code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_com... | 50 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> set:
lowerCamelCase__ : Optional[Any] = set()
# edges = list of graph's edges
lowerCamelCase__ : List[str] = get_edges(_UpperCAmelCase )
# While there are still elements in edges list, take an arbi... | 50 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int:
lowerCamelCase__ : List[Any] = abs(_UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = 0
while n > 0:
res += n % 10
n //= 10
return res
def SCREAMING_SNAKE_CASE ... | 50 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multi... | 50 | 1 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
_UpperCAmelCase : Any = logging.get_logger(__nam... | 50 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase : Dict ... | 50 | 1 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self : str , UpperCAmelCase : int ) -> None:
lowerCamelCase__ : int = data
lowerCamelCase__ : Node | None = None
lowerCamelCase__ : Node | None = Non... | 50 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_comm... | 50 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_UpperCAmelCase : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
... | 50 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase=() , _UpperCAmelCase=None , _UpperCAmelCas... | 50 | 1 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
_UpperCAmelCase : Optional[int] = """<<<<<<< This should probably be modified because it mentions: """
_UpperCAmelC... | 50 |
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 imp... | 50 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 100_0000 ) -> int:
lowerCamelCase__ : int = limit + 1
lowerCamelCase__ : Optional[Any] = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _Upper... | 50 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> list[tuple[int, int]]:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = position
lowerCamelCase__ : Optional[Any] = [
(y + 1,... | 50 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@requ... | 50 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int:
lowerCamelCase... | 50 | 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 app... | 50 |
# 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
#
# Unless required by app... | 50 | 1 |
from math import factorial
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 100 ) -> int:
return sum(map(_UpperCAmelCase , str(factorial(_UpperCAmelCase ) ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 50 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 100_0000 ) -> int:
lowerCamelCase__ : int = limit + 1
lowerCamelCase__ : Optional[Any] = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _Upper... | 50 | 1 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test... | 50 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_UpperCAmelCa... | 50 | 1 |
from __future__ import annotations
import math
_UpperCAmelCase : Any = """2020.9.26"""
_UpperCAmelCase : Optional[int] = """xcodz-dot, cclaus, dhruvmanila"""
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCA... | 50 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRo... | 50 | 1 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase : str = logging.get_logger("""transformers.models.speecht5""")
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ... | 50 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase : Optional[Any]... | 50 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppToken... | 50 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 50 | 1 |
from __future__ import annotations
_UpperCAmelCase : Optional[int] = tuple[int, int, int]
_UpperCAmelCase : int = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
_UpperCAmelCase : Optional[int] = """ABCDEFGHIJKLMNOPQRSTU... | 50 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[str] = len(_UpperCAmelCase )
lowerCamelCase__ : str = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zer... | 50 | 1 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
class lowerCAmelCase :
UpperCAmelCase__ = None
@experimental
def SCREAMING_SNAKE_... | 50 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = """M-CLIP"""
def __init__( self : Optional[Any] , UpperCAmelCase : Union[str, Any]=1024 , UpperCAmelCas... | 50 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> int:
lowerCamelCase__ : Union[str, Any] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowerCamelCase__ : List[Any] = n - k
# Calculat... | 50 |
from itertools import count
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 50 ) -> int:
lowerCamelCase__ : Optional[Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 50 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Union[str, Any] = {
"""configuration_xlm_roberta_xl""": [
"""XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XLMRobertaXLConfig""",
... | 50 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> None:
create_state_space_tree(_UpperCAmelCase , [] , 0 )
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )... | 50 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that nex... | 50 |
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_sentencepie... | 50 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
# ===== initialization =====
lowerCamelCase__ : Union[str, ... | 50 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
lowerCamelCase__ : Optional[int] = [
'encoder.version',
'decoder.version',
... | 50 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_speech_to_text": ["SPEECH_TO_T... | 0 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( _UpperCAmelC... | 50 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from d... | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase ( nn.Module ):
UpperCAmelCase__ = 42
UpperCAmelCase__ = jnp.floataa
def A_ ( self : Any ) -> Any:
lowerCamelCase__ : str = nn.Conv(
self.out_ch... | 50 | 0 |
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 2 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> set:
lowerCamelCase__ : Optional[Any] = set()
# edges = list of graph's edges
lowerCamelCase__ : List[str] = get_edges(_UpperCAmelCase )
# While there are still elements in edges list, take an arbi... | 50 | 0 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
lowercase : Dict = '\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding... | 3 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multi... | 50 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-s... | 4 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase : Dict ... | 50 | 0 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
