code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulat... | 50 |
'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowercase : str = logging.get_logger(__name_... | 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Any = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try... | 50 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( ):
'''simple docstring'''
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def lowerCamelCase__ ( A : List[str] ):
'''simple docstring'''
UpperCAmelCase = 1
Upper... | 50 |
'''simple docstring'''
import functools
def lowerCamelCase__ ( A : list[int] , A : list[int] ):
'''simple docstring'''
if not isinstance(A , A ) or not all(isinstance(A , A ) for day in days ):
raise ValueError('''The parameter days shou... | 50 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_lowercase : Optional[int] = """examples/"""
_lowercase : str = {
"""examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
... | 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Any = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try... | 50 | 1 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
# TODO Update this
_lowercase : int = {
"""f... | 50 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCamelCase__ ( A : Optional[Any] , A : Tuple=1 ):
'''simple docstring'''
if n_shave_prefix_segments >= 0:
r... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : List[Any] ):
'''simple docstring'''
UpperCAmelCase = 1
UpperCAmelCase = 2
while i * i <= n:
UpperCAmelCase = 0
while n % i == 0:
n //= i
... | 50 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Dict = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encod... | 50 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
... | 50 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 50 | 1 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTo... | 50 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : Tuple = ["image_processor", "tokenizer"]
__magic_name__ : Any = "ViT... | 50 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipeli... | 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Any = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
... | 50 | 1 |
'''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 lowerCamelCase__ ( A : ... | 50 |
'''simple docstring'''
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 acceler... | 50 | 1 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoencod... | 50 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase : Tuple = {
"""configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CO... | 50 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : List[str] = {
"""facebook/encodec_24khz""... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : List[Any] , A : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = [0 for i in range(r + 1 )]
# nc0 = 1
UpperCAmelCase = 1
for i in range(1 , n + 1 ):
... | 50 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/lic... | 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase : int = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig"""... | 50 |
'''simple docstring'''
import heapq
def lowerCamelCase__ ( A : dict ):
'''simple docstring'''
UpperCAmelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Prior... | 50 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_lowercase : Dict = """docs/source/en/_toctree.yml"""
def lowerCamelCase__ ( A : Any ):
'''simple docstring'''
UpperCAmelCase = defaultdict(A )
UpperCAmelCa... | 50 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_lowercase : Optional[int] = """examples/"""
_lowercase : str = {
"""examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
... | 50 | 1 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def lowerCamelCase__ ( A : dict ):
'''s... | 50 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,... | 50 | 1 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowercase : List[str] = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Transl... | 50 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__( metaclass=lowerCAmelCase ):
__magic_name__ : List[str] = ["note_seq"]
def __init__( self : Any , *lowerCAmelCase : List[str] , **lowerCAmelCase : int... | 50 | 1 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
_lowercase : List[Any] = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
_lowercase ... | 50 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A : List[Any] , A : int , A ... | 50 | 1 |
'''simple docstring'''
import math
def lowerCamelCase__ ( A : int ):
'''simple docstring'''
UpperCAmelCase = [True] * n
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = True
for i in range(3 , ... | 50 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase__( unittest.TestCase ):
... | 50 | 1 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def lowerCamelCase__ ( A : int ):
'''simple docstring'''
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __n... | 50 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCamelCase__ ( A : List[Any] ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
... | 50 | 1 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,... | 50 |
'''simple docstring'''
def lowerCamelCase__ ( A : str ):
'''simple docstring'''
assert column_title.isupper()
UpperCAmelCase = 0
UpperCAmelCase = len(A ) - 1
UpperCAmelCase = 0
while index >= 0:
UpperCAmelCase ... | 50 | 1 |
'''simple docstring'''
# 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 : Op... | 50 |
'''simple docstring'''
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... | 50 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_lowercase : ... | 50 |
'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowercase : str = logging.get_logger(__name_... | 50 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
_lowercase : List[Any] = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/m... | 50 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 50 | 1 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.da... | 50 |
'''simple docstring'''
import functools
def lowerCamelCase__ ( A : list[int] , A : list[int] ):
'''simple docstring'''
if not isinstance(A , A ) or not all(isinstance(A , A ) for day in days ):
raise ValueError('''The parameter days shou... | 50 | 1 |
'''simple docstring'''
from collections.abc import Generator
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = 0, 1
while True:
UpperCAmelCase , UpperCAmelCase = b, a + b
yield b... | 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Any = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try... | 50 | 1 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
_lowercase : List[str] = """
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.
