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()
a_    					=       logging.get_logger('transformers.models.speecht5')

a_    					=       {
    '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',
}
a_    					=       {
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
a_    					=       {
    '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',
}
a_    					=       {
    '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',
}
a_    					=       {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
a_    					=       {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
a_    					=       {
    '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',
}
a_    					=       {
    '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',
}
a_    					=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a_    					=       {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a_    					=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a_    					=       []
a_    					=       [
    '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',
]
a_    					=       IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
a_    					=       IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
a_    					=       IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Tuple	):
 for attribute in key.split('.'	):
  UpperCamelCase_					:						Any =					getattr(lowerCamelCase  ,  lowerCamelCase	)

 if weight_type is not None:
  UpperCamelCase_					:						Tuple =					getattr(lowerCamelCase  ,  lowerCamelCase	).shape
 else:
  UpperCamelCase_					:						Tuple =					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":
  UpperCamelCase_					:						List[str] =					value
 elif weight_type == "weight_g":
  UpperCamelCase_					:						List[str] =					value
 elif weight_type == "weight_v":
  UpperCamelCase_					:						str =					value
 elif weight_type == "bias":
  UpperCamelCase_					:						List[str] =					value
 elif weight_type == "running_mean":
  UpperCamelCase_					:						Union[str, Any] =					value
 elif weight_type == "running_var":
  UpperCamelCase_					:						List[Any] =					value
 elif weight_type == "num_batches_tracked":
  UpperCamelCase_					:						Dict =					value
 else:
  UpperCamelCase_					:						Optional[int] =					value

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





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]	):
 for key in ignore_keys:
  if key.endswith('.*'	):
   if name.startswith(key[:-1]	):
    return True
  elif ".*." in key:
   UpperCamelCase_, UpperCamelCase_					:						int =					key.split('.*.'	)
   if prefix in name and suffix in name:
    return True
  elif key in name:
   return True
 return False





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						Any =					[]

 if task == "s2t":
  UpperCamelCase_					:						str =					hf_model.speechta.encoder.prenet.feature_encoder
  UpperCamelCase_					:						str =					MAPPING_S2T
  UpperCamelCase_					:						str =					IGNORE_KEYS_S2T
 elif task == "t2s":
  UpperCamelCase_					:						Union[str, Any] =					None
  UpperCamelCase_					:						Union[str, Any] =					MAPPING_T2S
  UpperCamelCase_					:						str =					IGNORE_KEYS_T2S
 elif task == "s2s":
  UpperCamelCase_					:						List[str] =					hf_model.speechta.encoder.prenet.feature_encoder
  UpperCamelCase_					:						List[Any] =					MAPPING_S2S
  UpperCamelCase_					:						Any =					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

