
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowercase_       :						Dict     					=	logging.get_logger(__name__)

lowercase_       :						Optional[Any]     					=	OrderedDict(
    [
        ('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
        ('''beit''', '''BeitFeatureExtractor'''),
        ('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
        ('''clap''', '''ClapFeatureExtractor'''),
        ('''clip''', '''CLIPFeatureExtractor'''),
        ('''clipseg''', '''ViTFeatureExtractor'''),
        ('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
        ('''convnext''', '''ConvNextFeatureExtractor'''),
        ('''cvt''', '''ConvNextFeatureExtractor'''),
        ('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
        ('''data2vec-vision''', '''BeitFeatureExtractor'''),
        ('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
        ('''deit''', '''DeiTFeatureExtractor'''),
        ('''detr''', '''DetrFeatureExtractor'''),
        ('''dinat''', '''ViTFeatureExtractor'''),
        ('''donut-swin''', '''DonutFeatureExtractor'''),
        ('''dpt''', '''DPTFeatureExtractor'''),
        ('''encodec''', '''EncodecFeatureExtractor'''),
        ('''flava''', '''FlavaFeatureExtractor'''),
        ('''glpn''', '''GLPNFeatureExtractor'''),
        ('''groupvit''', '''CLIPFeatureExtractor'''),
        ('''hubert''', '''Wav2Vec2FeatureExtractor'''),
        ('''imagegpt''', '''ImageGPTFeatureExtractor'''),
        ('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
        ('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
        ('''levit''', '''LevitFeatureExtractor'''),
        ('''maskformer''', '''MaskFormerFeatureExtractor'''),
        ('''mctct''', '''MCTCTFeatureExtractor'''),
        ('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
        ('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
        ('''mobilevit''', '''MobileViTFeatureExtractor'''),
        ('''nat''', '''ViTFeatureExtractor'''),
        ('''owlvit''', '''OwlViTFeatureExtractor'''),
        ('''perceiver''', '''PerceiverFeatureExtractor'''),
        ('''poolformer''', '''PoolFormerFeatureExtractor'''),
        ('''regnet''', '''ConvNextFeatureExtractor'''),
        ('''resnet''', '''ConvNextFeatureExtractor'''),
        ('''segformer''', '''SegformerFeatureExtractor'''),
        ('''sew''', '''Wav2Vec2FeatureExtractor'''),
        ('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
        ('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
        ('''speecht5''', '''SpeechT5FeatureExtractor'''),
        ('''swiftformer''', '''ViTFeatureExtractor'''),
        ('''swin''', '''ViTFeatureExtractor'''),
        ('''swinv2''', '''ViTFeatureExtractor'''),
        ('''table-transformer''', '''DetrFeatureExtractor'''),
        ('''timesformer''', '''VideoMAEFeatureExtractor'''),
        ('''tvlt''', '''TvltFeatureExtractor'''),
        ('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
        ('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
        ('''van''', '''ConvNextFeatureExtractor'''),
        ('''videomae''', '''VideoMAEFeatureExtractor'''),
        ('''vilt''', '''ViltFeatureExtractor'''),
        ('''vit''', '''ViTFeatureExtractor'''),
        ('''vit_mae''', '''ViTFeatureExtractor'''),
        ('''vit_msn''', '''ViTFeatureExtractor'''),
        ('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
        ('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
        ('''whisper''', '''WhisperFeatureExtractor'''),
        ('''xclip''', '''CLIPFeatureExtractor'''),
        ('''yolos''', '''YolosFeatureExtractor'''),
    ]
)

lowercase_       :						Union[str, Any]     					=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Dict:
  for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
    if class_name in extractors:
      _SCREAMING_SNAKE_CASE   :				Any							    = model_type_to_module_name(lowerCamelCase__				)

      _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = importlib.import_module(f'''.{module_name}''',						"transformers.models"				)
      try:
        return getattr(lowerCamelCase__,						lowerCamelCase__				)
      except AttributeError:
        continue

  for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
    if getattr(lowerCamelCase__,						"__name__",						lowerCamelCase__				) == class_name:
      return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  _SCREAMING_SNAKE_CASE   :				List[str]							    = importlib.import_module("transformers"				)
  if hasattr(lowerCamelCase__,						lowerCamelCase__				):
    return getattr(lowerCamelCase__,						lowerCamelCase__				)

  return None





def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, os.PathLike],						lowerCamelCase__							:							Optional[Union[str, os.PathLike]] = None,						lowerCamelCase__							:							bool = False,						lowerCamelCase__							:							bool = False,						lowerCamelCase__							:							Optional[Dict[str, str]] = None,						lowerCamelCase__							:							Optional[Union[bool, str]] = None,						lowerCamelCase__							:							Optional[str] = None,						lowerCamelCase__							:							bool = False,						**lowerCamelCase__							:							Union[str, Any],						)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				List[str]							    = get_file_from_repo(
      lowerCamelCase__,						lowerCamelCase__,						cache_dir=lowerCamelCase__,						force_download=lowerCamelCase__,						resume_download=lowerCamelCase__,						proxies=lowerCamelCase__,						use_auth_token=lowerCamelCase__,						revision=lowerCamelCase__,						local_files_only=lowerCamelCase__,						)
  if resolved_config_file is None:
    logger.info(
        "Could not locate the feature extractor configuration file, will try to use the model config instead."				)
    return {}

  with open(lowerCamelCase__,						encoding="utf-8"				) as reader:
    return json.load(lowerCamelCase__				)





class 			UpperCamelCase      :
     def __init__(     self				):




