
import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE							:Dict										=    'docs/source/en/_toctree.yml'


def 						UpperCAmelCase      (     a_						)		->     Optional[int]:







				"""simple docstring"""



				__A             =			defaultdict(a_						)
				__A             =			[]
				__A             =			[]
				for doc in doc_list:
								if "local" in doc:
												counts[doc["local"]] += 1

								if doc["title"].lower() == "overview":
												overview_doc.append({"local": doc["local"], "title": doc["title"]}						)
								else:
												new_doc_list.append(a_						)

				__A             =			new_doc_list
				__A             =			[key for key, value in counts.items() if value > 1]

				__A             =			[]
				for duplicate_key in duplicates:
								__A             =			list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key}						)
								if len(a_						) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
												    "others."						)
								# Only add this once
								new_doc.append({"local": duplicate_key, "title": titles[0]}						)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1]						)
				__A             =			sorted(a_		,       key=lambda a_						: s["title"].lower()						)

				# "overview" gets special treatment and is always first
				if len(a_						) > 1:
								raise ValueError("{doc_list} has two 'overview' docs which is not allowed."						)

				overview_doc.extend(a_						)

				# Sort
				return overview_doc


def 						UpperCAmelCase      (     a_=False						)		->     List[str]:







				"""simple docstring"""



				with open(a_		,       encoding="utf-8"						) as f:
								__A             =			yaml.safe_load(f.read()						)

				# Get to the API doc
				__A             =			0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__A             =			content[api_idx]["sections"]

				# Then to the model doc
				__A             =			0
				while api_doc[scheduler_idx]["title"] != "Schedulers":
								scheduler_idx += 1

				__A             =			api_doc[scheduler_idx]["sections"]
				__A             =			clean_doc_toc(a_						)

				__A             =			False
				if new_scheduler_doc != scheduler_doc:
								__A             =			True
								if overwrite:
												__A             =			new_scheduler_doc

				if diff:
								if overwrite:
												__A             =			api_doc
												with open(a_		,       "w"		,       encoding="utf-8"						) as f:
																f.write(yaml.dump(a_		,       allow_unicode=a_						)						)
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."						)


def 						UpperCAmelCase      (     a_=False						)		->     List[Any]:







				"""simple docstring"""



				with open(a_		,       encoding="utf-8"						) as f:
								__A             =			yaml.safe_load(f.read()						)

				# Get to the API doc
				__A             =			0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__A             =			content[api_idx]["sections"]

				# Then to the model doc
				__A             =			0
				while api_doc[pipeline_idx]["title"] != "Pipelines":
								pipeline_idx += 1

				__A             =			False
				__A             =			api_doc[pipeline_idx]["sections"]
				__A             =			[]

				# sort sub pipeline docs
				for pipeline_doc in pipeline_docs:
								if "section" in pipeline_doc:
												__A             =			pipeline_doc["section"]
												__A             =			clean_doc_toc(a_						)
												if overwrite:
																__A             =			new_sub_pipeline_doc
								new_pipeline_docs.append(a_						)

				# sort overall pipeline doc
				__A             =			clean_doc_toc(a_						)

				if new_pipeline_docs != pipeline_docs:
								__A             =			True
								if overwrite:
												__A             =			new_pipeline_docs

				if diff:
								if overwrite:
												__A             =			api_doc
												with open(a_		,       "w"		,       encoding="utf-8"						) as f:
																f.write(yaml.dump(a_		,       allow_unicode=a_						)						)
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					check_scheduler_doc(args.fix_and_overwrite)
					check_pipeline_doc(args.fix_and_overwrite)


# Copyright 2021 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 applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


SCREAMING_SNAKE_CASE							:Tuple										=    'pytorch_model.bin'
SCREAMING_SNAKE_CASE							:str										=    'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE							:int										=    'adapter_config.json'
SCREAMING_SNAKE_CASE							:List[str]										=    'adapter_model.bin'
SCREAMING_SNAKE_CASE							:Any										=    'adapter_model.safetensors'
SCREAMING_SNAKE_CASE							:int										=    'tf_model.h5'
SCREAMING_SNAKE_CASE							:Tuple										=    'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE							:List[Any]										=    'model.ckpt'
SCREAMING_SNAKE_CASE							:Optional[int]										=    'flax_model.msgpack'
SCREAMING_SNAKE_CASE							:List[Any]										=    'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE							:List[Any]										=    'model.safetensors'
SCREAMING_SNAKE_CASE							:Any										=    'model.safetensors.index.json'
SCREAMING_SNAKE_CASE							:int										=    'config.json'
SCREAMING_SNAKE_CASE							:List[str]										=    'preprocessor_config.json'
SCREAMING_SNAKE_CASE							:Optional[int]										=    FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE							:Optional[Any]										=    'generation_config.json'
SCREAMING_SNAKE_CASE							:Dict										=    'modelcard.json'

SCREAMING_SNAKE_CASE							:Optional[Any]										=    '▁'
SCREAMING_SNAKE_CASE							:Any										=    SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

SCREAMING_SNAKE_CASE							:Tuple										=    [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE							:Union[str, Any]										=    [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE							:Tuple										=    [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]


def 						UpperCAmelCase      (     a_						)		->     List[str]:







				"""simple docstring"""



				if version.parse(a_						) < version.parse(a_						):
								if "dev" in min_version:
												__A             =			(
												    "This example requires a source install from HuggingFace Transformers (see "
												    "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
												)
								else:
												__A             =			F'''This example requires a minimum version of {min_version},'''
								error_message += F''' but the version found is {__version__}.\n'''
								raise ImportError(
								    error_message
								    + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
								    "versions of HuggingFace Transformers."						)
