import importlib
import inspect
import json
import os
from collections import OrderedDict

# 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 ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import 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,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


UpperCAmelCase      		=					logging.get_logger(__name__)

UpperCAmelCase      		=					OrderedDict(
    [
        ('''align''', '''AlignProcessor'''),
        ('''altclip''', '''AltCLIPProcessor'''),
        ('''bark''', '''BarkProcessor'''),
        ('''blip''', '''BlipProcessor'''),
        ('''blip-2''', '''Blip2Processor'''),
        ('''bridgetower''', '''BridgeTowerProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPProcessor'''),
        ('''clap''', '''ClapProcessor'''),
        ('''clip''', '''CLIPProcessor'''),
        ('''clipseg''', '''CLIPSegProcessor'''),
        ('''flava''', '''FlavaProcessor'''),
        ('''git''', '''GitProcessor'''),
        ('''groupvit''', '''CLIPProcessor'''),
        ('''hubert''', '''Wav2Vec2Processor'''),
        ('''instructblip''', '''InstructBlipProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2Processor'''),
        ('''layoutlmv3''', '''LayoutLMv3Processor'''),
        ('''markuplm''', '''MarkupLMProcessor'''),
        ('''mctct''', '''MCTCTProcessor'''),
        ('''mgp-str''', '''MgpstrProcessor'''),
        ('''oneformer''', '''OneFormerProcessor'''),
        ('''owlvit''', '''OwlViTProcessor'''),
        ('''pix2struct''', '''Pix2StructProcessor'''),
        ('''sam''', '''SamProcessor'''),
        ('''sew''', '''Wav2Vec2Processor'''),
        ('''sew-d''', '''Wav2Vec2Processor'''),
        ('''speech_to_text''', '''Speech2TextProcessor'''),
        ('''speech_to_text_2''', '''Speech2Text2Processor'''),
        ('''speecht5''', '''SpeechT5Processor'''),
        ('''trocr''', '''TrOCRProcessor'''),
        ('''tvlt''', '''TvltProcessor'''),
        ('''unispeech''', '''Wav2Vec2Processor'''),
        ('''unispeech-sat''', '''Wav2Vec2Processor'''),
        ('''vilt''', '''ViltProcessor'''),
        ('''vision-text-dual-encoder''', '''VisionTextDualEncoderProcessor'''),
        ('''wav2vec2''', '''Wav2Vec2Processor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2Processor'''),
        ('''wavlm''', '''Wav2Vec2Processor'''),
        ('''whisper''', '''WhisperProcessor'''),
        ('''xclip''', '''XCLIPProcessor'''),
    ]
)

UpperCAmelCase      		=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def SCREAMING_SNAKE_CASE					(  lowercase_:						str					):
       for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
              if class_name in processors:
                     _UpperCamelCase  :							Dict	    =	model_type_to_module_name(lowercase_					)

                     _UpperCamelCase  :							Any	    =	importlib.import_module(F".{module_name}"		,    'transformers.models'					)
                     try:
                            return getattr(lowercase_		,    lowercase_					)
                     except AttributeError:
                            continue

       for processor in PROCESSOR_MAPPING._extra_content.values():
              if getattr(lowercase_		,    '__name__'		,    lowercase_					) == class_name:
                     return processor

    # 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.
       _UpperCamelCase  :							Dict	    =	importlib.import_module('transformers'					)
       if hasattr(lowercase_		,    lowercase_					):
              return getattr(lowercase_		,    lowercase_					)

       return None





class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		Union[str, Any]	)			->				Optional[Any]:
        raise EnvironmentError(
            'AutoProcessor is designed to be instantiated '
            'using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'	)

 @classmethod
 @replace_list_option_in_docstrings(lowerCamelCase	)
 def        __snake_case       (      cls							:		Optional[int]							,      lowerCamelCase							:		str							,      **lowerCamelCase							:		int	)			->				int:
        _UpperCamelCase  :							Union[str, Any]	    =	kwargs.pop('config'							,      lowerCamelCase	)
        _UpperCamelCase  :							Dict	    =	kwargs.pop('trust_remote_code'							,      lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	True

        _UpperCamelCase  :							List[Any]	    =	None
        _UpperCamelCase  :							str	    =	None

        # First, let's see if we have a preprocessor config.
        # Filter the kwargs for `get_file_from_repo`.
        _UpperCamelCase  :							Tuple	    =	{
            key: kwargs[key] for key in inspect.signature(lowerCamelCase	).parameters.keys() if key in kwargs
        }
        # Let's start by checking whether the processor class is saved in an image processor
        _UpperCamelCase  :							Optional[Any]	    =	get_file_from_repo(
            lowerCamelCase							,      lowerCamelCase							,      **lowerCamelCase	)
        if preprocessor_config_file is not None:
               _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	ImageProcessingMixin.get_image_processor_dict(lowerCamelCase							,      **lowerCamelCase	)
               _UpperCamelCase  :							Tuple	    =	config_dict.get('processor_class'							,      lowerCamelCase	)
               if "AutoProcessor" in config_dict.get('auto_map'							,      {}	):
                      _UpperCamelCase  :							Tuple	    =	config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
        if preprocessor_config_file is not None and processor_class is None:
               _UpperCamelCase    ,   _UpperCamelCase  :							Dict	    =	FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase							,      **lowerCamelCase	)
               _UpperCamelCase  :							Tuple	    =	config_dict.get('processor_class'							,      lowerCamelCase	)
               if "AutoProcessor" in config_dict.get('auto_map'							,      {}	):
                      _UpperCamelCase  :							Dict	    =	config_dict['auto_map']['AutoProcessor']

        if processor_class is None:
               # Next, let's check whether the processor class is saved in a tokenizer
               _UpperCamelCase  :							List[str]	    =	get_file_from_repo(
                   lowerCamelCase							,      lowerCamelCase							,      **lowerCamelCase	)
               if tokenizer_config_file is not None:
                      with open(lowerCamelCase							,      encoding='utf-8'	) as reader:
                             _UpperCamelCase  :							Tuple	    =	json.load(lowerCamelCase	)

