lowerCamelCase ) -> List[str]:
lowercase__ : Optional[int] = FileLock(str(tmpdir / '''foo.lock''' ) )
low... | 16 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _UpperCAmelCase :
UpperCamelCase = None
def lowerCamelCase ( self :List[Any] ):
A = self.feature_extraction_... | 292 | 0 |
"""simple docstring"""
def _A ( UpperCamelCase_ : list[int]) -> int:
'''simple docstring'''
if not numbers:
return 0
if not isinstance(UpperCamelCase_, (list, tuple)) or not all(
isinstance(UpperCamelCase_, UpperCamelCase_) for number in numbers):
raise Value... | 17 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _UpperCAmelCase (... | 292 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class a__ ( A__ ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON seri... | 18 |
"""simple docstring"""
def A__ ( UpperCamelCase , UpperCamelCase = False ):
if not isinstance(UpperCamelCase , UpperCamelCase ):
A = F"Expected string as input, found {type(UpperCamelCase )}"
raise ValueError(UpperCamelCase )
... | 292 | 0 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def lowerCamelCase_ ( lowerCamelCase__ ):
return (data["data"], data["targ... | 19 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 292 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> list[list]:
lowercase : Dict = current_set.copy()
for row_index, row in enumerate(SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = row[0]
for column_index, column in enumerate... | 20 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
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 Co... | 292 | 0 |
from __future__ import annotations
from math import ceil, floor, sqrt
def UpperCamelCase_( lowerCamelCase_ = 200_0000 ) -> int:
_lowercase : list[int] = [0]
_lowercase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle... | 21 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Optional[int] = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vi... | 292 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE :str = {
'''configuration_roformer''... | 22 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import flo... | 292 | 0 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ..... | 23 |
"""simple docstring"""
def A__ ( UpperCamelCase ):
A = generate_pascal_triangle(UpperCamelCase )
for row_idx in range(UpperCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " ... | 292 | 0 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CL... | 24 |
"""simple docstring"""
import math
import sys
def A__ ( UpperCamelCase ):
A = ""
try:
with open(UpperCamelCase , "rb" ) as binary_file:
A = binary_file.read()
for dat in data:
A = F"{dat:08b}... | 292 | 0 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase__ : str = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-j... | 25 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self :List[str] , __UpperCamelCase :Union[str, Any] , __UpperCamelCase :Tuple ):
A = name
A = val
def __str__( self :str ):
return f"{self.__class__.__nam... | 292 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.se... | 26 |
"""simple docstring"""
from __future__ import annotations
_snake_case : str = []
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
for i in range(len(UpperCamelCase ) ):
if board[row][i] == 1:
retur... | 292 | 0 |
'''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, RandomResizedCrop, Resize, ToTensor
fro... | 27 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 292 | 0 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowerCamelCase ( A__ ) -> str:
"""simple docstring"""
if not isinstance(A__ , A__ ):
raise TypeError('Undefined for non-integers' ... | 28 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 292 | 0 |
def lowercase__ ( __snake_case : str , __snake_case : str = " " ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = []
UpperCAmelCase_ : Optional[Any] = 0
for index, char in enumera... | 29 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A__ ( UpperCamelCase = "laptop" ):
A = F"https://www.amazon.in/laptop/s?k={product}"
A = {
"User-Agent": "Mozilla/5.0 (X11; Li... | 292 | 0 |
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 lowercase__( unittest.TestCase ):
"""simple docstring"""
def _l... | 30 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler... | 292 | 0 |
'''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import dis... | 31 |
"""simple docstring"""
_snake_case : Optional[int] = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager impor... | 292 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable... | 32 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A__ ( UpperCamelCase ):
A = [
"encoder.version",
"decoder.version",
"model.encoder.version"... | 292 | 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_i... | 33 |
"""simple docstring"""
from math import isqrt, loga
def A__ ( UpperCamelCase ):
A = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase , UpperC... | 292 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers i... | 34 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case : Union[str, Any] = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig... | 292 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"vocab_file": "vocab.json",
"merges_file... | 35 |
"""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_ax... | 292 | 0 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
c... | 36 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to... | 292 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from... | 37 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _UpperCAmelCase (... | 292 | 0 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as... | 38 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _UpperCAmelCase :