"""
_lowercase : str ... | 50 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCamelCase__ ( A : Optional[Any] , A : Tuple=1 ):
'''simple docstring'''
if n_shave_prefix_segments >= 0:
r... | 50 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( A : int ):
'''simple docstring'''
UpperCAmelCase = 2
UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 50 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Dict = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encod... | 50 | 1 |
'''simple docstring'''
from functools import reduce
_lowercase : int = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""125406987471585238630507156932909632... | 50 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 50 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__( metaclass=lowerCAmelCase ):
__magic_name__ : int = ["torch", "transformers", "onnx"]
def __init__( self : int , *lowerCAmelCase : Any , **lowerCAmelCase ... | 50 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : Tuple = ["image_processor", "tokenizer"]
__magic_name__ : Any = "ViT... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : int ):
'''simple docstring'''
UpperCAmelCase = int(A )
if decimal in (0, 1): # Exit cases for the recursion
return str(A )
UpperCAmelCase , UpperCAmelCase = divmod(A , ... | 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Any = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
... | 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowercase : List[str] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotA... | 50 |
'''simple docstring'''
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 acceler... | 50 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCamelCase__( unittest.TestCase ):
def a__( self : Optional[int] )-> List[Any]:
"""simple ... | 50 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 50 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase : int = [
"""word... | 50 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : List[str] = {
"""facebook/encodec_24khz""... | 50 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
_lowercase : Optional[int] = r"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be use... | 50 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/lic... | 50 | 1 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_lowercase : Any = pytest.mark.integration
@pytest.mark.parametrize(''... | 50 |
'''simple docstring'''
import heapq
def lowerCamelCase__ ( A : dict ):
'''simple docstring'''
UpperCAmelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Prior... | 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : str = {
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
"""token... | 50 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_lowercase : Optional[int] = """examples/"""
_lowercase : str = {
"""examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : str ):
'''simple docstring'''
if n_term == "":
return []
UpperCAmelCase = []
for temp in range(int(A ) ):
series.append(f"""1/{temp + 1}""" if series else '''1''' )
return se... | 50 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,... | 50 | 1 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCamelCase__:
def __init__( self : Union[str, Any] , lowerCAmelCase : list[tuple[float, float]] )-> Any:
"""simple docstring"""
UpperCAme... | 50 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__( metaclass=lowerCAmelCase ):
__magic_name__ : List[str] = ["note_seq"]
def __init__( self : Any , *lowerCAmelCase : List[str] , **lowerCAmelCase : int... | 50 | 1 |
'''simple docstring'''
from statistics import mean
import numpy as np
def lowerCamelCase__ ( A : list , A : list , A : list , A : int ):
'''simple docstring'''
UpperCAmelCase = 0
# Number of processes finished
Upper... | 50 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A : List[Any] , A : int , A ... | 50 | 1 |
'''simple docstring'''
import socket
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
UpperCAmelCase = socket.gethostname()
UpperCAmelCase = 1_23_12
sock.connec... | 50 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase__( unittest.TestCase ):
... | 50 | 1 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTest... | 50 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCamelCase__ ( A : List[Any] ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
... | 50 | 1 |
'''simple docstring'''
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... | 50 |
'''simple docstring'''
def lowerCamelCase__ ( A : str ):
'''simple docstring'''
assert column_title.isupper()
UpperCAmelCase = 0
UpperCAmelCase = len(A ) - 1
UpperCAmelCase = 0
while index >= 0:
UpperCAmelCase ... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : list[int] ):
'''simple docstring'''
UpperCAmelCase = []
if len(A ) == 1:
return [nums.copy()]
for _ in range(len(A ) ):
UpperCAmelCase = nums.pop(0 )
UpperCA... | 50 |
'''simple docstring'''
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... | 50 | 1 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
f... | 50 |
'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowercase : str = logging.get_logger(__name_... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : str ):
'''simple docstring'''
UpperCAmelCase = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCAmelCase = ''''''
UpperCAmelCase = ''''''
# append eac... | 50 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 50 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__( unittest.TestC... | 50 |
'''simple docstring'''
import functools
def lowerCamelCase__ ( A : list[int] , A : list[int] ):
'''simple docstring'''
if not isinstance(A , A ) or not all(isinstance(A , A ) for day in days ):
raise ValueError('''The parameter days shou... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : int , A : int ):
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Any = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try... | 50 | 1 |
'''simple docstring'''
from math import factorial
def lowerCamelCase__ ( A : int = 20 ):
'''simple docstring'''
UpperCAmelCase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCAmelCase = n // 2
... | 50 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCamelCase__ ( A : Optional[Any] , A : Tuple=1 ):
'''simple docstring'''
if n_shave_prefix_segments >= 0:
