code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
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
UpperCAmelCase__ = logging.get_... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impor... | 290 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str = "cpu" , _UpperCAmelCase : Union[str, None] = None ) -> None:
'''simple docstring'''
_UpperCAmelC... | 290 |
import string
import numpy
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
class __lowerCAmelCase :
... | 290 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from ... | 290 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__ = "src/transformers"
UpperCAmelCase__ = "docs/source/en/ta... | 290 | 1 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> list[int]: # This function is recursive
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
# If the array contains only one element, we return it (it's the stop cond... | 290 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 290 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 | 1 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@require_torch
... | 290 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase__ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFF... | 290 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_visio... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenizati... | 290 | 1 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blender... | 290 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import H... | 290 | 1 |
from maths.prime_factors import prime_factors
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = F"Input value of [number={number}] must be an integer"
... | 290 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/ma... | 290 | 1 |
import operator as op
UpperCAmelCase__ = "scaler.pt"
UpperCAmelCase__ = "pytorch_model"
UpperCAmelCase__ = "random_states"
UpperCAmelCase__ = "optimizer"
UpperCAmelCase__ = "scheduler"
UpperCAmelCase__ = "pytorch_model.bin"
UpperCAmelCase__ = "pytorch_model.bin.index.json... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_clip": [
"CLIP_PRETRAINED_... | 290 | 1 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
UpperCAmelCase__ = _symbol_datab... | 290 |
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, CLIPTokenizerFast
from... | 290 | 1 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 290 | 1 |
from __future__ import annotations
UpperCAmelCase__ = 8.988E9 # units = N * m^s * C^-2
def A ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> dict[st... | 290 |
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__ = {
"facebook/xmod-base": "https://huggingface.co/facebo... | 290 | 1 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
UpperCAmelCase__ = logging.get_logger(__name__)
class __low... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 290 | 1 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.p... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
... | 290 | 1 |
def A ( _UpperCAmelCase : list[int] , _UpperCAmelCase : list[int] ) -> tuple[float, float]:
'''simple docstring'''
# Check if the input is valid
if not len(_UpperCAmelCase ) == len(_UpperCAmelCase ) == 3:
raise ValueError('Please enter a... | 290 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCAmelCase__ = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the do... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {"configuration_mbart": ["MBART_PRETRAINED_CONFI... | 290 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__... | 290 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def A ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase ... | 290 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 290 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pip... | 290 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 290 | 1 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCAmelCase__ = "__DUMMY_TRANSFORMERS_USER__"
UpperCAmelCase__ = "Dummy User"
UpperCAmelCase__ = "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"... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxC... | 290 | 1 |
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A ( _UpperCAmelCase : ... | 290 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 | 1 |
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
_UpperCAmelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 290 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 290 | 1 |
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 # noqa: F401 # Here to have a nic... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impor... | 290 | 1 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 290 |
import string
import numpy
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
class __lowerCAmelCase :
... | 290 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( A ):
UpperCamelCase = ... | 290 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__ = "src/transformers"
UpperCAmelCase__ = "docs/source/en/ta... | 290 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def A ( _UpperCAmelCase : bytes , _UpperCAmelCase : int ) -> np.array:
'''simple docstring'''
_UpperCAmelCase = F"{sampling_ra... | 290 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 | 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 HeunDiscreteScheduler
from ... | 290 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 | 1 |
UpperCAmelCase__ = [
"DownloadConfig",
"DownloadManager",
"DownloadMode",
"StreamingDownloadManager",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 290 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase__ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFF... | 290 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def A ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str=7 ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = ... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenizati... | 290 | 1 |
from math import factorial
def A ( _UpperCAmelCase : int = 100 ) -> int:
'''simple docstring'''
return sum(map(_UpperCAmelCase , str(factorial(_UpperCAmelCase ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ... | 290 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import H... | 290 | 1 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def A ( _UpperCAmelCase : T... | 290 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/ma... | 290 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availa... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_clip": [
"CLIP_PRETRAINED_... | 290 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
def A ( _UpperCAmelCase : Tuple ) -> Optional[Any]:
'''simp... | 290 |
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, CLIPTokenizerFast
from... | 290 | 1 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A ( _UpperCAmelCase : Tuple ) -> int:
'''simple docstring'''
# This defines a "chinese character" as anything in the CJK... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 290 | 1 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase__ = logging.getLogger()
def A ( _UpperCAmelCase : ... | 290 |
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__ = {
"facebook/xmod-base": "https://huggingface.co/facebo... | 290 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import H... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 290 | 1 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_f... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
... | 290 | 1 |
from __future__ import annotations
import math
def A ( _UpperCAmelCase : list , _UpperCAmelCase : list ) -> list:
'''simple docstring'''
if len(_UpperCAmelCase ) != 2 or len(a[0] ) != 2 or len(_UpperCAmelCase ) != 2 or len(b[0] ) != 2:
... | 290 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCAmelCase__ = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the do... | 290 | 1 |
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_torch_available():
... | 290 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__... | 290 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[Any]) -> Dict:
"""simple d... | 290 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 290 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __lowerCAmelCase ( A ):
def __init__( self : List[str] , A : List[Any] , A : Any , A : List[Any]) -> O... | 290 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 290 | 1 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, 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_available():
impor... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxC... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeTowerTextConfig... | 290 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ = get_tests_dir("fixtures/test_s... | 290 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 290 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
# See all AltCL... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impor... | 290 | 1 |
from __future__ import annotations
import time
UpperCAmelCase__ = list[tuple[int, int]]
UpperCAmelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0... | 290 |
import string
import numpy
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
class __lowerCAmelCase :
... | 290 | 1 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
def A ( _UpperCAmelCase : List[Any]=None , _UpperCAmelCa... | 290 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__ = "src/transformers"