  UpperCamelCase_					:						List[str] =					False
  if "conv_layers" in name:
   load_conv_layer(
       lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase  ,  hf_model.config.feat_extract_norm == 'group'  ,  )
   UpperCamelCase_					:						Any =					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:
     UpperCamelCase_, UpperCamelCase_					:						Any =					key.split('.*.'	)
     if prefix in name and suffix in name:
      UpperCamelCase_					:						List[Any] =					suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
    if key in name:
     UpperCamelCase_					:						Optional[Any] =					True
     if "*" in mapped_key:
      UpperCamelCase_					:						Tuple =					name.split(lowerCamelCase	)[0].split('.'	)[-2]
      UpperCamelCase_					:						List[Any] =					mapped_key.replace('*'  ,  lowerCamelCase	)
     if "weight_g" in name:
      UpperCamelCase_					:						str =					'weight_g'
     elif "weight_v" in name:
      UpperCamelCase_					:						Tuple =					'weight_v'
     elif "bias" in name:
      UpperCamelCase_					:						int =					'bias'
     elif "weight" in name:
      UpperCamelCase_					:						Tuple =					'weight'
     elif "running_mean" in name:
      UpperCamelCase_					:						Tuple =					'running_mean'
     elif "running_var" in name:
      UpperCamelCase_					:						Union[str, Any] =					'running_var'
     elif "num_batches_tracked" in name:
      UpperCamelCase_					:						Optional[int] =					'num_batches_tracked'
     else:
      UpperCamelCase_					:						int =					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    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     Optional[int]	):
 UpperCamelCase_					:						Union[str, Any] =					full_name.split('conv_layers.'	)[-1]
 UpperCamelCase_					:						Dict =					name.split('.'	)
 UpperCamelCase_					:						Union[str, Any] =					int(items[0]	)
 UpperCamelCase_					:						Optional[Any] =					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."	)
   UpperCamelCase_					:						List[str] =					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."	)
   UpperCamelCase_					:						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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."	)
   UpperCamelCase_					:						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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."	)
   UpperCamelCase_					:						int =					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    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Any=None  ,  lowerCamelCase					:     List[Any]=None  ,  lowerCamelCase					:     Tuple=None  ,  ):
 if config_path is not None:
  UpperCamelCase_					:						Tuple =					SpeechTaConfig.from_pretrained(lowerCamelCase	)
 else:
  UpperCamelCase_					:						int =					SpeechTaConfig()

 if task == "s2t":
  UpperCamelCase_					:						Any =					config.max_text_positions
  UpperCamelCase_					:						Optional[Any] =					SpeechTaForSpeechToText(lowerCamelCase	)
 elif task == "t2s":
  UpperCamelCase_					:						Union[str, Any] =					1876
  UpperCamelCase_					:						List[Any] =					600
  UpperCamelCase_					:						str =					config.max_speech_positions
  UpperCamelCase_					:						List[Any] =					SpeechTaForTextToSpeech(lowerCamelCase	)
 elif task == "s2s":
  UpperCamelCase_					:						List[str] =					1876
  UpperCamelCase_					:						Optional[int] =					config.max_speech_positions
  UpperCamelCase_					:						Optional[Any] =					SpeechTaForSpeechToSpeech(lowerCamelCase	)
 else:
  raise ValueError(F"Unknown task name: {task}"	)

 if vocab_path:
  UpperCamelCase_					:						List[Any] =					SpeechTaTokenizer(lowerCamelCase  ,  model_max_length=config.max_text_positions	)

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

 UpperCamelCase_					:						Any =					SpeechTaFeatureExtractor()
 UpperCamelCase_					:						Union[str, Any] =					SpeechTaProcessor(tokenizer=lowerCamelCase  ,  feature_extractor=lowerCamelCase	)
 processor.save_pretrained(lowerCamelCase	)

 UpperCamelCase_					:						Any =					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__":
   a_    					=       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.'
   )

   a_    					=       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,
   )




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def    __lowercase     (						):
 UpperCamelCase_					:						Optional[Any] =					HfArgumentParser(lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					parser.parse_args_into_dataclasses()[0]
 UpperCamelCase_					:						Dict =					TensorFlowBenchmark(args=lowerCamelCase	)
 try:
  UpperCamelCase_					:						Any =					parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  UpperCamelCase_					:						Any =					'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  UpperCamelCase_					:						Optional[int] =					' '.join(str(lowerCamelCase	).split(' '	)[:-1]	)
  UpperCamelCase_					:						Any =					''
  UpperCamelCase_					:						Any =					eval(str(lowerCamelCase	).split(' '	)[-1]	)
  UpperCamelCase_					:						List[Any] =					[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in TensorFlowBenchmark.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]	)
   else:
    wrong_args.append(lowerCamelCase	)
  if len(lowerCamelCase	) > 0:
   UpperCamelCase_					:						List[str] =					full_error_msg + begin_error_msg + str(lowerCamelCase	)
  raise ValueError(lowerCamelCase	)
 benchmark.run()


if __name__ == "__main__":
   main()