r... | 50 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
fro... | 50 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Dict = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encod... | 50 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class UpperCamelCase__( lowerCAmelCase ):
def __init__( self : Dict )-> Dict:
"""simple docstring"""
self.test()
def a__( self : Tuple )-> str:
... | 50 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : str , A : str ):
'''simple docstring'''
if len(A ) != len(A ):
raise ValueError('''String lengths must match!''' )
UpperCAmelCase = 0
for chara, chara in zip(A , A ):
... | 50 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : Tuple = ["image_processor", "tokenizer"]
__magic_name__ : Any = "ViT... | 50 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TF... | 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Any = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
... | 50 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_l... | 50 |
'''simple docstring'''
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 acceler... | 50 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils ... | 50 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 50 | 1 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_lowerc... | 50 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : List[str] = {
"""facebook/encodec_24khz""... | 50 | 1 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ... | 50 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/lic... | 50 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_lowercase : str = logging.get_log... | 50 |
'''simple docstring'''
import heapq
def lowerCamelCase__ ( A : dict ):
'''simple docstring'''
UpperCAmelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Prior... | 50 | 1 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : Dict = (EulerDiscreteScheduler,)
__magic_name__ ... | 50 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_lowercase : Optional[int] = """examples/"""
_lowercase : str = {
"""examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : str , A : bool = False ):
'''simple docstring'''
if not isinstance(A , A ):
UpperCAmelCase = f"""Expected string as input, found {type(A )}"""
raise ValueError(A )
if ... | 50 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,... | 50 | 1 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_lowercase : Optional[int] = [
# tf -> hf
("""/""", """."""),
("""layer_""",... | 50 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__( metaclass=lowerCAmelCase ):
__magic_name__ : List[str] = ["note_seq"]
def __init__( self : Any , *lowerCAmelCase : List[str] , **lowerCAmelCase : int... | 50 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : Tuple = {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-s... | 50 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A : List[Any] , A : int , A ... | 50 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : Tuple = ["image_processor", "tokenizer"]
__magic_name__ : Any = "ViT... | 50 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase__( unittest.TestCase ):
... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : int , A : int ):
'''simple docstring'''
return number | (1 << position)
def lowerCamelCase__ ( A : int , A : int ):
'''simple docstring'''
return number & ~(1 << posi... | 50 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCamelCase__ ( A : List[Any] ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
... | 50 | 1 |
'''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
_lowercase : Any = logging.get_logger(__name__)
_lowercase ... | 50 |
'''simple docstring'''
def lowerCamelCase__ ( A : str ):
'''simple docstring'''
assert column_title.isupper()
UpperCAmelCase = 0
UpperCAmelCase = len(A ) - 1
UpperCAmelCase = 0
while index >= 0:
UpperCAmelCase ... | 50 | 1 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distrib... | 50 |
'''simple docstring'''
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... | 50 | 1 |
'''simple docstring'''
# 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.... | 50 |
'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowercase : str = logging.get_logger(__name_... | 50 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
V... | 50 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : int , A : int ):
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def lowerCamelCase__ ( ):
'''simple docstring'''
assert or_gate(0 , 0 ) == 0
assert o... | 50 |
'''simple docstring'''
import functools
def lowerCamelCase__ ( A : list[int] , A : list[int] ):
'''simple docstring'''
if not isinstance(A , A ) or not all(isinstance(A , A ) for day in days ):
raise ValueError('''The parameter days shou... | 50 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cas... | 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Any = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try... | 50 | 1 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_avai... | 50 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCamelCase__ ( A : Optional[Any] , A : Tuple=1 ):
'''simple docstring'''
if n_shave_prefix_segments >= 0:
r... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : int ):
'''simple docstring'''
UpperCAmelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowerCamelCase__ ( A : int = 50_00 ):
'''simple docstring'''
UpperCAmelC... | 50 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Dict = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encod... | 50 | 1 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.... | 50 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 50 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 50 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : Tuple = ["image_processor", "tokenizer"]
__magic_name__ : Any = "ViT... | 50 | 1 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
_lowercase : List[Any] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", "... | 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Any = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : list[int] , A : list[int] ):
'''simple docstring'''
UpperCAmelCase = len(A )
print('''The following activities are selected:''' )
# The first activity is always selected
UpperCAmelCase ... | 50 |
'''simple docstring'''
