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 json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tok... | 281 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE... | 281 | 1 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a__ = 1.0_54_57_18_17E-34 # unit of ℏ : J * s
a__ = 3E8 # unit of c : m * s^-1
def lowercase ( SCREAMING_SNAKE_CASE__ ... | 198 |
def lowercase ( SCREAMING_SNAKE_CASE__ : int = 1_000 ) -> int:
_snake_case , _snake_case : str = 1, 1
_snake_case : List[Any] = 2
while True:
_snake_case : Union[str, Any] = 0
_snake_case : int = fa + fa
_snake_case , ... | 198 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def _UpperCamelCase (_lowerCamelCase : List[Any] ... | 24 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A : List[Any] = {}
try:
if not is_sentencepiece_available():... | 349 | 0 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 527 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {'''vocab_file''': ''... | 527 | 1 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCAmelCase_ : str = "laptop" ):
lowerCamelCase_ = F'''https://www.amazon.in/laptop/s?k={product}'''
lowerCamelCase_ ... | 675 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class snake_case ( pl.LightningModule ):
"""simple docstring"""
def __init... | 675 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/r... | 704 | import math
import sys
import cva
import numpy as np
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> np.ndarray:
"""simple docstring"""
lowercase = math.sqrt(UpperCAmelCase )
lowercase = 1 / (sigma * math.sqrt(2... | 479 | 0 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetY... | 138 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowercase__( )-> Optional[int]:
"""simple docstring"""
raise RuntimeError("CUDA ou... | 138 | 1 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
__A = logging.getLogger(__name__)
class a :
def __init__( self : ... | 702 | """simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def UpperCamelCase ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ... | 173 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/re... | 104 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __UpperCAmelCase ( A ... | 541 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__a : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, re... | 559 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class A ( lowerCamelCase_ , low... | 559 | 1 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
_UpperCamelCase : Optional[Any] = 300 # TEMPERATURE (unit = K)
def snake_case ( snake_case : Tuple , snake_case : List[Any] , snake_case : Union[str, Any... | 284 | import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
... | 559 | 0 |
from collections.abc import Callable
class lowercase :
def __init__( self : List[str] , _UpperCamelCase : Callable | None = None ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
# Store... | 647 | from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowerCamelCase : Optional[Any] = logging.get_logger(__na... | 647 | 1 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.ut... | 212 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : int, _UpperCAmelCase : str):
UpperCamelCase = ''''''
for i in table:
res += inp[i - 1]
return res
def __snake_case ( _UpperCAmelCase : Dict):
return data[1:] + data[0]... | 212 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : Dict =logging.get_logger(__name__)
UpperCAmelCase__ : Opti... | 718 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ : str ={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNe... | 269 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _UpperCamelCase : str ):
'''simple docstring'''
UpperCAmelCase_ = 0
for ch in input_str:
UpperCAmelCase_ = ord(_UpperCamelCase )
UpperCAmelCase_ = pow(2 , _UpperCamelCase )
... | 390 | '''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def __lowerCamelCase ( _UpperCamelCase : Tuple , _UpperCamelCase : List[Any] , _UpperCamelCase : Dict , _UpperCamelCase : Tuple ):
'''simple docstring'''
UpperCAmelC... | 390 | 1 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
_lowerCAmelCase : Dict ... | 694 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_lowerCAmelCase : List[Any] = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
_lowerCAmelCase : ... | 694 | 1 |
"""simple docstring"""
from collections import deque
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
lowercase__ : int = process_name # process... | 200 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNo... | 47 | 0 |
def A(__a: int = 6008_5147_5143 ):
try:
lowerCAmelCase_ = int(__a )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one." )
lowerCAmelCas... | 226 |
from __future__ import annotations
def A(__a: float , __a: float , __a: float ):
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise ValueError("Resistance cannot be negative" )
if v... | 226 | 1 |
"""simple docstring"""
import string
from math import logaa
def A ( _A, _A ):
"""simple docstring"""
snake_case_ :Union[str, Any] = document.translate(
str.maketrans("", "", string.punctuation ) ).replace("\n", "" )
snake_case_ :Tuple ... | 584 |
"""simple docstring"""
def A ( _A ):
"""simple docstring"""
return 10 - x * x
