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 copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTester... | 358 | def lowerCAmelCase__ ( a__ , a__ ) ->str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCamelCase = str(bin(a__ ) )[2:] # remove the leading "0b"
_UpperCamelCase = str(bin(a__ ) ... | 63 | 0 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def lowerCAmelCase__ ( a__ , a__ , a__ , a__ , a__ ) ->np.ndarray:
'''simple docstring'''
_UpperCamelCase = cva.getAffineTransform(a__ , a__ )
return cva.... | 359 | # This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase__ ( a__ , a__ , a__ , a_... | 63 | 0 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoade... | 360 | import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common i... | 63 | 0 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import Fr... | 361 | from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...test_p... | 63 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class _UpperC... | 362 | from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
... | 63 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <... | 363 | # Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _UpperCAmelCase ( Tenso... | 63 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UN... | 364 | import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {''... | 63 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowerCAmelCase__ ( a__ = "" ) ->dict[str, float]:
'''simple docstring'''
_UpperCamelCase = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
_UpperCamelCase = Beauti... | 365 | from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase ):
'''simple docstring'''
__A = ['''torch''']
def __init__( self : Any , *lowercase_ : Optional[int] , **lowercase_ : Optional[Any]) -> Opti... | 63 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distr... | 366 | import re
def lowerCAmelCase__ ( a__ ) ->str:
'''simple docstring'''
if len(re.findall("[ATCG]" , a__ ) ) != len(a__ ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , "TAGC" ) )
if __name__ == "__main__":
import doctes... | 63 | 0 |
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCAmelCase__ ( a__ , a__ , a__ , a__ = 100 , ) ->float:
'''simple docstring'''
_UpperCamelCase = x_start
_UpperCamelCase = fnc(a__ )
_UpperCamelCase ... | 367 | import os
def lowerCAmelCase__ ( ) ->Any:
'''simple docstring'''
with open(os.path.dirname(a__ ) + "/grid.txt" ) as f:
_UpperCamelCase = [] # noqa: E741
for _ in range(20 ):
l.append([int(a__ ) for x in f.readline().split()] )
_UpperCamelCase ... | 63 | 0 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase__ ( a__ , a__ , a__ , ... | 368 | from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docst... | 63 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''... | 369 | import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():... | 63 | 0 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
lowerCamelCase__ = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
lowerCamelCase__ = [file for file in filepaths if file != fi... | 370 | from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regress... | 63 | 0 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configu... | 371 | import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''vocab_file''': '''vocab.json''',
'''merges_file... | 63 | 0 |
def lowerCAmelCase__ ( a__ ) ->Optional[Any]:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{... | 350 | import math
class _UpperCAmelCase :
'''simple docstring'''
def __UpperCAmelCase ( self : Dict , lowercase_ : list[list[float]] , lowercase_ : list[int]) -> int:
"""simple docstring"""
_UpperCamelCase = 0.0
_UpperCamelCase ... | 63 | 0 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docst... | 351 | import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNet... | 63 | 0 |
def lowerCAmelCase__ ( a__ ) ->str:
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 352 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}
try:
if not is_tok... | 63 | 0 |
def lowerCAmelCase__ ( a__ , a__ ) ->str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCamelCase = str(bin(a__ ) )[2:] # remove the leading "0b"
_UpperCamelCase = str(bin(a__ ) ... | 353 | import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transf... | 63 | 0 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...... | 354 | import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__ ( a__ , a__ , a__ ) ->int:
'''simple docstring'''
_UpperCame... | 63 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _UpperCAmelCase :
'''simple docstring'''
__A = 42
__A = None
__A = None
def lowerCAmelCase__ ( ) ... | 355 | import requests
from bsa import BeautifulSoup
def lowerCAmelCase__ ( a__ = "https://www.worldometers.info/coronavirus" ) ->dict:
'''simple docstring'''
_UpperCamelCase = BeautifulSoup(requests.get(a__ ).text , "html.parser" )
_UpperCamelCase = soup.findAll(... | 63 | 0 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCamelCase__ = 10
def lowerCAmelCase__ ( a__ , a__ , a__ , a__ ) ->int:
'''simple docstr... | 356 | import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaFo... | 63 | 0 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCamelCase__ = HfApi()
lowerCamelCase__ = {}
# fmt: off
lowerCamelCase__ = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0... | 357 | import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class _UpperC... | 63 | 0 |
from __future__ import annotations
from math import pow, sqrt
def lowerCAmelCase__ ( a__ , a__ , a__ ) ->dict[str, float]:
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistan... | 358 | def lowerCAmelCase__ ( a__ , a__ ) ->str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCamelCase = str(bin(a__ ) )[2:] # remove the leading "0b"
_UpperCamelCase = str(bin(a__ ) ... | 63 | 0 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM impo... | 359 | # This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase__ ( a__ , a__ , a__ , a_... | 63 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common i... | 360 | import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common i... | 63 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCAmelCase ( lowerCAmelCase, lowerCAmelCase ):