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 acceler... | 50 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ ( A : str = "AAPL" ):
'''simple docstring'''
UpperCAmelCase = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
UpperCAmelCase = BeautifulSoup(reques... | 50 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Dict = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],... | 50 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : List[str] = {
"""facebook/encodec_24khz""... | 50 | 1 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
_lowercase : Dict = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0,... | 50 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/lic... | 50 | 1 |
'''simple docstring'''
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def lowerCamelCase__ ( A : bool = True , *A : Tuple , **A : Tuple ):
'''simple docstring'''
... | 50 |
'''simple docstring'''
import heapq
def lowerCamelCase__ ( A : dict ):
'''simple docstring'''
UpperCAmelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Prior... | 50 | 1 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class UpperCamelCase__( unittest.TestCase ):
def a__( self : int )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = [
... | 50 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_lowercase : Optional[int] = """examples/"""
_lowercase : str = {
"""examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
... | 50 | 1 |
'''simple docstring'''
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 acceler... | 50 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,... | 50 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_visio... | 50 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__( metaclass=lowerCAmelCase ):
__magic_name__ : List[str] = ["note_seq"]
def __init__( self : Any , *lowerCAmelCase : List[str] , **lowerCAmelCase : int... | 50 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Tuple = logging.get_logger(__name__)
_lowercase : int = {
"""bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/m... | 50 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A : List[Any] , A : int , A ... | 50 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
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, pre... | 50 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase__( unittest.TestCase ):
... | 50 | 1 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_avail... | 50 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCamelCase__ ( A : List[Any] ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
... | 50 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase = {}
UpperCAmelCase = 2
while True:
UpperCAmelCase = factor_map.pop(A ... | 50 |
'''simple docstring'''
def lowerCamelCase__ ( A : str ):
'''simple docstring'''
assert column_title.isupper()
UpperCAmelCase = 0
UpperCAmelCase = len(A ) - 1
UpperCAmelCase = 0
while index >= 0:
UpperCAmelCase ... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : int ):
'''simple docstring'''
if number < 0:
raise ValueError('''number must not be negative''' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 |
'''simple docstring'''
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... | 50 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from u... | 50 |
'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowercase : str = logging.get_logger(__name_... | 50 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
... | 50 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 50 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_lowercase : Optional[Any] = logging.get_logger("""transformers.models.speecht5""")
def lowerCamelCase__ ( A : ... | 50 |
'''simple docstring'''
import functools
def lowerCamelCase__ ( A : list[int] , A : list[int] ):
'''simple docstring'''
if not isinstance(A , A ) or not all(isinstance(A , A ) for day in days ):
raise ValueError('''The parameter days shou... | 50 | 1 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class UpperCamelCase__( lowerCAmelCase ):
def __init__( self : Tuple , lowerCAmelCase : Optional[int] , lowerCAmelCase : Dict )-> int:
"""simple docstring"""
su... | 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Any = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try... | 50 | 1 |
'''simple docstring'''
import functools
def lowerCamelCase__ ( A : list[int] , A : list[int] ):
'''simple docstring'''
if not isinstance(A , A ) or not all(isinstance(A , A ) for day in days ):
raise ValueError('''The parameter days shou... | 50 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCamelCase__ ( A : Optional[Any] , A : Tuple=1 ):
'''simple docstring'''
if n_shave_prefix_segments >= 0:
r... | 50 | 1 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf... | 50 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Dict = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encod... | 50 | 1 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCamelCase__:
def a__( self : Tuple , lowerCAmelCase : List[str] )-> str:
"""simple docstring"""
... | 50 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 50 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_lowercase : str = {
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie... | 50 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : Tuple = ["image_processor", "tokenizer"]
__magic_name__ : Any = "ViT... | 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Any = {
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLIPTextCon... | 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Any = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
... | 50 | 1 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 50 |
'''simple docstring'''
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 acceler... | 50 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class UpperCamelCase__( lowerCAmelCase ):
# `task` is not a ClassVar since we want it... | 50 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 50 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : str , A : int ):
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(A ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 50 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : List[str] = {
"""facebook/encodec_24khz""... | 50 | 1 |
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