                      _UpperCamelCase  :							Tuple	    =	config_dict.get('processor_class'							,      lowerCamelCase	)
                      if "AutoProcessor" in config_dict.get('auto_map'							,      {}	):
                             _UpperCamelCase  :							int	    =	config_dict['auto_map']['AutoProcessor']

        if processor_class is None:
               # Otherwise, load config, if it can be loaded.
               if not isinstance(lowerCamelCase							,      lowerCamelCase	):
                      _UpperCamelCase  :							Any	    =	AutoConfig.from_pretrained(
                          lowerCamelCase							,      trust_remote_code=lowerCamelCase							,      **lowerCamelCase	)

               # And check if the config contains the processor class.
               _UpperCamelCase  :							Tuple	    =	getattr(lowerCamelCase							,      'processor_class'							,      lowerCamelCase	)
               if hasattr(lowerCamelCase							,      'auto_map'	) and "AutoProcessor" in config.auto_map:
                      _UpperCamelCase  :							int	    =	config.auto_map['AutoProcessor']

        if processor_class is not None:
               _UpperCamelCase  :							int	    =	processor_class_from_name(lowerCamelCase	)

        _UpperCamelCase  :							Optional[Any]	    =	processor_auto_map is not None
        _UpperCamelCase  :							Tuple	    =	processor_class is not None or type(lowerCamelCase	) in PROCESSOR_MAPPING
        _UpperCamelCase  :							int	    =	resolve_trust_remote_code(
            lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

        if has_remote_code and trust_remote_code:
               _UpperCamelCase  :							Any	    =	get_class_from_dynamic_module(
                   lowerCamelCase							,      lowerCamelCase							,      **lowerCamelCase	)
               _UpperCamelCase  :							Any	    =	kwargs.pop('code_revision'							,      lowerCamelCase	)
               if os.path.isdir(lowerCamelCase	):
                      processor_class.register_for_auto_class()
               return processor_class.from_pretrained(
                   lowerCamelCase							,      trust_remote_code=lowerCamelCase							,      **lowerCamelCase	)
        elif processor_class is not None:
               return processor_class.from_pretrained(
                   lowerCamelCase							,      trust_remote_code=lowerCamelCase							,      **lowerCamelCase	)
        # Last try: we use the PROCESSOR_MAPPING.
        elif type(lowerCamelCase	) in PROCESSOR_MAPPING:
               return PROCESSOR_MAPPING[type(lowerCamelCase	)].from_pretrained(lowerCamelCase							,      **lowerCamelCase	)

        # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
        # tokenizer.
        try:
               return AutoTokenizer.from_pretrained(
                   lowerCamelCase							,      trust_remote_code=lowerCamelCase							,      **lowerCamelCase	)
        except Exception:
               try:
                      return AutoImageProcessor.from_pretrained(
                          lowerCamelCase							,      trust_remote_code=lowerCamelCase							,      **lowerCamelCase	)
               except Exception:
                      pass

               try:
                      return AutoFeatureExtractor.from_pretrained(
                          lowerCamelCase							,      trust_remote_code=lowerCamelCase							,      **lowerCamelCase	)
               except Exception:
                      pass

        raise ValueError(
            f"Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a "
            'tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'
            'the files of at least one of those processing classes.'	)

 @staticmethod
 def        __snake_case       (      lowerCamelCase							:		Any							,      lowerCamelCase							:		List[str]	)			->				Any:
        PROCESSOR_MAPPING.register(lowerCamelCase							,      lowerCamelCase	)




import torch
from torch import autograd






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[str]:
        a	:     str						      =    inputs.numel()
        a	:     str						      =    int(0.0_05 * nb_elems     ) + 1
        if sigmoid:
             a	:     Optional[Any]						      =    (torch.sigmoid(lowerCAmelCase_     ) > threshold).type(inputs.type()     )
        else:
             a	:     Union[str, Any]						      =    (inputs > threshold).type(inputs.type()     )
        if mask.sum() < nb_min:
             # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
             a	:     str						      =    inputs.flatten().kthvalue(max(nb_elems - nb_min				,  1     )     ).values
             a	:     Tuple						      =    (inputs > k_threshold).type(inputs.type()     )
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        return gradOutput, None, None






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[Any]:
        a	:     List[Any]						      =    inputs.clone()
        a      ,				a	:     Optional[Any]						      =    inputs.flatten().sort(descending=lowerCAmelCase_     )
        a	:     Tuple						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Optional[Any]						      =    0
        a	:     List[Any]						      =    1
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        return gradOutput, None






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        a	:     Union[str, Any]						      =    inputs.clone()
        a      ,				a	:     List[Any]						      =    inputs.abs().flatten().sort(descending=lowerCAmelCase_     )
        a	:     Optional[int]						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Tuple						      =    0
        a	:     List[str]						      =    1
        return mask