

'''simple docstring'''
import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowerCamelCase					: str   					=	logging.get_logger("transformers.models.speecht5")

lowerCamelCase					: Tuple   					=	{
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
lowerCamelCase					: List[Any]   					=	{
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
lowerCamelCase					: int   					=	{
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
lowerCamelCase					: Any   					=	{
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
lowerCamelCase					: Union[str, Any]   					=	{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
lowerCamelCase					: List[str]   					=	{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
lowerCamelCase					: List[Any]   					=	{
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
lowerCamelCase					: Optional[Any]   					=	{
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
lowerCamelCase					: Optional[int]   					=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCamelCase					: Optional[int]   					=	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCamelCase					: Tuple   					=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCamelCase					: str   					=	[]
lowerCamelCase					: int   					=	[
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
lowerCamelCase					: Union[str, Any]   					=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
lowerCamelCase					: str   					=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
lowerCamelCase					: Optional[int]   					=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]


def 			_lowerCAmelCase   (		_UpperCamelCase		:	int				,					_UpperCamelCase		:	List[str]				,					_UpperCamelCase		:	Dict				,					_UpperCamelCase		:	Optional[int]				,					_UpperCamelCase		:	List[Any]							) ->     List[Any]:
     """simple docstring"""




     for attribute in key.split('.'							):
          _SCREAMING_SNAKE_CASE				       =getattr(lowerCamelCase_				,					lowerCamelCase_							)

     if weight_type is not None:
          _SCREAMING_SNAKE_CASE				       =getattr(lowerCamelCase_				,					lowerCamelCase_							).shape
     else:
          _SCREAMING_SNAKE_CASE				       =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":
          _SCREAMING_SNAKE_CASE				       =value
     elif weight_type == "weight_g":
          _SCREAMING_SNAKE_CASE				       =value
     elif weight_type == "weight_v":
          _SCREAMING_SNAKE_CASE				       =value
     elif weight_type == "bias":
          _SCREAMING_SNAKE_CASE				       =value
     elif weight_type == "running_mean":
          _SCREAMING_SNAKE_CASE				       =value
     elif weight_type == "running_var":
          _SCREAMING_SNAKE_CASE				       =value
     elif weight_type == "num_batches_tracked":
          _SCREAMING_SNAKE_CASE				       =value
     else:
          _SCREAMING_SNAKE_CASE				       =value

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


def 			_lowerCAmelCase   (		_UpperCamelCase		:	int				,					_UpperCamelCase		:	Optional[Any]							) ->     Any:
     """simple docstring"""




     for key in ignore_keys:
          if key.endswith('.*'							):
               if name.startswith(key[:-1]							):
                    return True
          elif ".*." in key:
               _SCREAMING_SNAKE_CASE				       =key.split('.*.'							)
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False


def 			_lowerCAmelCase   (		_UpperCamelCase		:	Optional[Any]				,					_UpperCamelCase		:	Union[str, Any]				,					_UpperCamelCase		:	int							) ->     int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE				       =[]

     if task == "s2t":
          _SCREAMING_SNAKE_CASE				       =hf_model.speechta.encoder.prenet.feature_encoder
          _SCREAMING_SNAKE_CASE				       =MAPPING_S2T
          _SCREAMING_SNAKE_CASE				       =IGNORE_KEYS_S2T
     elif task == "t2s":
          _SCREAMING_SNAKE_CASE				       =None
          _SCREAMING_SNAKE_CASE				       =MAPPING_T2S
          _SCREAMING_SNAKE_CASE				       =IGNORE_KEYS_T2S
     elif task == "s2s":
          _SCREAMING_SNAKE_CASE				       =hf_model.speechta.encoder.prenet.feature_encoder
          _SCREAMING_SNAKE_CASE				       =MAPPING_S2S
          _SCREAMING_SNAKE_CASE				       =IGNORE_KEYS_S2S
     else:
          raise ValueError(f"Unsupported task: {task}"							)

     for name, value in fairseq_dict.items():
          if should_ignore(lowerCamelCase_				,					lowerCamelCase_							):
               logger.info(f"{name} was ignored"							)
               continue

