






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

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







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Dict:
      for attribute in key.split("""."""   ):
            UpperCamelCase      = getattr(__UpperCamelCase		,						__UpperCamelCase   )

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

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







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Union[str, Any]:
      for key in ignore_keys:
            if key.endswith(""".*"""   ):
                  if name.startswith(key[:-1]   ):
                        return True
            elif ".*." in key:
                  UpperCamelCase  ,UpperCamelCase      = key.split(""".*."""   )
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Tuple:
      UpperCamelCase      = []

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

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

            UpperCamelCase      = False
            if "conv_layers" in name:
                  load_conv_layer(
                      __UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						hf_model.config.feat_extract_norm == """group"""		,						)
                  UpperCamelCase      = 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      = key.split(""".*."""   )
                              if prefix in name and suffix in name:
                                    UpperCamelCase      = suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                        if key in name:
                              UpperCamelCase      = True
                              if "*" in mapped_key:
                                    UpperCamelCase      = name.split(__UpperCamelCase   )[0].split("""."""   )[-2]
                                    UpperCamelCase      = mapped_key.replace("""*"""		,						__UpperCamelCase   )
                              if "weight_g" in name:
                                    UpperCamelCase      = """weight_g"""
                              elif "weight_v" in name:
                                    UpperCamelCase      = """weight_v"""
                              elif "bias" in name:
                                    UpperCamelCase      = """bias"""
                              elif "weight" in name:
                                    UpperCamelCase      = """weight"""
                              elif "running_mean" in name:
                                    UpperCamelCase      = """running_mean"""
                              elif "running_var" in name:
                                    UpperCamelCase      = """running_var"""
                              elif "num_batches_tracked" in name:
                                    UpperCamelCase      = """num_batches_tracked"""
                              else:
                                    UpperCamelCase      = None
                              set_recursively(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )
                        continue
            if not is_used:
                  unused_weights.append(__UpperCamelCase   )

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







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      UpperCamelCase      = full_name.split("""conv_layers."""   )[-1]
      UpperCamelCase      = name.split("""."""   )
      UpperCamelCase      = int(items[0]   )
      UpperCamelCase      = 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      = 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      = 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      = 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      = value
                  logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
      else:
            unused_weights.append(__UpperCamelCase   )







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						)->			Optional[int]:
      if config_path is not None:
            UpperCamelCase      = SpeechTaConfig.from_pretrained(__UpperCamelCase   )
      else:
            UpperCamelCase      = SpeechTaConfig()

      if task == "s2t":
            UpperCamelCase      = config.max_text_positions
            UpperCamelCase      = SpeechTaForSpeechToText(__UpperCamelCase   )
      elif task == "t2s":
            UpperCamelCase      = 1876
            UpperCamelCase      = 600
            UpperCamelCase      = config.max_speech_positions
            UpperCamelCase      = SpeechTaForTextToSpeech(__UpperCamelCase   )
      elif task == "s2s":
            UpperCamelCase      = 1876
            UpperCamelCase      = config.max_speech_positions
            UpperCamelCase      = SpeechTaForSpeechToSpeech(__UpperCamelCase   )
      else:
            raise ValueError(F"Unknown task name: {task}"   )

      if vocab_path:
            UpperCamelCase      = SpeechTaTokenizer(__UpperCamelCase		,						model_max_length=config.max_text_positions   )

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

      UpperCamelCase      = SpeechTaFeatureExtractor()
      UpperCamelCase      = SpeechTaProcessor(tokenizer=__UpperCamelCase		,						feature_extractor=__UpperCamelCase   )
      processor.save_pretrained(__UpperCamelCase   )

      UpperCamelCase      = torch.load(__UpperCamelCase   )
      recursively_load_weights(fairseq_checkpoint["""model"""]		,						__UpperCamelCase		,						__UpperCamelCase   )

      model.save_pretrained(__UpperCamelCase   )

      if repo_id:
            print("""Pushing to the hub..."""   )
            processor.push_to_hub(__UpperCamelCase   )
            model.push_to_hub(__UpperCamelCase   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       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.'
     )

     SCREAMING_SNAKE_CASE__     =       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,
     )








'''simple docstring'''

import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE__     =       'scheduler_config.json'






class     a_						(      lowerCamelCase    ):
  lowercase							=				1
  lowercase							=				2
  lowercase							=				3
  lowercase							=				4
  lowercase							=				5
  lowercase							=				6
  lowercase							=				7
  lowercase							=				8
  lowercase							=				9
  lowercase							=				10
  lowercase							=				11
  lowercase							=				12
  lowercase							=				13
  lowercase							=				14






@dataclass
class     a_						(      lowerCamelCase    ):
  lowercase							=				42






class     a_						:
  lowercase							=				SCHEDULER_CONFIG_NAME
  lowercase							=				[]
  lowercase							=				True

  @classmethod
  def   A__						(				cls      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE=False      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = cls.load_config(
            pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE      ,		subfolder=_SCREAMING_SNAKE_CASE      ,		return_unused_kwargs=_SCREAMING_SNAKE_CASE      ,		return_commit_hash=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)
        return cls.from_config(_SCREAMING_SNAKE_CASE      ,		return_unused_kwargs=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = False      ,		**_SCREAMING_SNAKE_CASE				)  ->				Dict:



        """simple docstring"""






        self.save_config(save_directory=_SCREAMING_SNAKE_CASE      ,		push_to_hub=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return self._get_compatibles()


  @classmethod
  def   A__						(				cls				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = list(set([cls.__name__] + cls._compatibles				)				)
        UpperCamelCase      = importlib.import_module(__name__.split("""."""				)[0]				)
        UpperCamelCase      = [
            getattr(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				) for c in compatible_classes_str if hasattr(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        ]
        return compatible_classes