def A ( _A, _A ):
"""simple docstring"""
# Bolzano theory in order to find if there is a root between a and b
if equation(_A ) * equation(_A ) >= 0:
raise Val... | 584 | 1 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common im... | 720 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = ... | 437 | 0 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import id... | 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 t... | 38 | 1 |
'''simple docstring'''
# 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/LICE... | 706 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case__ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbe... | 638 | 0 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def a__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : float = 0.0 , _SCREAMING_SNAKE_CASE : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) ... | 71 |
'''simple docstring'''
import faiss # 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 requests # noqa: F401 # Here to have a nice missing dependency error message early on
imp... | 133 | 0 |
'''simple docstring'''
import os
import sys
_A: int = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSeque... | 704 |
'''simple docstring'''
# Copyright 2021 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... | 617 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the roo... | 132 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'YituTech/conv-bert-base': 'https://huggingface.co/YituT... | 132 | 1 |
def _a ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
_lowerCAmelCase = len(__SCREAMING_SNAKE_CASE )
_lowerCAmelCase = len(__SCREAMING_SNAKE_CASE )
_lowerCAmelCase = (
first_str_length if firs... | 702 |
from __future__ import annotations
_UpperCamelCase: Dict =8.9_88e9 # units = N * m^s * C^-2
def _a ( __SCREAMING_SNAKE_CASE : float , __SCREAMING_SNAKE_CASE : float , __SCREAMING_SNAKE_CASE : float , __SCREAMING_SNAKE_CASE : float ):
"""... | 585 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 86 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a :List[Any] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
... | 86 | 1 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
SCREAMING_SNAKE_CASE__ : int ... | 629 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _A ( lowerCamelCase ):
a__ : List[str] = []
if isinstance(... | 629 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {
'''configuration_vision_text_dual_encoder''': ['''VisionTextDualEncoderConfig'''],
'''proce... | 122 | """simple docstring"""
from math import sqrt
def lowercase__( __SCREAMING_SNAKE_CASE : int ):
assert isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
lowercase_ : List[Any] = T... | 425 | 0 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__snake_case = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
... | 711 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
res... | 280 | 0 |
def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : str ):
def get_matched_characters(lowerCAmelCase_ : str, lowerCAmelCase_ : str ) -> str:
__lowerCAmelCase = []
__lowerCAmelCase = min(len(_stra ), len(_stra ... | 53 |
"""simple docstring"""
import random
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = num - 1
_lowerCAmelCase : List[Any] = 0
while s % 2 == 0:
_lowerCAmelCase : Tuple = s // 2... | 259 | 0 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def a ( __a="ro" , __a="en" , __a="wmt16" , __a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 280 |
'''simple docstring'''
import qiskit
def a ( __a , __a ) -> qiskit.result.counts.Counts:
'''simple docstring'''
UpperCamelCase__ :int = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
U... | 280 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscr... | 625 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class snake_case_ ( a ):
'''simple docstring'''
__UpperCamelCase = 'EncodecFeatureExtractor'
__UpperCamelCase ... | 625 | 1 |
def snake_case (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
def get_matched_characters(UpperCamelCase : str , UpperCamelCase : str ) -> str:
lowerCamelCase__ = []
lowerCamelCase__ = min(len(_stra ) , ... | 235 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as... | 235 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Any = logging.get_logger(__name__)
UpperCamelCase : int = {}
class A__ ( A__ ):
"""simple docstring"""
_lowercase = 'llama'
_lowercase = [... | 37 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class UpperCamelCase__ ( __lowerCAmelCase ):
lowerCAmelCase__ : Any = "bert-generation"
def __init__( self : Union[str, Any] , lowerCamelCase : Optiona... | 489 | 0 |
"""simple docstring"""
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECOR... | 229 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : Optional[int] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
... | 229 | 1 |
'''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
fr... | 394 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_s... | 517 | 0 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def snake_case__ ( UpperCAmelCase : int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmelCase : float , Up... | 111 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOut... | 111 | 1 |
'''simple docstring'''