UpperCamelCase = None
def lowerCamelCase ( self :List[Any] ):
A = self.feature_extraction_... | 292 | 0 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = val
_UpperCAmelCase = None
_UpperCAmelCase = None
def ... | 39 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _UpperCAmelCase (... | 292 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 40 |
"""simple docstring"""
def A__ ( UpperCamelCase , UpperCamelCase = False ):
if not isinstance(UpperCamelCase , UpperCamelCase ):
A = F"Expected string as input, found {type(UpperCamelCase )}"
raise ValueError(UpperCamelCase )
... | 292 | 0 |
'''simple docstring'''
import math
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> list[int]:
lowerCamelCase__ : Tuple = []
lowerCamelCase__ : int = 2
lowerCamelCase__ : str = int(... | 41 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 292 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTes... | 42 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
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 Co... | 292 | 0 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_validate_point(SCREAMING_SNAKE_CASE )
_validate_point(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) != len(SCREAMING_SNAKE_CASE ):
raise ValueError('''Both points must be in the same n... | 43 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Optional[int] = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vi... | 292 | 0 |
"""simple docstring"""
import numpy as np
class __A :
def __init__( self ):
_lowerCAmelCase : Optional[Any] = (0, 0)
_lowerCAmelCase : Optional[Any] = None
_lowerCAmelCase : int = 0
_lowerCAmelCase : ... | 44 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import flo... | 292 | 0 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def lowercase ( lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : tuple[int, int] , lowerCAmelCase__ : tuple[int, int] , lowerCAmelCase__ : bool , ) -> tuple... | 45 |
"""simple docstring"""
def A__ ( UpperCamelCase ):
A = generate_pascal_triangle(UpperCamelCase )
for row_idx in range(UpperCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " ... | 292 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_visio... | 46 |
"""simple docstring"""
import math
import sys
def A__ ( UpperCamelCase ):
A = ""
try:
with open(UpperCamelCase , "rb" ) as binary_file:
A = binary_file.read()
for dat in data:
A = F"{dat:08b}... | 292 | 0 |
'''simple docstring'''
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 47 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self :List[str] , __UpperCamelCase :Union[str, Any] , __UpperCamelCase :Tuple ):
A = name
A = val
def __str__( self :str ):
return f"{self.__class__.__nam... | 292 | 0 |
from ..utils import DummyObject, requires_backends
class UpperCamelCase__ (metaclass=lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Tuple = ["""onnx"""]
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ... | 48 |
"""simple docstring"""
from __future__ import annotations
_snake_case : str = []
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
for i in range(len(UpperCamelCase ) ):
if board[row][i] == 1:
retur... | 292 | 0 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = len(_UpperCAmelCase )
__a = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for... | 49 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 292 | 0 |
from math import pow
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we ha... | 50 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 292 | 0 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.s... | 51 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A__ ( UpperCamelCase = "laptop" ):
A = F"https://www.amazon.in/laptop/s?k={product}"
A = {
"User-Agent": "Mozilla/5.0 (X11; Li... | 292 | 0 |
def A_ ( _lowerCAmelCase ) -> Tuple:
UpperCamelCase : Dict = 0
UpperCamelCase : Any = len(_lowerCAmelCase )
for i in range(n - 1 ):
for j in range(i + 1 , _lowerCAmelCase ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions... | 52 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler... | 292 | 0 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
a__ : List[str] ='''naver-clova-ix/donut-base'''
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def _lowerCamelCase ( self : Dict ):
__Uppe... | 53 |
"""simple docstring"""
_snake_case : Optional[int] = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager impor... | 292 | 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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTI... | 54 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A__ ( UpperCamelCase ):
A = [
"encoder.version",
"decoder.version",
"model.encoder.version"... | 292 | 0 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # ... | 55 |
"""simple docstring"""
from math import isqrt, loga
def A__ ( UpperCamelCase ):
A = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase , UpperC... | 292 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a : Union[str, Any] = loggi... | 56 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case : Union[str, Any] = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig... | 292 | 0 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _UpperCamelCase ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , __a ):
... | 57 |
"""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_ax... | 292 | 0 |
'''simple docstring'''
import re
def lowerCamelCase ( __lowerCamelCase : str ) ->bool:
_SCREAMING_SNAKE_CASE = re.compile(R"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$""" )
if match := re.search(__lowerCamelCase , __lowerCamelCase ):
return match.strin... | 58 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to... | 292 | 0 |
def UpperCamelCase ( __lowerCamelCase : int ):
snake_case : Optional[Any] = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def UpperCamelCase ( __lowerCamelCase : int = 5000 ):
snake_case : Optional[Any] = [(i *... | 59 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _UpperCAmelCase (... | 292 | 0 |
"""simple docstring"""
import numpy as np
import datasets
snake_case__ : Dict = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
... | 60 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _UpperCAmelCase :
UpperCamelCase = None
def lowerCamelCase ( self :List[Any] ):
A = self.feature_extraction_... | 292 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __a ( __lowerCamelCase ):
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = analyze_text(__lowerCamelCase )
UpperCAmelCase_ :... | 61 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _UpperCAmelCase (... | 292 | 0 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_A = get_tests_dir('fixtures/spiece.model')
@re... | 62 |
"""simple docstring"""
def A__ ( UpperCamelCase , UpperCamelCase = False ):
if not isinstance(UpperCamelCase , UpperCamelCase ):
A = F"Expected string as input, found {type(UpperCamelCase )}"
raise ValueError(UpperCamelCase )
... | 292 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ : int = logging.get_logger(__name__)
lowerCAmelCase_ : str = ... | 63 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 292 | 0 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_ext... | 64 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
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 Co... | 292 | 0 |
def lowerCAmelCase_ ( __A, __A, __A, __A ) -> int:
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ = len(__A ), len(grid[0] )
if (
min(__A, __A ) < 0
or row == row_length
or col == ... | 65 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Optional[int] = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vi... | 292 | 0 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( _lowercase = 5000 ):
'''simple docstring'''
snake_case_ :Any = [(i * (3 * i - 1)) // 2 for i... | 66 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import flo... | 292 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ ) -> int:
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
... | 67 |
"""simple docstring"""
def A__ ( UpperCamelCase ):
A = generate_pascal_triangle(UpperCamelCase )
for row_idx in range(UpperCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " ... | 292 | 0 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCAmelCase__ = get_tests_dir(... | 68 |
"""simple docstring"""
import math
import sys
def A__ ( UpperCamelCase ):
A = ""
try:
with open(UpperCamelCase , "rb" ) as binary_file:
A = binary_file.read()
for dat in data:
A = F"{dat:08b}... | 292 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import Mvp... | 69 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self :List[str] , __UpperCamelCase :Union[str, Any] , __UpperCamelCase :Tuple ):
A = name
A = val
def __str__( self :str ):
return f"{self.__class__.__nam... | 292 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transf... | 70 |
"""simple docstring"""
from __future__ import annotations
_snake_case : str = []
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
for i in range(len(UpperCamelCase ) ):
if board[row][i] == 1:
retur... | 292 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import tr... | 71 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 292 | 0 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def snake_case_ ( A_ : np.ndarray, A_ : tuple[int, int], A_ : tuple[int, int], A_ : bool, ):
'''simple docstring'''
_lowerCamelCase , _lowerCamelCase ... | 72 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 292 | 0 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
a ="""\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
... | 73 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A__ ( UpperCamelCase = "laptop" ):
A = F"https://www.amazon.in/laptop/s?k={product}"
A = {
"User-Agent": "Mozilla/5.0 (X11; Li... | 292 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
_lowercase = logging.get_logg... | 74 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler... | 292 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : Union[str, Any] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAIN... | 75 |
"""simple docstring"""
_snake_case : Optional[int] = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager impor... | 292 | 0 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__... | 76 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A__ ( UpperCamelCase ):
A = [
"encoder.version",
"decoder.version",
"model.encoder.version"... | 292 | 0 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
_UpperCamelCase : Optional[Any] = "examples/"
_UpperCamelCase : Tuple = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
... | 77 |
"""simple docstring"""
from math import isqrt, loga
def A__ ( UpperCamelCase ):
A = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase , UpperC... | 292 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ = {"""configuration_reformer""": ["""REFORMER_PR... | 78 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case : Union[str, Any] = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig... | 292 | 0 |
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCamelCase_ = '''.'''
