



"""simple docstring"""





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


SCREAMING_SNAKE_CASE				:     Optional[Any]          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[str]          =	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'''),
    ]
)

SCREAMING_SNAKE_CASE				:     int          =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def 	__UpperCAmelCase					(       snake_case_  :  str   )							->	List[str]:






   """simple docstring"""


   for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
      if class_name in processors:
         _lowerCAmelCase					     =		model_type_to_module_name(snake_case_   )

         _lowerCAmelCase					     =		importlib.import_module(F""".{module_name}"""   ,	"""transformers.models"""   )
         try:
            return getattr(snake_case_   ,	snake_case_   )
         except AttributeError:
            continue

   for processor in PROCESSOR_MAPPING._extra_content.values():
      if getattr(snake_case_   ,	"""__name__"""   ,	snake_case_   ) == 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.
   _lowerCAmelCase					     =		importlib.import_module("""transformers"""   )
   if hasattr(snake_case_   ,	snake_case_   ):
      return getattr(snake_case_   ,	snake_case_   )

   return None



class       __lowerCamelCase	:







   def __init__(self				):




      '''simple docstring'''




      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 							A__						(cls       ,	lowerCamelCase       ,	**lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		kwargs.pop("""config"""       ,	lowerCamelCase				)
      _lowerCAmelCase					     =		kwargs.pop("""trust_remote_code"""       ,	lowerCamelCase				)
      _lowerCAmelCase					     =		True

      _lowerCAmelCase					     =		None
      _lowerCAmelCase					     =		None

      # First, let's see if we have a preprocessor config.
      # Filter the kwargs for `get_file_from_repo`.
      _lowerCAmelCase					     =		{
          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
      _lowerCAmelCase					     =		get_file_from_repo(
          lowerCamelCase       ,	lowerCamelCase       ,	**lowerCamelCase				)
      if preprocessor_config_file is not None:
         _lowerCAmelCase     ,						_lowerCAmelCase					     =		ImageProcessingMixin.get_image_processor_dict(lowerCamelCase       ,	**lowerCamelCase				)
         _lowerCAmelCase					     =		config_dict.get("""processor_class"""       ,	lowerCamelCase				)
         if "AutoProcessor" in config_dict.get("""auto_map"""       ,	{}				):
            _lowerCAmelCase					     =		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:
         _lowerCAmelCase     ,						_lowerCAmelCase					     =		FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase       ,	**lowerCamelCase				)
         _lowerCAmelCase					     =		config_dict.get("""processor_class"""       ,	lowerCamelCase				)
         if "AutoProcessor" in config_dict.get("""auto_map"""       ,	{}				):
            _lowerCAmelCase					     =		config_dict["""auto_map"""]["""AutoProcessor"""]

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

            _lowerCAmelCase					     =		config_dict.get("""processor_class"""       ,	lowerCamelCase				)
            if "AutoProcessor" in config_dict.get("""auto_map"""       ,	{}				):
               _lowerCAmelCase					     =		config_dict["""auto_map"""]["""AutoProcessor"""]

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

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

      if processor_class is not None:
         _lowerCAmelCase					     =		processor_class_from_name(lowerCamelCase				)

      _lowerCAmelCase					     =		processor_auto_map is not None
      _lowerCAmelCase					     =		processor_class is not None or type(lowerCamelCase				) in PROCESSOR_MAPPING
      _lowerCAmelCase					     =		resolve_trust_remote_code(
          lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)

      if has_remote_code and trust_remote_code:
         _lowerCAmelCase					     =		get_class_from_dynamic_module(
             lowerCamelCase       ,	lowerCamelCase       ,	**lowerCamelCase				)
         _lowerCAmelCase					     =		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 							A__						(lowerCamelCase       ,	lowerCamelCase				):




      '''simple docstring'''




      PROCESSOR_MAPPING.register(lowerCamelCase       ,	lowerCamelCase				)




"""simple docstring"""





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:     str          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[str]          =	{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}






def 	__UpperCAmelCase					(       snake_case_  :  List[str]   ,	snake_case_  :  Dict   ,	snake_case_  :  Any   ,	snake_case_  :  str   ,	snake_case_  :  Tuple   )							->	str:






   """simple docstring"""


   for attribute in key.split("""."""   ):
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   )

   if weight_type is not None:
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   ).shape
   else:
      _lowerCAmelCase					     =		hf_pointer.shape

   assert hf_shape == value.shape, (
       F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
       F""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_g":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_v":
      _lowerCAmelCase					     =		value
   elif weight_type == "bias":
      _lowerCAmelCase					     =		value
   else:
      _lowerCAmelCase					     =		value

   logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""   )






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  Any   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		[]
   _lowerCAmelCase					     =		fairseq_model.state_dict()

   _lowerCAmelCase					     =		hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _lowerCAmelCase					     =		False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	hf_model.config.feat_extract_norm == """group"""   ,	)
         _lowerCAmelCase					     =		True
      else:
         for key, mapped_key in MAPPING.items():
            _lowerCAmelCase					     =		mapped_key

            if key in name:
               _lowerCAmelCase					     =		True
               if "*" in mapped_key:
                  _lowerCAmelCase					     =		name.split(snake_case_   )[0].split("""."""   )[-2]
                  _lowerCAmelCase					     =		mapped_key.replace("""*"""   ,	snake_case_   )
               if "weight_g" in name:
                  _lowerCAmelCase					     =		"""weight_g"""
               elif "weight_v" in name:
                  _lowerCAmelCase					     =		"""weight_v"""
               elif "weight" in name:
                  _lowerCAmelCase					     =		"""weight"""
               elif "bias" in name:
                  _lowerCAmelCase					     =		"""bias"""
               else:
                  _lowerCAmelCase					     =		None
               set_recursively(snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   )
            continue
      if not is_used:
         unused_weights.append(snake_case_   )

   logger.warning(F"""Unused weights: {unused_weights}"""   )






def 	__UpperCAmelCase					(       snake_case_  :  Any   ,	snake_case_  :  List[Any]   ,	snake_case_  :  Dict   ,	snake_case_  :  Union[str, Any]   ,	snake_case_  :  Optional[Any]   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		full_name.split("""conv_layers."""   )[-1]
   _lowerCAmelCase					     =		name.split("""."""   )
   _lowerCAmelCase					     =		int(items[0]   )
   _lowerCAmelCase					     =		int(items[1]   )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(snake_case_   )






def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   )							->	Any:






   """simple docstring"""


   _lowerCAmelCase					     =		HubertConfig()
   _lowerCAmelCase					     =		model.config

   _lowerCAmelCase					     =		fs_config.activation_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		fs_config.attention_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		eval(fs_config.extractor_conv_feature_layers   )
   _lowerCAmelCase					     =		[x[0] for x in conv_layers]
   _lowerCAmelCase					     =		[x[1] for x in conv_layers]
   _lowerCAmelCase					     =		[x[2] for x in conv_layers]
   _lowerCAmelCase					     =		"""gelu"""
   _lowerCAmelCase					     =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.activation_fn
   _lowerCAmelCase					     =		fs_config.dropout
   _lowerCAmelCase					     =		fs_config.encoder_embed_dim
   _lowerCAmelCase					     =		0.0_2
   _lowerCAmelCase					     =		fs_config.encoder_ffn_embed_dim
   _lowerCAmelCase					     =		1e-5
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.encoder_attention_heads
   _lowerCAmelCase					     =		fs_config.conv_pos_groups
   _lowerCAmelCase					     =		fs_config.conv_pos
   _lowerCAmelCase					     =		len(snake_case_   )
   _lowerCAmelCase					     =		fs_config.encoder_layers

   return config






@torch.no_grad()
def 	__UpperCAmelCase					(       snake_case_  :  Optional[Any]   ,	snake_case_  :  Union[str, Any]=None   )							->	List[str]:






   """simple docstring"""


   _lowerCAmelCase					     =		distilhubert().model.model

   if config_path is not None:
      _lowerCAmelCase					     =		HubertConfig.from_pretrained(snake_case_   )
   else:
      _lowerCAmelCase					     =		convert_config(snake_case_   )
   _lowerCAmelCase					     =		model.eval()

   _lowerCAmelCase					     =		WavaVecaFeatureExtractor(
       feature_size=1   ,	sampling_rate=16000   ,	padding_value=0   ,	do_normalize=snake_case_   ,	return_attention_mask=snake_case_   ,	)
   _lowerCAmelCase					     =		HubertModel(snake_case_   )

   recursively_load_weights(snake_case_   ,	snake_case_   )

   feature_extractor.save_pretrained(snake_case_   )
   hf_model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     List[str]          =	argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  SCREAMING_SNAKE_CASE				:     Optional[int]          =	parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)