... | 5 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_comm... | 50 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
A : Optional[Any] = 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 doc... | 6 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase=() , _UpperCAmelCase=None , _UpperCAmelCas... | 50 | 0 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
class A ( _UpperCAmelCase ):
"""simple docstring"""... | 7 |
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 imp... | 50 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''facebook/xlm-roberta-xl''': '''https:/... | 8 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> list[tuple[int, int]]:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = position
lowerCamelCase__ : Optional[Any] = [
(y + 1,... | 50 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __magic_name__( self :Dict ) -> Dict:
__... | 9 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int:
lowerCamelCase... | 50 | 0 |
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
__A = logging.get_logger(__name__)
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
lowerCamelCase... | 10 |
# 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
#
# Unless required by app... | 50 | 0 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thin... | 11 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 100_0000 ) -> int:
lowerCamelCase__ : int = limit + 1
lowerCamelCase__ : Optional[Any] = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _Upper... | 50 | 0 |
UpperCAmelCase_ = [
'Audio',
'Array2D',
'Array3D',
'Array4D',
'Array5D',
'ClassLabel',
'Features',
'Sequence',
'Value',
'Image',
'Translation',
'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD... | 12 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_UpperCAmelCa... | 50 | 0 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowerCAmelCase : str = ["""small""", """medium""", """large"""]
lowerCAmelCase : Dict = """lm_head.decoder.weight"""
lowerCAmelCase : Optional[Any] = """lm_head.weight"""
def ... | 13 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRo... | 50 | 0 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 14 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase : Optional[Any]... | 50 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcesso... | 15 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 50 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is... | 16 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[str] = len(_UpperCAmelCase )
lowerCamelCase__ : str = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zer... | 50 | 0 |
"""simple docstring"""
import sys
from collections import defaultdict
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[str] ):
__lowercase = []
def _lowercase ( self : Union[str, Any], UpperCAmelCase__ : Opt... | 17 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = """M-CLIP"""
def __init__( self : Optional[Any] , UpperCAmelCase : Union[str, Any]=1024 , UpperCAmelCas... | 50 | 0 |
import os
def _snake_case ( ):
"""simple docstring"""
with open(os.path.dirname(lowerCAmelCase ) + "/p022_names.txt" ) as file:
SCREAMING_SNAKE_CASE_ : List[str] = str(file.readlines()[0] )
SCREAMING_SNAKE_CASE_ : Any = names.replace("\"" , "" ).split("," )
... | 18 |
from itertools import count
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 50 ) -> int:
lowerCamelCase__ : Optional[Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 50 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNet... | 19 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> None:
create_state_space_tree(_UpperCAmelCase , [] , 0 )
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )... | 50 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 20 |
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_sentencepie... | 50 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCond... | 21 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
lowerCamelCase__ : Optional[int] = [
'encoder.version',
'decoder.version',
... | 50 | 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__SCREAMING_SNAKE_CASE :Union[str, Any] = [
os.path.join(os.path.dirname(__file__), dirname)
f... | 22 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( _UpperCAmelC... | 50 | 0 |
'''simple docstring'''
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 SCREAMING_S... | 23 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase ( nn.Module ):
UpperCAmelCase__ = 42
UpperCAmelCase__ = jnp.floataa
def A_ ( self : Any ) -> Any:
lowerCamelCase__ : str = nn.Conv(
self.out_ch... | 50 | 0 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import C... | 24 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> set:
lowerCamelCase__ : Optional[Any] = set()
# edges = list of graph's edges
lowerCamelCase__ : List[str] = get_edges(_UpperCAmelCase )
# While there are still elements in edges list, take an arbi... | 50 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ : Union[str, Any] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
tr... | 25 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multi... | 50 | 0 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm im... | 26 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase : Dict ... | 50 | 0 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__lowercase : List[str] = datasets.logging.get_logger(__name__)
__lowercase : List[str] = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Me... | 27 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_comm... | 50 | 0 |
'''simple docstring'''
from typing import Any
import numpy as np
def __lowerCamelCase ( A__ ) -> bool:
"""simple docstring"""
return np.array_equal(A__ , matrix.conjugate().T )
def __lowerCamelCase ( A__ , A_... | 28 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase=() , _UpperCAmelCase=None , _UpperCAmelCas... | 50 | 0 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_c... | 29 |
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 imp... | 50 | 0 |
def a ( snake_case__: int , snake_case__: list ):
'''simple docstring'''
_enforce_args(snake_case__ , snake_case__ )
if n == 0:
return 0
