


'''simple docstring'''

import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class 	snake_case       (       lowerCamelCase__     ):





  """simple docstring"""




  _lowerCamelCase						       =    ["image_processor", "tokenizer"]
  _lowerCamelCase						       =    "AutoImageProcessor"
  _lowerCamelCase						       =    "AutoTokenizer"






  def __init__(       self     ,    UpperCamelCase=None     ,    UpperCamelCase=None     ,    **UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    None
   if "feature_extractor" in kwargs:
    warnings.warn(
        "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
        " instead."     ,    SCREAMING_SNAKE_CASE__     ,    )
    lowerCamelCase_						=    kwargs.pop("feature_extractor"	)

   lowerCamelCase_						=    image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
    raise ValueError("You need to specify an `image_processor`."	)
   if tokenizer is None:
    raise ValueError("You need to specify a `tokenizer`."	)

   super().__init__(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__	)
   lowerCamelCase_						=    self.image_processor
   lowerCamelCase_						=    False






  def __call__(       self     ,    *UpperCamelCase     ,    **UpperCamelCase	):







   """simple docstring"""



   if self._in_target_context_manager:
    return self.current_processor(*SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__	)

   lowerCamelCase_						=    kwargs.pop("images"     ,    SCREAMING_SNAKE_CASE__	)
   lowerCamelCase_						=    kwargs.pop("text"     ,    SCREAMING_SNAKE_CASE__	)
   if len(SCREAMING_SNAKE_CASE__	) > 0:
    lowerCamelCase_						=    args[0]
    lowerCamelCase_						=    args[1:]

   if images is None and text is None:
    raise ValueError("You need to specify either an `images` or `text` input to process."	)

   if images is not None:
    lowerCamelCase_						=    self.image_processor(SCREAMING_SNAKE_CASE__     ,    *SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__	)
   if text is not None:
    lowerCamelCase_						=    self.tokenizer(SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__	)

   if text is None:
    return inputs
   elif images is None:
    return encodings
   else:
    lowerCamelCase_						=    encodings['''input_ids''']
    return inputs






  def     snake_case						(       self     ,    *UpperCamelCase     ,    **UpperCamelCase	):







   """simple docstring"""
   return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__	)






  def     snake_case						(       self     ,    *UpperCamelCase     ,    **UpperCamelCase	):







   """simple docstring"""
   return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__	)






  @contextmanager
  def     snake_case						(       self	):







   """simple docstring"""
   warnings.warn(
       "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
       "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
       "your images inputs, or in a separate call."	)
   lowerCamelCase_						=    True
   lowerCamelCase_						=    self.tokenizer
   yield
   lowerCamelCase_						=    self.image_processor
   lowerCamelCase_						=    False






  def     snake_case						(       self     ,    UpperCamelCase     ,    UpperCamelCase=False     ,    UpperCamelCase=None	):







   """simple docstring"""



   if added_vocab is None:
    lowerCamelCase_						=    self.tokenizer.get_added_vocab()

   lowerCamelCase_						=    {}

   while tokens:
    lowerCamelCase_						=    re.search(r"<s_(.*?)>"     ,    SCREAMING_SNAKE_CASE__     ,    re.IGNORECASE	)
    if start_token is None:
     break
    lowerCamelCase_						=    start_token.group(1	)
    lowerCamelCase_						=    re.search(rf'''</s_{key}>'''     ,    SCREAMING_SNAKE_CASE__     ,    re.IGNORECASE	)
    lowerCamelCase_						=    start_token.group()
    if end_token is None:
     lowerCamelCase_						=    tokens.replace(SCREAMING_SNAKE_CASE__     ,    ""	)
    else:
     lowerCamelCase_						=    end_token.group()
     lowerCamelCase_						=    re.escape(SCREAMING_SNAKE_CASE__	)
     lowerCamelCase_						=    re.escape(SCREAMING_SNAKE_CASE__	)
     lowerCamelCase_						=    re.search(f'''{start_token_escaped}(.*?){end_token_escaped}'''     ,    SCREAMING_SNAKE_CASE__     ,    re.IGNORECASE	)
     if content is not None:
      lowerCamelCase_						=    content.group(1	).strip()
      if r"<s_" in content and r"</s_" in content:  # non-leaf node
       lowerCamelCase_						=    self.tokenajson(SCREAMING_SNAKE_CASE__     ,    is_inner_value=SCREAMING_SNAKE_CASE__     ,    added_vocab=SCREAMING_SNAKE_CASE__	)
       if value:
        if len(SCREAMING_SNAKE_CASE__	) == 1:
         lowerCamelCase_						=    value[0]
        lowerCamelCase_						=    value
      else:  # leaf nodes
       lowerCamelCase_						=    []
       for leaf in content.split(r"<sep/>"	):
        lowerCamelCase_						=    leaf.strip()
        if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
         lowerCamelCase_						=    leaf[1:-2]  # for categorical special tokens
        output[key].append(SCREAMING_SNAKE_CASE__	)
       if len(output[key]	) == 1:
        lowerCamelCase_						=    output[key][0]

