


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
UpperCAmelCase_      : List[str]  					=       logging.get_logger(__name__)

UpperCAmelCase_      : Tuple  					=       {
    '''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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
UpperCAmelCase_      : Union[str, Any]  					=       [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			Any  ,    __A :			int  ,    __A :			str  ,    __A :			Union[str, Any]				)	->		int:

       """simple docstring"""

       for attribute in key.split('.'				):
              a_					:		Optional[Any]							=  getattr(lowerCamelCase__  ,    lowerCamelCase__				)

       if weight_type is not None:
              a_					:		Optional[int]							=  getattr(lowerCamelCase__  ,    lowerCamelCase__				).shape
       else:
              a_					:		int							=  hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  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":
              a_					:		int							=  value
       elif weight_type == "weight_g":
              a_					:		List[str]							=  value
       elif weight_type == "weight_v":
              a_					:		Any							=  value
       elif weight_type == "bias":
              a_					:		Union[str, Any]							=  value
       else:
              a_					:		Tuple							=  value

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







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]  ,    __A :			List[str]				)	->		Optional[Any]:

       """simple docstring"""

       a_					:		int							=  []
       a_					:		str							=  fairseq_model.state_dict()

       a_					:		Dict							=  hf_model.unispeech_sat.feature_extractor

       for name, value in fairseq_dict.items():
              a_					:		Optional[Any]							=  False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__  ,    hf_model.config.feat_extract_norm == 'group'  ,    )
                     a_					:		Optional[int]							=  True
              else:
                     for key, mapped_key in MAPPING.items():
                            a_					:		str							=  """unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                            if key in name or key.split('w2v_model.'				)[-1] == name.split('.'				)[0]:
                                   if "layer_norm_for_extract" in name and (".".join(name.split('.'				)[:-1]				) != key):
                                          # special case since naming is very similar
                                          continue
                                   a_					:		Optional[int]							=  True
                                   if "*" in mapped_key:
                                          a_					:		Optional[Any]							=  name.split(lowerCamelCase__				)[0].split('.'				)[-2]
                                          a_					:		Dict							=  mapped_key.replace('*'  ,    lowerCamelCase__				)
                                   if "weight_g" in name:
                                          a_					:		Tuple							=  """weight_g"""
                                   elif "weight_v" in name:
                                          a_					:		int							=  """weight_v"""
                                   elif "bias" in name:
                                          a_					:		Union[str, Any]							=  """bias"""
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          a_					:		Optional[Any]							=  """weight"""
                                   else:
                                          a_					:		Any							=  None
                                   set_recursively(lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__				)
                            continue
              if not is_used:
                     unused_weights.append(lowerCamelCase__				)

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







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			int  ,    __A :			Tuple  ,    __A :			Optional[Any]  ,    __A :			int				)	->		List[str]:

       """simple docstring"""

       a_					:		Union[str, Any]							=  full_name.split('conv_layers.'				)[-1]
       a_					:		Optional[int]							=  name.split('.'				)
       a_					:		Any							=  int(items[0]				)
       a_					:		List[str]							=  int(items[1]				)

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""				)
                     a_					:		Union[str, Any]							=  value
                     logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""				)
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""				)
                     a_					:		str							=  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:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""				)
                     a_					:		Dict							=  value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""				)
                     a_					:		Dict							=  value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
       else:
              unused_weights.append(lowerCamelCase__				)







@torch.no_grad()
def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			int  ,    __A :			Optional[Any]=None  ,    __A :			Any=None  ,    __A :			Any=True				)	->		List[Any]:

       """simple docstring"""

       if config_path is not None:
              a_					:		Optional[int]							=  UniSpeechSatConfig.from_pretrained(lowerCamelCase__				)
       else:
              a_					:		Dict							=  UniSpeechSatConfig()

       a_					:		List[str]							=  """"""

       if is_finetuned:
              a_					:		Any							=  UniSpeechSatForCTC(lowerCamelCase__				)
       else:
              a_					:		Optional[int]							=  UniSpeechSatForPreTraining(lowerCamelCase__				)

       a_					:		Optional[int]							=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]  ,    arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				)}				)
       a_					:		List[Any]							=  model[0].eval()

       recursively_load_weights(lowerCamelCase__  ,    lowerCamelCase__				)

       hf_wavavec.save_pretrained(lowerCamelCase__				)


if __name__ == "__main__":
   UpperCAmelCase_      : Tuple  					=       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(
       '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
   )
   UpperCAmelCase_      : str  					=       parser.parse_args()
   convert_unispeech_sat_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )







'''simple docstring'''




import re



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]"""						,      str_    )]



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[int]	=     split_input(str_    )
			return "".join(
			    ["""""".join([char.capitalize() for char in sub_str]    ) for sub_str in string_split]    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			try:
						A_	:						List[Any]	=     split_input(lowerCamelCase__    )
						if upper:
									A_	:						Tuple	=     """""".join(
									    [
									        separator.join([char.upper() for char in sub_str]    )
									        for sub_str in string_split
									    ]    )
						else:
									A_	:						Optional[int]	=     """""".join(
									    [
									        separator.join([char.lower() for char in sub_str]    )
									        for sub_str in string_split
									    ]    )
						return res_str
			except IndexError:
						return "not valid string"



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			return to_simple_case(lowerCamelCase__    )



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			try:
						A_	:						Tuple	=     to_simple_case(lowerCamelCase__    )
						return res_str[0].lower() + res_str[1:]
			except IndexError:
						return "not valid string"



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			return to_complex_case(lowerCamelCase__						,      lowerCamelCase__						,      """_"""    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			return to_complex_case(lowerCamelCase__						,      lowerCamelCase__						,      """-"""    )


if __name__ == "__main__":
						__import__('''doctest''').testmod()