       """simple docstring"""


       raise EnvironmentError(
           "AutoFeatureExtractor is designed to be instantiated "
           "using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."				)
     @classmethod
     @replace_list_option_in_docstrings(snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     cls  ,     snake_case__  ,     **snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = kwargs.pop("config"  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = kwargs.pop("trust_remote_code"  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				str							    = True

       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = FeatureExtractionMixin.get_feature_extractor_dict(snake_case__  ,     **snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = config_dict.get("feature_extractor_type"  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = None
       if "AutoFeatureExtractor" in config_dict.get("auto_map"  ,     {}				):
         _SCREAMING_SNAKE_CASE   :				int							    = config_dict["auto_map"]["AutoFeatureExtractor"]

       # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
       if feature_extractor_class is None and feature_extractor_auto_map is None:
         if not isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = AutoConfig.from_pretrained(snake_case__  ,     **snake_case__				)
         # It could be in `config.feature_extractor_type``
         _SCREAMING_SNAKE_CASE   :				List[str]							    = getattr(snake_case__  ,     "feature_extractor_type"  ,     snake_case__				)
         if hasattr(snake_case__  ,     "auto_map"				) and "AutoFeatureExtractor" in config.auto_map:
           _SCREAMING_SNAKE_CASE   :				str							    = config.auto_map["AutoFeatureExtractor"]

       if feature_extractor_class is not None:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = feature_extractor_class_from_name(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = feature_extractor_auto_map is not None
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = feature_extractor_class is not None or type(snake_case__				) in FEATURE_EXTRACTOR_MAPPING
       _SCREAMING_SNAKE_CASE   :				int							    = resolve_trust_remote_code(
           snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)

       if has_remote_code and trust_remote_code:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = get_class_from_dynamic_module(
             snake_case__  ,     snake_case__  ,     **snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = kwargs.pop("code_revision"  ,     snake_case__				)
         if os.path.isdir(snake_case__				):
           feature_extractor_class.register_for_auto_class()
         return feature_extractor_class.from_dict(snake_case__  ,     **snake_case__				)
       elif feature_extractor_class is not None:
         return feature_extractor_class.from_dict(snake_case__  ,     **snake_case__				)
       # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
       elif type(snake_case__				) in FEATURE_EXTRACTOR_MAPPING:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = FEATURE_EXTRACTOR_MAPPING[type(snake_case__				)]
         return feature_extractor_class.from_dict(snake_case__  ,     **snake_case__				)

       raise ValueError(
           F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
           F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
           F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()				)}'''				)




     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     snake_case__  ,     snake_case__				):




       """simple docstring"""


       FEATURE_EXTRACTOR_MAPPING.register(snake_case__  ,     snake_case__				)





"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/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.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowercase_       :						Tuple     					=	[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							int=None,						lowerCamelCase__							:							Any=None,						lowerCamelCase__							:							Any=None				)  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = True
  while ask_again:
    _SCREAMING_SNAKE_CASE   :				List[str]							    = input(lowerCamelCase__				)
    try:
      if default is not None and len(lowerCamelCase__				) == 0:
        return default
      return convert_value(lowerCamelCase__				) if convert_value is not None else result
    except Exception:
      if error_message is not None:
        print(lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Dict=[],						lowerCamelCase__							:							Optional[int]=None,						lowerCamelCase__							:							str=0				)  ->    str:
  _SCREAMING_SNAKE_CASE   :				int							    = BulletMenu(lowerCamelCase__,						lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = menu.run(default_choice=lowerCamelCase__				)
  return convert_value(lowerCamelCase__				) if convert_value is not None else result





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int]				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = int(lowerCamelCase__				)
  return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = int(lowerCamelCase__				)
  return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[Any]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = int(lowerCamelCase__				)
  return DynamoBackend(DYNAMO_BACKENDS[value]				).value





def    _lowerCAmelCase    (     lowerCamelCase__							:							int				)  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				int							    = int(lowerCamelCase__				)
  return PrecisionType(["no", "fp16", "bf16", "fp8"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Any:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = int(lowerCamelCase__				)
  return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Optional[Any]:
  return {"yes": True, "no": False}[value.lower()]





class 			UpperCamelCase      (							argparse.RawDescriptionHelpFormatter  ):
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = super()._format_usage(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = usage.replace("<command> [<args>] "  ,     ""				)
       return usage