lowercase_ = float('''-inf''' )
for i in range(1 , n + 1 ):
lowe... | 30 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> list[tuple[int, int]]:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = position
lowerCamelCase__ : Optional[Any] = [
(y + 1,... | 50 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available... | 31 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int:
lowerCamelCase... | 50 | 0 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def __init__( self : Optional[int] , ... | 32 |
# 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
#
# Unless required by app... | 50 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark,... | 33 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 100_0000 ) -> int:
lowerCamelCase__ : int = limit + 1
lowerCamelCase__ : Optional[Any] = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _Upper... | 50 | 0 |
'''simple docstring'''
def snake_case_ (_a : int ):
UpperCAmelCase = (1 + 2_4 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def snake_case_ (_a : int = 5_0_0_0 ):
UpperCAmelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , ... | 34 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_UpperCAmelCa... | 50 | 0 |
'''simple docstring'''
import operator
def __snake_case( _lowerCAmelCase , _lowerCAmelCase = False , _lowerCAmelCase = None ) -> list:
snake_case__ : int = operator.lt if reverse else operator.gt
snake_case__ : Any = solution or []
... | 35 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRo... | 50 | 0 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
impo... | 36 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase : Optional[Any]... | 50 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
if depth < 0:
raise ValueError(... | 37 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 50 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import tor... | 38 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[str] = len(_UpperCAmelCase )
lowerCamelCase__ : str = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zer... | 50 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
... | 39 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = """M-CLIP"""
def __init__( self : Optional[Any] , UpperCAmelCase : Union[str, Any]=1024 , UpperCAmelCas... | 50 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve... | 40 |
from itertools import count
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 50 ) -> int:
lowerCamelCase__ : Optional[Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 50 | 0 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
_A : Union[str, Any] ='''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
_A... | 41 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> None:
create_state_space_tree(_UpperCAmelCase , [] , 0 )
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )... | 50 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : Union[str, Any] = {"configuration_xlnet... | 42 |
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_sentencepie... | 50 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
... | 43 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
lowerCamelCase__ : Optional[int] = [
'encoder.version',
'decoder.version',
... | 50 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> bool:
if not isinstance(_lowerCamelCase ,_lowerCamelCase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
_lowerCAmelCase : Any = str(_lowerCamelCase ... | 44 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( _UpperCAmelC... | 50 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerC... | 45 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase ( nn.Module ):
UpperCAmelCase__ = 42
UpperCAmelCase__ = jnp.floataa
def A_ ( self : Any ) -> Any:
lowerCamelCase__ : str = nn.Conv(
self.out_ch... | 50 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {"vocab... | 46 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> set:
lowerCamelCase__ : Optional[Any] = set()
# edges = list of graph's edges
lowerCamelCase__ : List[str] = get_edges(_UpperCAmelCase )
# While there are still elements in edges list, take an arbi... | 50 | 0 |
'''simple docstring'''
from __future__ import annotations
lowerCamelCase : List[str] = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _lowerCAmelCase ( _UpperCamelCase : list[list[int]] , _UpperCamelCase : list[int] ... | 47 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multi... | 50 | 0 |
# Imports
import numpy as np
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None ) -> List[Any]:
... | 48 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase : Dict ... | 50 | 0 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers i... | 49 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_comm... | 50 | 0 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.rou... | 51 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase=() , _UpperCAmelCase=None , _UpperCAmelCas... | 50 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowerCamelCase : Tuple = (720, 1280) # Height, Width
__lowerCamelCase : int = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowerCame... | 52 |
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 imp... | 50 | 0 |
'''simple docstring'''
from __future__ import annotations
class snake_case :
"""simple docstring"""
def __init__( self : Optional[int] , __A : list[list[int]] ):
__UpperCamelCase = TypeError(
'Matrices must be formed from a list of ... | 53 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> list[tuple[int, int]]:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = position
lowerCamelCase__ : Optional[Any] = [
(y + 1,... | 50 | 0 |
"""simple docstring"""
import itertools
import math
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, al... | 54 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int:
lowerCamelCase... | 50 | 0 |
'''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/LICENS... | 55 |
# 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
#
# Unless required by app... | 50 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if not isinstance(__UpperCAmelCase, __UpperCAmelCase ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(__UpperCAmelCase ) == 0:
ra... | 56 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 100_0000 ) -> int:
lowerCamelCase__ : int = limit + 1