# ... | 79 |
"""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_ax... | 292 | 0 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a__ : Optional[int] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a__ : int = None
def _UpperCamelCase ( ) -> Dict... | 80 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to... | 292 | 0 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowerCamelCase_ : Optional[Any] = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
... | 81 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _UpperCAmelCase (... | 292 | 0 |
import argparse
import json
import os
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 Ac... | 82 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _UpperCAmelCase :
UpperCamelCase = None
def lowerCamelCase ( self :List[Any] ):
A = self.feature_extraction_... | 292 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
snake_case_ : ... | 83 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _UpperCAmelCase (... | 292 | 0 |
"""simple docstring"""
import sys
def _snake_case ( lowercase__ : Tuple ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :str = len(lowercase__ )
lowerCAmelCase_ :Dict = [[0 for x in range(lowercase__ )] for x in range(lowe... | 84 |
"""simple docstring"""
def A__ ( UpperCamelCase , UpperCamelCase = False ):
if not isinstance(UpperCamelCase , UpperCamelCase ):
A = F"Expected string as input, found {type(UpperCamelCase )}"
raise ValueError(UpperCamelCase )
... | 292 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_SCREAMING_SNAKE_CASE : str = {"processing_layou... | 85 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 292 | 0 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_avail... | 86 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
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 Co... | 292 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowercase_ ( _lowerCamelCase : str = ""):
lowercase__ : str = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
lowercase__ : int = BeautifulSoup(reques... | 87 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Optional[int] = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vi... | 292 | 0 |
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = abs(A_ )
__magic_name__ = 0
while n > 0:
res += n % 10
n //= 10
return res
def a__ ( A_ ):
'''simple docstring'''
__magic_name... | 88 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import flo... | 292 | 0 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ ) -> bool:
_a : List[str] = 0
for ch in input_str:
_a : Optional[Any] = ord(lowerCAmelCase_ )
_a : Tuple = pow(2 , lowerCAmelCase_ )
# If w... | 89 |
"""simple docstring"""
def A__ ( UpperCamelCase ):
A = generate_pascal_triangle(UpperCamelCase )
for row_idx in range(UpperCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " ... | 292 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_... | 90 |
"""simple docstring"""
import math
import sys
def A__ ( UpperCamelCase ):
A = ""
try:
with open(UpperCamelCase , "rb" ) as binary_file:
A = binary_file.read()
for dat in data:
A = F"{dat:08b}... | 292 | 0 |
"""simple docstring"""
from math import isqrt, loga
def _A (__a ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i... | 91 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self :List[str] , __UpperCamelCase :Union[str, Any] , __UpperCamelCase :Tuple ):
A = name
A = val
def __str__( self :str ):
return f"{self.__class__.__nam... | 292 | 0 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_h... | 92 |
"""simple docstring"""
from __future__ import annotations
_snake_case : str = []
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
for i in range(len(UpperCamelCase ) ):
if board[row][i] == 1:
retur... | 292 | 0 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def snake_case_ ( __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ):
... | 93 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 292 | 0 |
snake_case : Dict = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/tran... | 94 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 292 | 0 |
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be posi... | 95 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A__ ( UpperCamelCase = "laptop" ):
A = F"https://www.amazon.in/laptop/s?k={product}"
A = {
"User-Agent": "Mozilla/5.0 (X11; Li... | 292 | 0 |
"""simple docstring"""
lowercase__ = range(2, 20 + 1)
lowercase__ = [10**k for k in range(ks[-1] + 1)]
lowercase__ = {}
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
... | 96 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler... | 292 | 0 |
'''simple docstring'''
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Any = 1
UpperCamelCase__ :Optional[int] = 2
while i * i <= n:
UpperCamelCase__ :Optional[Any] = 0
while n % i == 0:
... | 97 |
"""simple docstring"""
_snake_case : Optional[int] = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager impor... | 292 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
... | 98 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A__ ( UpperCamelCase ):
A = [
"encoder.version",
"decoder.version",
"model.encoder.version"... | 292 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils im... | 99 |
"""simple docstring"""
from math import isqrt, loga
def A__ ( UpperCamelCase ):
A = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase , UpperC... | 292 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://huggin... | 100 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case : Union[str, Any] = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig... | 292 | 0 |
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