UpperCAmelCase__ = "docs/source/en/ta... | 290 | 1 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
class __lowerCAmelCase ( A ):
UpperCamelCase = ['''input_ids''... | 290 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmel... | 290 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 | 1 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
UpperCAmelCase__ = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
"... | 290 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase__ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFF... | 290 | 1 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenizati... | 290 | 1 |
import unittest
from knapsack import knapsack as k
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : List[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = [0]
... | 290 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import H... | 290 | 1 |
UpperCAmelCase__ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def A ( ) -> None:
'''simple docstring'''
_UpperCAmelCase = input('Enter message: ' )
_UpperCAmelCase = input('Enter key [alphanumeric]: ' )
_UpperCAmelCase = input('Encrypt/Decrypt [e/d]: '... | 290 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/ma... | 290 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_clip": [
"CLIP_PRETRAINED_... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase__ = {
"configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}
try:
if not is_torch_available():... | 290 |
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, CLIPTokenizerFast
from... | 290 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, D... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 290 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __lowerCAmelCase :
def __init__( self : Dict , A : list[tuple[float, float]]) -> Any:
"""simple docstring"""
_UpperCAmelCase = list_of_points
... | 290 |
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__ = {
"facebook/xmod-base": "https://huggingface.co/facebo... | 290 | 1 |
# using dfs for finding eulerian path traversal
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any]=None ) -> Optional[int]:
'''simpl... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 290 | 1 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
UpperCAmelCase__ = 100
UpperCAmelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
cont... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
... | 290 | 1 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils imp... | 290 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCAmelCase__ = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the do... | 290 | 1 |
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 ( A , unittest.TestCase ... | 290 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__... | 290 | 1 |
from __future__ import annotations
def A ( _UpperCAmelCase : tuple[int, int] , _UpperCAmelCase : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = position
_UpperCAmelCase = [
... | 290 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 290 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 290 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 290 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxC... | 290 | 1 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 290 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 | 1 |
from __future__ import annotations
def A ( _UpperCAmelCase : str , _UpperCAmelCase : list[str] | None = None , _UpperCAmelCase : dict[str, float] | None = None , _UpperCAmelCase : bool = False , ) -> tuple[i... | 290 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 290 | 1 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impor... | 290 | 1 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - us... | 290 |
import string
import numpy
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
class __lowerCAmelCase :
... | 290 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 290 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__ = "src/transformers"
UpperCAmelCase__ = "docs/source/en/ta... | 290 | 1 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 290 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 | 1 |
UpperCAmelCase__ = [0, 2, 4, 6, 8]
UpperCAmelCase__ = [1, 3, 5, 7, 9]
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : list[int] , _UpperCAmelCase : int ) -> int:
'''simp... | 290 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __lowerCA... | 290 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase__ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFF... | 290 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenizati... | 290 | 1 |
UpperCAmelCase__ = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_9344,
"knot": 1.852,
}
UpperCAmelCase__ = {
"km/h": 1.0,
"m/s": 0.2_7777_7778,
"mph": 0.6_2137_1192,
"knot": 0.5_3995_6803,
}
def A ( _UpperCAmelCase : float , _UpperCAmelC... | 290 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import H... | 290 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import ... | 290 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/ma... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase__ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependen... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_clip": [
"CLIP_PRETRAINED_... | 290 | 1 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
... | 290 |
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, CLIPTokenizerFast
from... | 290 | 1 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
ne... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 290 | 1 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_colla... | 290 |
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__ = {
"facebook/xmod-base": "https://huggingface.co/facebo... | 290 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 290 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def A ... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
... | 290 | 1 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_... | 290 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCAmelCase__ = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the do... | 290 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowerCAmelCase ( A ):
UpperCamelCas... | 290 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__... | 290 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPIma... | 290 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 290 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
UpperCAmelCase__ = get_logger(__name__)
UpperCAmelCase__ = r"\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)... | 290 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 290 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxC... | 290 | 1 |
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 __lowerCAmelCase ( A , A ):
... | 290 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 | 1 |
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 290 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 290 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__ = "src/transformers"
UpperCAmelCase__ = "docs/source/en/ta... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impor... | 290 | 1 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_tor... | 290 |
import string
import numpy
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
class __lowerCAmelCase :
... | 290 | 1 |
class __lowerCAmelCase :
def __init__( self : Tuple , A : Optional[int]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = arr.split(',')
def _lowerCamelCase ( self : Tuple) -> Dict:
"""simple doc... | 290 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__ = "src/transformers"
UpperCAmelCase__ = "docs/source/en/ta... | 290 | 1 |
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__ = {
"facebook/xmod-base": "https://huggingface.co/facebo... | 290 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 | 1 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase__ = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\... | 290 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 | 1 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_... | 290 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase__ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFF... | 290 | 1 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase__ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFF... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenizati... | 290 | 1 |
import unittest
import numpy as np
from transformers import BertConfig, 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_available():
from transformers.mo... | 290 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import H... | 290 | 1 |
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_available,
is_accele... | 290 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/ma... | 290 | 1 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class __lowerCAmelCase ( A ):
def __init__( self : List[str] , *A : List[Any] , **A : Union[str, ... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_clip": [
"CLIP_PRETRAINED_... | 290 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece... | 290 |
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, CLIPTokenizerFast
from... | 290 | 1 |
import numpy as np
def A ( _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] ) -> Optional[int]:
... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 290 | 1 |
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