          _SCREAMING_SNAKE_CASE				       =False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					hf_model.config.feat_extract_norm == 'group'				,					)
               _SCREAMING_SNAKE_CASE				       =True
          else:
               for key, mapped_key in MAPPING.items():
                    # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                    if "*" in key:
                         _SCREAMING_SNAKE_CASE				       =key.split('.*.'							)
                         if prefix in name and suffix in name:
                              _SCREAMING_SNAKE_CASE				       =suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                    if key in name:
                         _SCREAMING_SNAKE_CASE				       =True
                         if "*" in mapped_key:
                              _SCREAMING_SNAKE_CASE				       =name.split(lowerCamelCase_							)[0].split('.'							)[-2]
                              _SCREAMING_SNAKE_CASE				       =mapped_key.replace('*'				,					lowerCamelCase_							)
                         if "weight_g" in name:
                              _SCREAMING_SNAKE_CASE				       ='weight_g'
                         elif "weight_v" in name:
                              _SCREAMING_SNAKE_CASE				       ='weight_v'
                         elif "bias" in name:
                              _SCREAMING_SNAKE_CASE				       ='bias'
                         elif "weight" in name:
                              _SCREAMING_SNAKE_CASE				       ='weight'
                         elif "running_mean" in name:
                              _SCREAMING_SNAKE_CASE				       ='running_mean'
                         elif "running_var" in name:
                              _SCREAMING_SNAKE_CASE				       ='running_var'
                         elif "num_batches_tracked" in name:
                              _SCREAMING_SNAKE_CASE				       ='num_batches_tracked'
                         else:
                              _SCREAMING_SNAKE_CASE				       =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 			_lowerCAmelCase   (		_UpperCamelCase		:	List[Any]				,					_UpperCamelCase		:	Union[str, Any]				,					_UpperCamelCase		:	List[Any]				,					_UpperCamelCase		:	List[str]				,					_UpperCamelCase		:	Tuple							) ->     Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE				       =full_name.split('conv_layers.'							)[-1]
     _SCREAMING_SNAKE_CASE				       =name.split('.'							)
     _SCREAMING_SNAKE_CASE				       =int(items[0]							)
     _SCREAMING_SNAKE_CASE				       =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."							)
               _SCREAMING_SNAKE_CASE				       =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."							)
               _SCREAMING_SNAKE_CASE				       =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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."							)
               _SCREAMING_SNAKE_CASE				       =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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."							)
               _SCREAMING_SNAKE_CASE				       =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 			_lowerCAmelCase   (		_UpperCamelCase		:	List[str]				,					_UpperCamelCase		:	List[Any]				,					_UpperCamelCase		:	str				,					_UpperCamelCase		:	Optional[int]=None				,					_UpperCamelCase		:	int=None				,					_UpperCamelCase		:	List[Any]=None				,					) ->     int:
     """simple docstring"""




     if config_path is not None:
          _SCREAMING_SNAKE_CASE				       =SpeechTaConfig.from_pretrained(lowerCamelCase_							)
     else:
          _SCREAMING_SNAKE_CASE				       =SpeechTaConfig()

     if task == "s2t":
          _SCREAMING_SNAKE_CASE				       =config.max_text_positions
          _SCREAMING_SNAKE_CASE				       =SpeechTaForSpeechToText(lowerCamelCase_							)
     elif task == "t2s":
          _SCREAMING_SNAKE_CASE				       =18_76
          _SCREAMING_SNAKE_CASE				       =6_00
          _SCREAMING_SNAKE_CASE				       =config.max_speech_positions
          _SCREAMING_SNAKE_CASE				       =SpeechTaForTextToSpeech(lowerCamelCase_							)
     elif task == "s2s":
          _SCREAMING_SNAKE_CASE				       =18_76
          _SCREAMING_SNAKE_CASE				       =config.max_speech_positions
          _SCREAMING_SNAKE_CASE				       =SpeechTaForSpeechToSpeech(lowerCamelCase_							)
     else:
          raise ValueError(f"Unknown task name: {task}"							)

     if vocab_path:
          _SCREAMING_SNAKE_CASE				       =SpeechTaTokenizer(lowerCamelCase_				,					model_max_length=config.max_text_positions							)

          # Mask token behaves like a normal word, i.e. include the space before it
          _SCREAMING_SNAKE_CASE				       =AddedToken('<mask>'				,					lstrip=lowerCamelCase_				,					rstrip=lowerCamelCase_							)
          _SCREAMING_SNAKE_CASE				       =mask_token
          tokenizer.add_special_tokens({'mask_token': mask_token}							)
          tokenizer.add_tokens(['<ctc_blank>']							)

     _SCREAMING_SNAKE_CASE				       =SpeechTaFeatureExtractor()
     _SCREAMING_SNAKE_CASE				       =SpeechTaProcessor(tokenizer=lowerCamelCase_				,					feature_extractor=lowerCamelCase_							)
     processor.save_pretrained(lowerCamelCase_							)

     _SCREAMING_SNAKE_CASE				       =torch.load(lowerCamelCase_							)
     recursively_load_weights(fairseq_checkpoint['model']				,					lowerCamelCase_				,					lowerCamelCase_							)

     model.save_pretrained(lowerCamelCase_							)

     if repo_id:
          print('Pushing to the hub...'							)
          processor.push_to_hub(lowerCamelCase_							)
          model.push_to_hub(lowerCamelCase_							)


if __name__ == "__main__":
     lowerCamelCase					: str   					=	argparse.ArgumentParser()
     parser.add_argument(
         "--task",
         default="s2t",
         type=str,
         help="Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.",
     )
     parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
     parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
     parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
     parser.add_argument(
         "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
     )

     lowerCamelCase					: Optional[Any]   					=	parser.parse_args()
     convert_speechta_checkpoint(
         args.task,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.vocab_path,
         args.push_to_hub,
     )






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     List[Any]  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      FunnelConfig.from_json_file(lowerCamelCase_  )
			print(F'Building PyTorch model from configuration: {config}'  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      FunnelBaseModel(lowerCamelCase_  ) if base_model else FunnelModel(lowerCamelCase_  )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			torch.save(model.state_dict()  ,							lowerCamelCase_  )


if __name__ == "__main__":
						UpperCamelCase__ :   Optional[Any]						=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
						)
						UpperCamelCase__ :   Tuple						=							parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
						)