def A ( UpperCamelCase_ : int ) -> str:
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
lowerCAmelCase__ = len(bin(UpperCamelCase_ )[3:] )
lowerCAmelCase__ = bin(abs(Up... | 48 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def A ( UpperCamelCase_ : Tuple ) -> int:
'''simple docstring'''
for param in module.parameters():
lowerCAmelCase__ = False
def A ( ) ->... | 48 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_xlm_roberta_xl": [
"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaXLConfig",
"XLMRo... | 707 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''... | 694 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__snake_case :Optional[Any] =logging.get_logger(__name__)
def ... | 106 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :List[str] =logging.get_logger(__name__)
__snake_case :int ={'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class lowerCAmelCase__ ( _lowerCamelCase ... | 106 | 1 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase_ : int = ... | 718 |
'''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... | 521 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( a : int ):
assert (
isinstance(a , a ) and number_of_steps > 0
), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
a__ ... | 394 |
'''simple docstring'''
def lowerCAmelCase_ ( a : int ):
a__ = generate_pascal_triangle(a )
for row_idx in range(a ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=' ' )
... | 394 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHea... | 704 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase = {
'configuration_layoutlmv3': [
'LAYOUTLMV3_PRETRAINED_CO... | 613 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , __lowercase=2 , __lowercase=3 , __lowercase=64 , __lowerc... | 167 | from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '... | 167 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def lowercase_ ( __A : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercas... | 701 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not ... | 8 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common impo... | 583 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCamelCase )
class UpperCAmelCase ( __lowerCamelCase ):
a__: str = fi... | 583 | 1 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
lowerCAmelCase = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()["""__file... | 675 |
import math
import unittest
from transformers import BioGptConfig, 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 ModelTe... | 675 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase : str = {
'distilbert-base-uncased': 'https://huggingf... | 511 |
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = int(a )
if n_element < 1:
SCREAMING_SNAKE_CASE_ : Optional[int] = ValueError('a should be a positive number' )
raise my_error
SCREAMING_SNAKE_CASE_ : str ... | 511 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
... | 583 |
def _lowercase ( lowercase__ , lowercase__ ):
__lowerCAmelCase : Union[str, Any] = len(lowercase__ )
__lowerCAmelCase : Any = len(lowercase__ )
__lowerCAmelCase : str = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
__low... | 583 | 1 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 198 | """simple docstring"""
def lowercase__( __SCREAMING_SNAKE_CASE : int = 2_00 ):
lowercase_ : str = [1, 2, 5, 10, 20, 50, 1_00, 2_00]
lowercase_ : Dict = [0] * (pence + 1)
lowercase_ : List[Any] = 1 # base case: 1 way to make ... | 425 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase( _a ):
snake_case_ : Dict = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , **SCREAMING_SNAKE_C... | 473 |
import os
from datetime import datetime as dt
from github import Github
A : Union[str, Any] = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def _lowe... | 473 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0... | 366 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 366 | 1 |
from collections.abc import Generator
from math import sin
def A_ ( lowercase_ ) ->Dict:
"""simple docstring"""
if len(__A ) != 3_2:
raise ValueError('Input must be of length 32' )
SCREAMING_SNAKE_CASE = b''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 ... | 701 |
def A_ ( lowercase_ , lowercase_ ) ->int:
"""simple docstring"""
if len(lowercase_ ) != len(lowercase_ ):
raise ValueError('String lengths must match!' )
SCREAMING_SNAKE_CASE = 0
for chara, chara in zip(lowercase_ , lowercase_ ):
if chara != chara:
count += 1... | 259 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from .... | 602 |
"""simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers im... | 602 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoO... | 41 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
i... | 41 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__n... | 472 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__snake_case = {
"""cola""": ... | 472 | 1 |
"""simple docstring"""
def __a ( ) ->Union[str, Any]:
a__: List[str] = 0
for i in range(1 , 1001 ):
total += i**i
return str(_SCREAMING_SNAKE_CASE )[-10:]
if __name__ == "__main__":
print(solution())
| 714 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ = {
'configuration_distilbert': [
