



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,
)


_lowerCamelCase						    =  logging.get_logger(__name__)

_lowerCamelCase						    =  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"""),
    ]
)

_lowerCamelCase						    =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
  if class_name in extractors:
   __SCREAMING_SNAKE_CASE       :    Dict       =	model_type_to_module_name(__lowerCamelCase     )

   __SCREAMING_SNAKE_CASE       :    Dict       =	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       :    Optional[int]       =	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   :     Optional[int]		,	):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	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       _SCREAMING_SNAKE_CASE	:





     def __init__(       self :							int				)->str:
      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(UpperCamelCase				)
     def 	__snake_case    (       cls :							int     ,  UpperCamelCase :							Optional[Any]     ,  **UpperCamelCase :							Optional[Any]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    str       =	kwargs.pop("config"     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	kwargs.pop("trust_remote_code"     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	True

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Any       =	FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase     ,  **UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	config_dict.get("feature_extractor_type"     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	None
      if "AutoFeatureExtractor" in config_dict.get("auto_map"     ,  {}				):
       __SCREAMING_SNAKE_CASE       :    Any       =	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(UpperCamelCase     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    List[str]       =	AutoConfig.from_pretrained(UpperCamelCase     ,  **UpperCamelCase				)
       # It could be in `config.feature_extractor_type``
       __SCREAMING_SNAKE_CASE       :    Dict       =	getattr(UpperCamelCase     ,  "feature_extractor_type"     ,  UpperCamelCase				)
       if hasattr(UpperCamelCase     ,  "auto_map"				) and "AutoFeatureExtractor" in config.auto_map:
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	config.auto_map["AutoFeatureExtractor"]

      if feature_extractor_class is not None:
       __SCREAMING_SNAKE_CASE       :    Dict       =	feature_extractor_class_from_name(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    str       =	feature_extractor_auto_map is not None
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	feature_extractor_class is not None or type(UpperCamelCase				) in FEATURE_EXTRACTOR_MAPPING
      __SCREAMING_SNAKE_CASE       :    int       =	resolve_trust_remote_code(
          UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)

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

      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 	__snake_case    (       UpperCamelCase :							int     ,  UpperCamelCase :							Any				)->Any:
      FEATURE_EXTRACTOR_MAPPING.register(UpperCamelCase     ,  UpperCamelCase				)











# 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 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			"""philschmid/bart-large-cnn-samsum"""
     lowerCAmelCase					=			(
         """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
         """and returns a summary of the text."""
     )
     lowerCAmelCase					=			"""summarizer"""
     lowerCAmelCase					=			AutoTokenizer
     lowerCAmelCase					=			AutoModelForSeqaSeqLM

     lowerCAmelCase					=			["""text"""]
     lowerCAmelCase					=			["""text"""]





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Tuple				)->Tuple:
      return self.pre_processor(UpperCamelCase     ,  return_tensors="pt"     ,  truncation=UpperCamelCase				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Any]				)->Optional[Any]:
      return self.model.generate(**UpperCamelCase				)[0]






     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							Dict				)->Optional[int]:
      return self.pre_processor.decode(UpperCamelCase     ,  skip_special_tokens=UpperCamelCase     ,  clean_up_tokenization_spaces=UpperCamelCase				)