'''simple docstring'''
@regis... | 361 | from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...test_p... | 63 | 0 |
# Copyright 2023 The HuggingFace Inc. 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 appl... | 362 | from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
... | 63 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def lowerCAmelCase__ ( a__ , a__ , a__ ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase = AutoConfig.from_pretrained(a__ )
_UpperCamelCase ... | 363 | # Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _UpperCAmelCase ( Tenso... | 63 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
lowerCamelC... | 364 | import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {''... | 63 | 0 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse... | 365 | from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase ):
'''simple docstring'''
__A = ['''torch''']
def __init__( self : Any , *lowercase_ : Optional[int] , **lowercase_ : Optional[Any]) -> Opti... | 63 | 0 |
import unittest
import numpy as np
def lowerCAmelCase__ ( a__ , a__ , a__ , a__ = None , ) ->np.ndarray:
'''simple docstring'''
_UpperCamelCase = np.shape(a__ )
_UpperCamelCase = np.shape(a__ )
_UpperCamelCase = np.shape(a__ ... | 366 | import re
def lowerCAmelCase__ ( a__ ) ->str:
'''simple docstring'''
if len(re.findall("[ATCG]" , a__ ) ) != len(a__ ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , "TAGC" ) )
if __name__ == "__main__":
import doctes... | 63 | 0 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def lowerCAmelCase__ ( a__ , a__ , a__=1_024 , a__=1_024 , a__=False , **a__ ) ->int:
'''simple docstring'... | 367 | import os
def lowerCAmelCase__ ( ) ->Any:
'''simple docstring'''
with open(os.path.dirname(a__ ) + "/grid.txt" ) as f:
_UpperCamelCase = [] # noqa: E741
for _ in range(20 ):
l.append([int(a__ ) for x in f.readline().split()] )
_UpperCamelCase ... | 63 | 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__ = {
'''bert-base-uncased''': '''https://huggingfac... | 368 | from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docst... | 63 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_avai... | 369 | import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():... | 63 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCAmelCase__ ( a__ ) ->str:
'''simple docstring'''
if (
(cp >= 0X4e00 and cp <= 0X9fff)
or (cp >= 0X3400 and cp <= 0X4dbf) #
... | 370 | from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regress... | 63 | 0 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():... | 371 | import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''vocab_file''': '''vocab.json''',
'''merges_file... | 63 | 0 |
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_torc... | 350 | import math
class _UpperCAmelCase :
'''simple docstring'''
def __UpperCAmelCase ( self : Dict , lowercase_ : list[list[float]] , lowercase_ : list[int]) -> int:
"""simple docstring"""
_UpperCamelCase = 0.0
_UpperCamelCase ... | 63 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFI... | 351 | import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNet... | 63 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {''... | 352 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}
try:
if not is_tok... | 63 | 0 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
__A = (KDPMaDiscreteScheduler,)
__A = 10
def __UpperCAmelC... | 353 | import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transf... | 63 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docst... | 354 | import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__ ( a__ , a__ , a__ ) ->int:
'''simple docstring'''
_UpperCame... | 63 | 0 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = '''▁'''
lowerCamelCase__ ... | 355 | import requests
from bsa import BeautifulSoup
def lowerCAmelCase__ ( a__ = "https://www.worldometers.info/coronavirus" ) ->dict:
'''simple docstring'''
_UpperCamelCase = BeautifulSoup(requests.get(a__ ).text , "html.parser" )
_UpperCamelCase = soup.findAll(... | 63 | 0 |
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
lowerCamelCase__ = get_logger(__name__)
lowerCamelCase__ = R'''
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)... | 356 | import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaFo... | 63 | 0 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__ ( a__ , a__ , a__ ) ->List[str]:
'''simple docstring'''
_UpperCamelCase = TaC... | 357 | import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class _UpperC... | 63 | 0 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase__ : List[str] = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def lowerCAmelCase__ ( ) ->List[str]:
'''simple docstring'''
_UpperCamelCase = Github(os.envir... | 358 | def lowerCAmelCase__ ( a__ , a__ ) ->str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCamelCase = str(bin(a__ ) )[2:] # remove the leading "0b"
_UpperCamelCase = str(bin(a__ ) ... | 63 | 0 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCAmelCase ( lowerCAmelCase, lowerCAmelCase ):
'''simple docstring'''
@register_to_config
def __init__( self : Optional[Any] , ... | 359 | # This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase__ ( a__ , a__ , a__ , a_... | 63 | 0 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract... | 360 | import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common i... | 63 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''vocab_file''': '''vocab.json''',
... | 361 | from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...test_p... | 63 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...t... | 362 | from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
... | 63 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase__ = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '... | 363 | # Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _UpperCAmelCase ( Tenso... | 63 | 0 |
def lowerCAmelCase__ ( a__ ) ->str:
return "".join(chr(ord(a__ ) - 32 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 364 | import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {''... | 63 | 0 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from tra... | 365 | from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase ):
