






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()
snake_case__      :	List[str]       	=      logging.get_logger("""transformers.models.speecht5""")

snake_case__      :	Optional[Any]       	=      {
    """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""",
}
snake_case__      :	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""",
}
snake_case__      :	List[Any]       	=      {
    """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""",
}
snake_case__      :	List[str]       	=      {
    """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""",
}
snake_case__      :	Tuple       	=      {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
snake_case__      :	Dict       	=      {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
snake_case__      :	int       	=      {
    """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""",
}
snake_case__      :	str       	=      {
    """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""",
}
snake_case__      :	Dict       	=      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
snake_case__      :	Tuple       	=      {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
snake_case__      :	Dict       	=      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
snake_case__      :	Dict       	=      []
snake_case__      :	Optional[Any]       	=      [
    """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""",
]
snake_case__      :	Tuple       	=      IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
snake_case__      :	Any       	=      IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
snake_case__      :	int       	=      IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]






def     _snake_case    (	__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):




   for attribute in key.split('.'):
      UpperCamelCase_									=     getattr(__a    ,      __a)

   if weight_type is not None:
      UpperCamelCase_									=     getattr(__a    ,      __a).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     _snake_case    (	__lowercase    ,      __lowercase):




   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     _snake_case    (	__lowercase    ,      __lowercase    ,      __lowercase):
   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(__a    ,      __a):
         logger.info(f"""{name} was ignored""")
         continue

      UpperCamelCase_									=     False
      if "conv_layers" in name:
         load_conv_layer(
             __a    ,      __a    ,      __a    ,      __a    ,      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(__a)[0].split('.')[-2]
                  UpperCamelCase_									=     mapped_key.replace('*'    ,      __a)
               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(__a    ,      __a    ,      __a    ,      __a    ,      __a)
            continue
      if not is_used:
         unused_weights.append(__a)

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






def     _snake_case    (	__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):
   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(__a)






@torch.no_grad()
def     _snake_case    (	__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase=None    ,      __lowercase=None    ,      __lowercase=None    ,      ):




   if config_path is not None:
      UpperCamelCase_									=     SpeechTaConfig.from_pretrained(__a)
   else:
      UpperCamelCase_									=     SpeechTaConfig()

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

   if vocab_path:
      UpperCamelCase_									=     SpeechTaTokenizer(__a    ,      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=__a    ,      rstrip=__a)
      UpperCamelCase_									=     mask_token
      tokenizer.add_special_tokens({'mask_token': mask_token})
      tokenizer.add_tokens(['<ctc_blank>'])

   UpperCamelCase_									=     SpeechTaFeatureExtractor()
   UpperCamelCase_									=     SpeechTaProcessor(tokenizer=__a    ,      feature_extractor=__a)
   processor.save_pretrained(__a)

   UpperCamelCase_									=     torch.load(__a)
   recursively_load_weights(fairseq_checkpoint['model']    ,      __a    ,      __a)

   model.save_pretrained(__a)

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


if __name__ == "__main__":
 snake_case__      :	Optional[int]       	=      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."""
 )

 snake_case__      :	List[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,
 )













def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   return not any(
       neighbour == 1 and colored_vertices[i] == color
       for i, neighbour in enumerate(__lowercase))






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):

   # Base Case
   if index == len(__lowercase):
      return True

   # Recursive Step
   for i in range(__lowercase):
      if valid_coloring(graph[index]    ,      __lowercase    ,      __lowercase):
         # Color current vertex
         UpperCamelCase_									=     i
         # Validate coloring
         if util_color(__lowercase    ,      __lowercase    ,      __lowercase    ,      index + 1):
            return True
         # Backtrack
         UpperCamelCase_									=     -1
   return False






def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     [-1] * len(__lowercase)

   if util_color(__lowercase    ,      __lowercase    ,      __lowercase    ,      0):
      return colored_vertices

   return []