'DISTILBERT_PRETRAIN... | 217 | 0 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _A ( lowercase__ , lowercase__ , lowercase__ ):
lowercase__ = OmegaConf.load(lowercase__ )
lowercase__ = to... | 325 |
'''simple docstring'''
def _A ( lowercase__ ):
assert (
isinstance(lowercase__ , lowercase__ ) and number_of_steps > 0
), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
lowercase__ ... | 325 | 1 |
import os
import string
import sys
__snake_case : List[str] =1 << 8
__snake_case : Optional[int] ={
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 2_7,
'up': 6_5 + ARROW_KEY_FLAG,
'down': 6_6 + ARROW_KEY_FLAG,
'right': 6_7 + ARROW_KEY_FLAG,
'left': 6_... | 717 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ... | 90 | 0 |
class A :
def __init__( self: List[Any] ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ ={}
def lowerCAmelCase__ ( self: str ) -> None:
'''simple docstring'''
... | 54 | from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
def __lt__( self , __SCREAMING_SNAKE_CASE ) ->Optiona... | 312 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
... | 90 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case : Union[str, Any] =logging.get_logger(__name__)
__snake_case : Dict ={'voc... | 90 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 8 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if i... | 171 | 0 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights... | 572 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedC... | 572 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 153 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def SCREAMING_SNAKE_CASE ( lowercase_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3... | 588 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Union[str, Any] = logging.get_logger(__name__)
_A : Optional[int] = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config... | 518 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __magic_name__ ( __snake_case : List[str] ) -> Tuple:
lowercase : Union[str, Any] = ... | 518 | 1 |
"""simple docstring"""
from __future__ import annotations
_lowerCAmelCase :List[str] = '#'
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self ) -> None:
_UpperCAmelCase : dict = {}
def __lowerCAmelCase ( ... | 506 |
"""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... | 506 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _snake_case ... | 704 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : List[Any] ... | 57 | 0 |
def _lowerCAmelCase ( A__: Optional[int] , A__: str ):
'''simple docstring'''
UpperCAmelCase = [1]
for i in range(2 , __UpperCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of ... | 254 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBas... | 96 | 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 Regres... | 718 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__a = '\\n\n'
__a = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt... | 300 | 0 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Tra... | 622 |
a__ : Tuple = "Tobias Carryer"
from time import time
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : str , lowerCAmelCase : List[str] , lowerCAmelCase : Any , lowerCAmelCase : str , lowerCAmelCase : ... | 622 | 1 |
"""simple docstring"""
import unittest
from transformers import DonutProcessor
lowercase__ = 'naver-clova-ix/donut-base'
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Union[st... | 217 | """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
lowercase__ = logging.get_logger(__name__)
lowercase__ = '▁'
lowercase__ ... | 217 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseO... | 538 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin... | 538 | 1 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.... | 102 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAM... | 102 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)... | 458 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __magic_name__ ( ) -> str:
"""simple docstring"""
lowercase_ : Optional[int] = ArgumentP... | 458 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils... | 393 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
SCREAMING_SNAKE_CASE__ = ... | 393 | 1 |
"""simple docstring"""
def lowercase__ ( lowerCAmelCase : int = 100 ) -> int:
"""simple docstring"""
UpperCAmelCase = n * (n + 1) * (2 * n + 1) / 6
UpperCAmelCase = (n * (n + 1) / 2) ** 2
return int(square_of_sum - ... | 373 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class _UpperCAmelCase :
def __init__( self , lowercase_ ) -> None:
UpperCAmelCase = value
UpperCAmelCase = None
... | 373 | 1 |
import logging
from transformers import PretrainedConfig
SCREAMING_SNAKE_CASE : Tuple = logging.getLogger(__name__)
SCREAMING_SNAKE_CASE : Dict = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summariza... | 441 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import Vide... | 441 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase : str = logging.get_logger(__name__)
lowercase : Optional[Any] = {
'nielsr/canine-s': 2_0_4_... | 649 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax... | 649 | 1 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone... | 498 |
"""simple docstring"""
from __future__ import annotations
A_ = []