lowerCamelCase__ : Optional[Any] = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _Upper... | 50 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : int =... | 57 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_UpperCAmelCa... | 50 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWit... | 58 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRo... | 50 | 0 |
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ):
snake_case : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=__lowerCamelCase , default="biencoder-nq... | 59 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase : Optional[Any]... | 50 | 0 |
"""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 _snake_case ( _snake_case :... | 60 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 50 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTeste... | 61 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[str] = len(_UpperCAmelCase )
lowerCamelCase__ : str = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zer... | 50 | 0 |
_A = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
_A = ['a', 'b', 'c', 'd', 'e']
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str ):
__UpperCamelCase ... | 62 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = """M-CLIP"""
def __init__( self : Optional[Any] , UpperCAmelCase : Union[str, Any]=1024 , UpperCAmelCas... | 50 | 0 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
@require_tor... | 63 |
from itertools import count
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 50 ) -> int:
lowerCamelCase__ : Optional[Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 50 | 0 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def UpperCAmelCase__ (snake_case__ : List[str] , snake_case__ : Tuple , snake_case__ : int , snake_case__ : List[str]=None ):
"""simple docstring"""
_snake_ca... | 64 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> None:
create_state_space_tree(_UpperCAmelCase , [] , 0 )
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )... | 50 | 0 |
from bisect import bisect
from itertools import accumulate
def lowerCAmelCase_ ( __A, __A, __A, __A ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ = sorted(zip(__A, __A ), key=lambda __A : x[0] / x[1], reverse=__A ... | 65 |
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_sentencepie... | 50 | 0 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_si... | 66 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
lowerCamelCase__ : Optional[int] = [
'encoder.version',
'decoder.version',
... | 50 | 0 |
'''simple docstring'''
import math
import sys
def __lowerCAmelCase ( UpperCamelCase__ ) -> str:
__lowerCamelCase = ''''''
try:
with open(UpperCamelCase__ , '''rb''' ) as binary_file:
__lowerCamelCase = binary_file.read()
for dat in data:
_... | 67 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( _UpperCAmelC... | 50 | 0 |
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 lowerCAmelCase__ ( ) -> Optional[Any]:
'''simple docstring'''
raise RuntimeError("CUDA out of memory.... | 68 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase ( nn.Module ):
UpperCAmelCase__ = 42
UpperCAmelCase__ = jnp.floataa
def A_ ( self : Any ) -> Any:
lowerCamelCase__ : str = nn.Conv(
self.out_ch... | 50 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.jso... | 69 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> set:
lowerCamelCase__ : Optional[Any] = set()
# edges = list of graph's edges
lowerCamelCase__ : List[str] = get_edges(_UpperCAmelCase )
# While there are still elements in edges list, take an arbi... | 50 | 0 |
'''simple docstring'''
import math
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase = 0 , lowerCAmelCase = 0 ):
"""simple docstring"""
_lowerCAmelCase = end or len(lowerCAmelCase )
for i in range(lowerCAmelCase ... | 70 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multi... | 50 | 0 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def A ( a_ ,a_ ) -> Union[str, Any]:
__UpperCamelCase : List[Any] =... | 71 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase : Dict ... | 50 | 0 |
"""simple docstring"""
from collections import defaultdict
def snake_case_ ( A_ : int ):
'''simple docstring'''
_lowerCamelCase : Dict = 1
_lowerCamelCase : List[Any] = True
for v in tree[start]:
if v not... | 72 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_comm... | 50 | 0 |
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_tensor, load_image, load_numpy,... | 73 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase=() , _UpperCAmelCase=None , _UpperCAmelCas... | 50 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational imp... | 74 |
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 imp... | 50 | 0 |
'''simple docstring'''
def a_ ( __snake_case : list , __snake_case : list , __snake_case : int ) -> int:
"""simple docstring"""
if len(__snake_case ) != len(__snake_case ):
raise ValueError('''The length of ... | 75 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> list[tuple[int, int]]:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = position
lowerCamelCase__ : Optional[Any] = [
(y + 1,... | 50 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}
class _UpperCamelCase (... | 76 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int:
lowerCamelCase... | 50 | 0 |
"""simple docstring"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class UpperCAmelCase_ :
def __init__( self , a , a , a , a , a , a=0.2 , a=0.2 ) -> Dict:
lowercase__ : Any = bp_numa
lowercase__ ... | 77 |
# 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
#
# Unless required by app... | 50 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case_ = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5On... | 78 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 100_0000 ) -> int:
lowerCamelCase__ : int = limit + 1
lowerCamelCase__ : Optional[Any] = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _Upper... | 50 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, requir... | 79 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_UpperCAmelCa... | 50 | 0 |
'''simple docstring'''
import argparse
import os
import re
a__ : Any = 'src/diffusers'
# Pattern that looks at the indentation in a line.
a__ : Any = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
a__ : List[Any] = re... | 80 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRo... | 50 | 0 |
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