'''simple docstring'''
__A = ['''torch''']
def __init__( self : Any , *lowercase_ : Optional[int] , **lowercase_ : Optional[Any]) -> Opti... | 63 | 0 |
import numpy
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] , lowercase_ : numpy.ndarray , lowercase_ : numpy.ndarray) -> None:
"""simple docstring"""
_UpperCamelCase = input_array
# Random initial w... | 366 | import re
def lowerCAmelCase__ ( a__ ) ->str:
'''simple docstring'''
if len(re.findall("[ATCG]" , a__ ) ) != len(a__ ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , "TAGC" ) )
if __name__ == "__main__":
import doctes... | 63 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Tuple , lowercase_ : List[Any]=2 , lowercase_ : Dict=3 , lowercas... | 367 | import os
def lowerCAmelCase__ ( ) ->Any:
'''simple docstring'''
with open(os.path.dirname(a__ ) + "/grid.txt" ) as f:
_UpperCamelCase = [] # noqa: E741
for _ in range(20 ):
l.append([int(a__ ) for x in f.readline().split()] )
_UpperCamelCase ... | 63 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config''... | 368 | from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docst... | 63 | 0 |
from __future__ import annotations
def lowerCAmelCase__ ( a__ , a__ , a__ , ) ->tuple[str, float]:
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif stress < 0:
... | 369 | import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():... | 63 | 0 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowerCAmelCase__ ( a__ ) ->list[list[float]]:
'''simple docstring'''
_UpperCamelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only ... | 370 | from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regress... | 63 | 0 |
from __future__ import annotations
from typing import TypedDict
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
__A = 42
__A = 42
def lowerCAmelCase__ ( a__ ) ->list[str]:
'''simple docstring'''
if not isinstance(a__ , a__ ):
... | 371 | import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''vocab_file''': '''vocab.json''',
'''merges_file... | 63 | 0 |
"""simple docstring"""
import math
def lowercase_ ( _lowerCamelCase: int = 100 ) -> int:
'''simple docstring'''
__lowerCamelCase : Optional[int] = sum(i * i for i in range(1 , n + 1 ) )
__lowerCamelCase : Optional[int] = int(math... | 64 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-pre''': '''... | 64 | 1 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowercase_ ( _lowerCamelCase: Union[str, Any] , _lowerCamelCase: str , _lowerCamelCase: List[str] , _lowerCamelCase: Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
... | 64 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
class _snake_case ( a__ ):
snake_case__ = "bert-generation"
def __init__( self : Optional[int] , UpperCAmelCase : Dict=50358 , UpperCAmelCase : int=1024 , U... | 64 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https:/... | 64 | """simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
... | 64 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class _snake_case ... | 64 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__A = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. ... | 64 | 1 |
"""simple docstring"""
import math
def lowercase_ ( _lowerCamelCase: int ) -> list:
'''simple docstring'''
__lowerCamelCase : Any = [True] * n
__lowerCamelCase : Any = False
__lowerCamelCase : Dict = Fals... | 64 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils im... | 64 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise Optiona... | 64 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: int ) -> int:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("Input value must be an 'int' type" )
__lowerCamelCase : Dict = 0
... | 64 | 1 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _snake_case ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
snake_case__ = ... | 64 | """simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad... | 64 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
'''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', '''M2M100OnnxConfig'''],
'''t... | 64 | """simple docstring"""
from typing import Any
import numpy as np
def lowercase_ ( _lowerCamelCase: np.ndarray ) -> bool:
'''simple docstring'''
return np.array_equal(_lowerCamelCase , matrix.conjugate().T )
def lowercase_ ( _lowerCamelCase: np.ndarray , _lowerCa... | 64 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _snake_case ( a__ ... | 64 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['... | 64 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_pa... | 64 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https:/... | 64 | 1 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_conf... | 64 | """simple docstring"""
import requests
__A = '''YOUR API KEY'''
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: str = giphy_api_key ) -> list:
'''simple docstring'''
__lowerCamelCase : Dict = "+".join(query.split() )
__lowerCamelCase ... | 64 | 1 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _snake_case ... | 64 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class _snake_case ... | 64 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaF... | 64 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: int = 4000000 ) -> int:
'''simple docstring'''
__lowerCamelCase : Tuple = [0, 1]
__lowerCamelCase : Union[str, Any] = 0
while fib[i] <= n:
fib.append(fib[i] ... | 64 | 1 |
"""simple docstring"""
import functools
from typing import Any
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: list[str] ) -> bool:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or len(_lowerCamelCase ) == 0:
raise ValueE... | 64 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: int = 100 ) -> int:
'''simple docstring'''
__lowerCamelCase : Optional[Any] = set()
__lowerCamelCase : Union[str, Any] = 0
__lowerCamelCase : Optional[Any] ... | 64 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQF... | 64 | """simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: str ) -> str | Literal[False]:
'''simple docstring'''