     lowerCamelCase_						=    tokens[tokens.find(SCREAMING_SNAKE_CASE__	) + len(SCREAMING_SNAKE_CASE__	) :].strip()
     if tokens[:6] == r"<sep/>":  # non-leaf nodes
      return [output] + self.tokenajson(tokens[6:]     ,    is_inner_value=SCREAMING_SNAKE_CASE__     ,    added_vocab=SCREAMING_SNAKE_CASE__	)

   if len(SCREAMING_SNAKE_CASE__	):
    return [output] if is_inner_value else output
   else:
    return [] if is_inner_value else {"text_sequence": tokens}






  @property
  def     snake_case						(       self	):







   """simple docstring"""
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,    SCREAMING_SNAKE_CASE__     ,    )
   return self.image_processor_class







  @property
  def     snake_case						(       self	):







   """simple docstring"""
   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,    SCREAMING_SNAKE_CASE__     ,    )
   return self.image_processor







"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__				:Optional[int]			   =	logging.get_logger(__name__)

UpperCamelCase__				:int			   =	{
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			for attribute in key.split('''.'''		):
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		)

			if weight_type is not None:
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		).shape
			else:
						_UpperCamelCase					:Optional[int]					     =	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":
						_UpperCamelCase					:str					     =	value
			elif weight_type == "weight_g":
						_UpperCamelCase					:Dict					     =	value
			elif weight_type == "weight_v":
						_UpperCamelCase					:Optional[Any]					     =	value
			elif weight_type == "bias":
						_UpperCamelCase					:str					     =	value
			else:
						_UpperCamelCase					:int					     =	value

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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			_UpperCamelCase					:Optional[int]					     =	[]
			_UpperCamelCase					:List[str]					     =	fairseq_model.state_dict()

			_UpperCamelCase					:str					     =	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCamelCase					:Optional[Any]					     =	False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					hf_model.config.feat_extract_norm == '''group'''       ,					)
									_UpperCamelCase					:Dict					     =	True
						else:
									for key, mapped_key in MAPPING.items():
												_UpperCamelCase					:Optional[int]					     =	'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

												if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
															_UpperCamelCase					:List[str]					     =	True
															if "*" in mapped_key:
																		_UpperCamelCase					:List[str]					     =	name.split(snake_case__		)[0].split('''.'''		)[-2]
																		_UpperCamelCase					:Tuple					     =	mapped_key.replace('''*'''       ,					snake_case__		)
															if "weight_g" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight_g'''
															elif "weight_v" in name:
																		_UpperCamelCase					:Union[str, Any]					     =	'''weight_v'''
															elif "weight" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight'''
															elif "bias" in name:
																		_UpperCamelCase					:List[Any]					     =	'''bias'''
															else:
																		_UpperCamelCase					:List[Any]					     =	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 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[int]					     =	full_name.split('''conv_layers.'''		)[-1]
			_UpperCamelCase					:Optional[int]					     =	name.split('''.'''		)
			_UpperCamelCase					:Optional[Any]					     =	int(items[0]		)
			_UpperCamelCase					:List[str]					     =	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."
									)
									_UpperCamelCase					:Optional[int]					     =	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."
									)
									_UpperCamelCase					:Optional[Any]					     =	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."
									)
									_UpperCamelCase					:int					     =	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."
									)
									_UpperCamelCase					:Dict					     =	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 				A_       ( snake_case__       ,					snake_case__		)					->  List[str]:
			_UpperCamelCase					:str					     =	SEWConfig()
			if is_finetuned:
						_UpperCamelCase					:Optional[int]					     =	model.wav_encoder.wav_model.cfg
			else:
						_UpperCamelCase					:Dict					     =	model.cfg

			_UpperCamelCase					:Dict					     =	fs_config.conv_bias
			_UpperCamelCase					:int					     =	eval(fs_config.conv_feature_layers		)
			_UpperCamelCase					:List[Any]					     =	[x[0] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[1] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[2] for x in conv_layers]
			_UpperCamelCase					:str					     =	'''gelu'''
			_UpperCamelCase					:Optional[int]					     =	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
			_UpperCamelCase					:List[Any]					     =	0.0
			_UpperCamelCase					:Optional[int]					     =	fs_config.activation_fn.name
			_UpperCamelCase					:str					     =	fs_config.encoder_embed_dim
			_UpperCamelCase					:Dict					     =	0.02
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_ffn_embed_dim
			_UpperCamelCase					:str					     =	1E-5
			_UpperCamelCase					:int					     =	fs_config.encoder_layerdrop
			_UpperCamelCase					:Union[str, Any]					     =	fs_config.encoder_attention_heads
			_UpperCamelCase					:List[str]					     =	fs_config.conv_pos_groups
			_UpperCamelCase					:List[Any]					     =	fs_config.conv_pos
			_UpperCamelCase					:List[str]					     =	len(snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_layers
			_UpperCamelCase					:Optional[int]					     =	fs_config.squeeze_factor

