

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

lowercase_             =     {
    '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',
}
lowercase_             =     {
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
lowercase_             =     {
    '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',
}
lowercase_             =     {
    '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',
}
lowercase_             =     {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
lowercase_             =     {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
lowercase_             =     {
    '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',
}
lowercase_             =     {
    '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',
}
lowercase_             =     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowercase_             =     {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase_             =     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase_             =     []
lowercase_             =     [
    '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',
]
lowercase_             =     IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
lowercase_             =     IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
lowercase_             =     IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
def   a   (    A__			:     Tuple   ,     A__			:     int   ,     A__			:     List[str]   ,     A__			:     Any   ,     A__			:     Dict  )					->   str:





      """simple docstring"""



      for attribute in key.split('.'  ):
            _lowercase			 =getattr(A__   ,     A__  )

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

      logger.info(F'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'''  )
def   a   (    A__			:     Optional[Any]   ,     A__			:     List[str]  )					->   Any:





      """simple docstring"""



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





      """simple docstring"""



      _lowercase			 =[]

      if task == "s2t":
            _lowercase			 =hf_model.speechta.encoder.prenet.feature_encoder
            _lowercase			 =MAPPING_S2T
            _lowercase			 =IGNORE_KEYS_S2T
      elif task == "t2s":
            _lowercase			 =None
            _lowercase			 =MAPPING_T2S
            _lowercase			 =IGNORE_KEYS_T2S
      elif task == "s2s":
            _lowercase			 =hf_model.speechta.encoder.prenet.feature_encoder
            _lowercase			 =MAPPING_S2S
            _lowercase			 =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

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                        if key in name:
                              _lowercase			 =True
                              if "*" in mapped_key:
                                    _lowercase			 =name.split(A__  )[0].split('.'  )[-2]
                                    _lowercase			 =mapped_key.replace('*'   ,     A__  )
                              if "weight_g" in name:
                                    _lowercase			 ='weight_g'
                              elif "weight_v" in name:
                                    _lowercase			 ='weight_v'
                              elif "bias" in name:
                                    _lowercase			 ='bias'
                              elif "weight" in name:
                                    _lowercase			 ='weight'
                              elif "running_mean" in name:
                                    _lowercase			 ='running_mean'
                              elif "running_var" in name:
                                    _lowercase			 ='running_var'
                              elif "num_batches_tracked" in name:
                                    _lowercase			 ='num_batches_tracked'
                              else:
                                    _lowercase			 =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   a   (    A__			:     int   ,     A__			:     Optional[int]   ,     A__			:     Optional[Any]   ,     A__			:     Optional[int]   ,     A__			:     Tuple  )					->   Tuple:





      """simple docstring"""



      _lowercase			 =full_name.split('conv_layers.'  )[-1]
      _lowercase			 =name.split('.'  )
      _lowercase			 =int(items[0]  )
      _lowercase			 =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.'''  )
                  _lowercase			 =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.'''  )
                  _lowercase			 =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.'''  )
                  _lowercase			 =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.'''  )
                  _lowercase			 =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   a   (    A__			:     int   ,     A__			:     Optional[Any]   ,     A__			:     List[str]   ,     A__			:     Dict=None   ,     A__			:     Any=None   ,     A__			:     int=None   ,     )					->   str:





      """simple docstring"""



      if config_path is not None:
            _lowercase			 =SpeechTaConfig.from_pretrained(A__  )
      else:
            _lowercase			 =SpeechTaConfig()

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

      if vocab_path:
            _lowercase			 =SpeechTaTokenizer(A__   ,     model_max_length=config.max_text_positions  )

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

      _lowercase			 =SpeechTaFeatureExtractor()
      _lowercase			 =SpeechTaProcessor(tokenizer=A__   ,     feature_extractor=A__  )
      processor.save_pretrained(A__  )

      _lowercase			 =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__":
  lowercase_             =     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.'
  )

  lowercase_             =     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   a   (    A__			:     Optional[int]  )					->   Tuple:





      """simple docstring"""



      _lowercase			 =[0] * len(A__  )
      _lowercase			 =[]
      _lowercase			 =[]
      _lowercase			 =0

      for values in graph.values():
            for i in values:
                  indegree[i] += 1

      for i in range(len(A__  )  ):
            if indegree[i] == 0:
                  queue.append(A__  )

      while queue:
            _lowercase			 =queue.pop(0  )
            cnt += 1
            topo.append(A__  )
            for x in graph[vertex]:
                  indegree[x] -= 1
                  if indegree[x] == 0:
                        queue.append(A__  )

      if cnt != len(A__  ):
            print('Cycle exists'  )
      else:
            print(A__  )


# Adjacency List of Graph
lowercase_             =     {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