__lowerCamelCase : Optional[int] ... | 64 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# See all BioGPT models at https://huggingface.co... | 64 | """simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: Optional[int]=7 ) -> int:
'''simple docstring'''
__lowerCamelCase : List[st... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
__A = 100
__A = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__A = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.differen... | 64 | """simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise Optiona... | 64 | 1 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from t... | 64 | """simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decor... | 64 | 1 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf,... | 64 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( a__ ):
def __init__( self : Option... | 64 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
... | 64 | """simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''google/efficie... | 64 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
'''microsoft/markuplm-large''': '''... | 64 | """simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_r... | 64 | 1 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta impor... | 64 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 64 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''distilbert-base-uncased''': '''https://huggingface.... | 64 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-pre''': '''... | 64 | 1 |
"""simple docstring"""
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 im... | 64 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
class _snake_case ( a__ ):
snake_case__ = "bert-generation"
def __init__( self : Optional[int] , UpperCAmelCase : Dict=50358 , UpperCAmelCase : int=1024 , U... | 64 | 1 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def lowercase_ ( _lowerCamelCase: Dict , _lowerCamelCase: str ) -> Any:
'''simple docstring'''
... | 64 | """simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
... | 64 | 1 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__A = logging.getLogg... | 64 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__A = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. ... | 64 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _snake_case ( a__ ):
snake_case__ = ["image_processor", "tokenizer"]
snake_case__ = "ViTImageProcessor"
snake_c... | 64 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils im... | 64 | 1 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import... | 64 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: int ) -> int:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("Input value must be an 'int' type" )
__lowerCamelCase : Dict = 0
... | 64 | 1 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def lowercase_ ( _lowerCamelCase: Iterable[str] , _lowerCamelCase: int ) -> Generator[tuple[str, ...], None, None]:
'''simple docstring'''
__lowerCamelCase : int ... | 64 | """simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad... | 64 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 64 | """simple docstring"""
from typing import Any
import numpy as np
def lowercase_ ( _lowerCamelCase: np.ndarray ) -> bool:
'''simple docstring'''
return np.array_equal(_lowerCamelCase , matrix.conjugate().T )
def lowercase_ ( _lowerCamelCase: np.ndarray , _lowerCa... | 64 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeTowerConfig''',
... | 64 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: float | Decimal , _lowerCamelCase: float = 10**-10 ) -> float:
'''simple docstrin... | 64 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https:/... | 64 | 1 |
"""simple docstring"""
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 lowercase_ ( _lowerCamelCase: Union[str, Any] ) -> str:
'''simple d... | 64 | """simple docstring"""
import requests
__A = '''YOUR API KEY'''
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: str = giphy_api_key ) -> list:
'''simple docstring'''
__lowerCamelCase : Dict = "+".join(query.split() )
__lowerCamelCase ... | 64 | 1 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_r... | 64 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class _snake_case ... | 64 | 1 |
"""simple docstring"""
import sys
__A = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''668966489504452... | 64 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: int = 4000000 ) -> int:
'''simple docstring'''
__lowerCamelCase : Tuple = [0, 1]
__lowerCamelCase : Union[str, Any] = 0
while fib[i] <= n:
fib.append(fib[i] ... | 64 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/... | 64 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: int = 100 ) -> int:
'''simple docstring'''
__lowerCamelCase : Optional[Any] = set()
__lowerCamelCase : Union[str, Any] = 0
__lowerCamelCase : Optional[Any] ... | 64 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:
if n... | 64 | """simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: str ) -> str | Literal[False]:
'''simple docstring'''
__lowerCamelCase : Optional[int] ... | 64 | 1 |
"""simple docstring"""
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
... | 64 | """simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: Optional[int]=7 ) -> int:
'''simple docstring'''
__lowerCamelCase : List[st... | 64 | 1 |
"""simple docstring"""
def lowercase_ ( _lowerCamelCase: Any ) -> str:
'''simple docstring'''
__lowerCamelCase : List[str] = len(_lowerCamelCase )
for i in range(length - 1 ):
__lowerCamelCase : Union[str, Any] = i
... | 64 | """simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise Optiona... | 64 | 1 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _snake_case ( unittest.TestCase ):
def lowerCamelCase__ ( self : Union[str, Any] ):
debug_l... | 64 | """simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decor... | 64 | 1 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_s... | 64 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( a__ ):
def __init__( self : Option... | 64 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_ba... | 64 | """simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''google/efficie... | 64 | 1 |
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