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						_UpperCamelCase					:List[Any]					     =	model.cfg
						_UpperCamelCase					:List[Any]					     =	fs_config.final_dropout
						_UpperCamelCase					:Dict					     =	fs_config.layerdrop
			_UpperCamelCase					:Any					     =	fs_config.activation_dropout
			_UpperCamelCase					:List[str]					     =	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			_UpperCamelCase					:Optional[Any]					     =	fs_config.attention_dropout
			_UpperCamelCase					:List[Any]					     =	fs_config.dropout_input
			_UpperCamelCase					:Dict					     =	fs_config.dropout
			_UpperCamelCase					:int					     =	fs_config.mask_channel_length
			_UpperCamelCase					:Tuple					     =	fs_config.mask_channel_prob
			_UpperCamelCase					:int					     =	fs_config.mask_length
			_UpperCamelCase					:Dict					     =	fs_config.mask_prob

			_UpperCamelCase					:List[Any]					     =	'''Wav2Vec2FeatureExtractor'''
			_UpperCamelCase					:Optional[Any]					     =	'''Wav2Vec2CTCTokenizer'''

			return config





@torch.no_grad()
def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__=None       ,					snake_case__=None       ,					snake_case__=True		)					->  int:

			if is_finetuned:
						_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:List[Any]					     =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]       ,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		)}		)
			else:
						_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:str					     =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

			if config_path is not None:
						_UpperCamelCase					:Any					     =	SEWConfig.from_pretrained(snake_case__		)
			else:
						_UpperCamelCase					:List[str]					     =	convert_config(model[0]       ,					snake_case__		)
			_UpperCamelCase					:List[str]					     =	model[0].eval()

			_UpperCamelCase					:Optional[Any]					     =	True if config.feat_extract_norm == '''layer''' else False
			_UpperCamelCase					:str					     =	WavaVecaFeatureExtractor(
			    feature_size=1       ,					sampling_rate=1_60_00       ,					padding_value=0       ,					do_normalize=snake_case__       ,					return_attention_mask=snake_case__       ,					)

			if is_finetuned:
						if dict_path:
									_UpperCamelCase					:int					     =	Dictionary.load(snake_case__		)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									_UpperCamelCase					:List[Any]					     =	target_dict.pad_index
									_UpperCamelCase					:str					     =	target_dict.bos_index
									_UpperCamelCase					:int					     =	target_dict.pad_index
									_UpperCamelCase					:Dict					     =	target_dict.bos_index
									_UpperCamelCase					:int					     =	target_dict.eos_index
									_UpperCamelCase					:str					     =	len(target_dict.symbols		)
									_UpperCamelCase					:Optional[int]					     =	os.path.join(snake_case__       ,					'''vocab.json'''		)
									if not os.path.isdir(snake_case__		):
												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case__		)		)
												return
									os.makedirs(snake_case__       ,					exist_ok=snake_case__		)
									with open(snake_case__       ,					'''w'''       ,					encoding='''utf-8'''		) as vocab_handle:
												json.dump(target_dict.indices       ,					snake_case__		)
									_UpperCamelCase					:int					     =	WavaVecaCTCTokenizer(
									    snake_case__       ,					unk_token=target_dict.unk_word       ,					pad_token=target_dict.pad_word       ,					bos_token=target_dict.bos_word       ,					eos_token=target_dict.eos_word       ,					word_delimiter_token='''|'''       ,					do_lower_case=snake_case__       ,					)
									_UpperCamelCase					:Dict					     =	WavaVecaProcessor(feature_extractor=snake_case__       ,					tokenizer=snake_case__		)
									processor.save_pretrained(snake_case__		)

						_UpperCamelCase					:Tuple					     =	SEWForCTC(snake_case__		)
			else:
						_UpperCamelCase					:str					     =	SEWModel(snake_case__		)
						feature_extractor.save_pretrained(snake_case__		)

			recursively_load_weights(snake_case__       ,					snake_case__       ,					snake_case__		)

			hf_model.save_pretrained(snake_case__		)


if __name__ == "__main__":
					UpperCamelCase__				: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("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					UpperCamelCase__				:Tuple			   =	parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)