def _lowerCAmelCase ( UpperCAmelCase__ : list[list[int]], UpperCAmelCase__ : int, UpperCAmelCase__ : int ) ->bool:
for i in range(len(UpperCAmelCase__ ) ):
... | 498 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils impo... | 62 | """simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_UpperCamelCase : int = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
... | 599 | 0 |
'''simple docstring'''
from collections import namedtuple
import requests
from lxml import html # type: ignore
snake_case_ : Optional[Any] = namedtuple('covid_data', 'cases deaths recovered')
def __snake_case ( _UpperCAmelCase : str = "https://www.worldometers.info/coronavirus/"):
... | 702 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Dict = logging.get_logger(__name__)
snake_case_ : Union[str, Any] = {
'facebook/encodec_24khz': '... | 350 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : Tuple ={
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRA... | 483 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Tuple =logging.get_logger(__name__)
A_ : int ={
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/effici... | 483 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCAmelCase : List[Any] = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTo... | 284 |
import csv
import tweepy
# Twitter API credentials
__lowerCAmelCase : str = ""
__lowerCAmelCase : Any = ""
__lowerCAmelCase : Any = ""
__lowerCAmelCase : Optional[int] = ""
def UpperCAmelCase_ ( __lowerCAmelCase ) -> None:
... | 284 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
Rand... | 257 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
fro... | 497 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_lowerCAmelCase = "docs/source/en/_toctree.yml"
def _lowerCAmelCase ( lowercase : Any ) ->str:
"""simple docstring"""
lowercase__ = ... | 706 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( lowercase : List[str] , lower... | 318 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase )
class UpperCAmelCase ( _UpperCAmelCase ):
'''simple docstring'''
SCREAMING_SNAKE_C... | 42 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers impo... | 588 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, l... | 297 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ ... | 297 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
a : Dict = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : ... | 69 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_ten... | 135 | 0 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# 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
... | 295 |
"""simple docstring"""
# Copyright 2021 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
... | 295 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
fro... | 269 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_nump... | 269 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.... | 381 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase__:
'''simple docstring'''
def __init__( self , __lowerCamelCase ) -> Tuple:
_SCREAMING_SNAKE_CASE : List[str] = list_of_po... | 381 | 1 |
'''simple docstring'''
from __future__ import annotations
class a_ :
def __init__( self : List[str] , lowercase : Optional[Any]=None ):
"""simple docstring"""
lowercase_ :Optional[int] = data
lowe... | 172 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[Any] ={'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_av... | 172 | 1 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
A_ : Optional[Any] = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass cl... | 706 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Tuple = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_... | 696 | 0 |
"""simple docstring"""
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... | 573 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order speci... | 573 | 1 |
import copy
import random
from transformers import CLIPTokenizer
class UpperCamelCase__ ( snake_case_ ):
def __init__( self : List[Any] , *UpperCamelCase__ : int , **UpperCamelCase__ : List[Any] ):
'''simple docstring'''
... | 701 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_... | 650 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor... | 38 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A ( lowercase__ : dict ) -> tuple:
return (data["data"], d... | 45 | 0 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewT... | 214 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _A ( __snake_case :int ) -> Optional[int]:
"""simple docstring"""
if (
(cp >= 0x4E_00 and cp <= 0x9F_FF)
or (cp >= 0x34_0... | 214 | 1 |
import heapq
def _a ( __UpperCamelCase : dict ):
lowerCAmelCase__ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queu... | 233 |
def _a ( __UpperCamelCase : int ):
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
raise ValueError('''check_bouncy() accepts only integer arguments''' )
lowerCAmelCase__ : List[str] = str(__UpperCamelCase )
lowerCAmelCase__ : List[Any] ... | 233 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ... | 193 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
a_ = datasets.utils.logging.get_logger(__name__)
@dataclass
class _UpperCamelCase ( datasets.B... | 193 | 1 |
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