






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_						:						List[Any]						      =					logging.get_logger('transformers.models.speecht5')

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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Tuple:


  for attribute in key.split('.'    ):
    UpperCamelCase_:   List[Any]        =							getattr(UpperCAmelCase__      ,       UpperCAmelCase__    )

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

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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Optional[int]:


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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							List[str]:
  UpperCamelCase_:   Optional[Any]        =							[]

  if task == "s2t":
    UpperCamelCase_:   Any        =							hf_model.speechta.encoder.prenet.feature_encoder
    UpperCamelCase_:   int        =							MAPPING_S2T
    UpperCamelCase_:   Dict        =							IGNORE_KEYS_S2T
  elif task == "t2s":
    UpperCamelCase_:   Optional[int]        =							None
    UpperCamelCase_:   Dict        =							MAPPING_T2S
    UpperCamelCase_:   Union[str, Any]        =							IGNORE_KEYS_T2S
  elif task == "s2s":
    UpperCamelCase_:   Any        =							hf_model.speechta.encoder.prenet.feature_encoder
    UpperCamelCase_:   Optional[int]        =							MAPPING_S2S
    UpperCamelCase_:   Dict        =							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_:   Dict        =							False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       hf_model.config.feat_extract_norm == 'group'      ,       )
      UpperCamelCase_:   Tuple        =							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_:   List[Any]        =							key.split('.*.'    )
          if prefix in name and suffix in name:
            UpperCamelCase_:   Tuple        =							suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          UpperCamelCase_:   Dict        =							True
          if "*" in mapped_key:
            UpperCamelCase_:   Any        =							name.split(UpperCAmelCase__    )[0].split('.'    )[-2]
            UpperCamelCase_:   Tuple        =							mapped_key.replace('*'      ,       UpperCAmelCase__    )
          if "weight_g" in name:
            UpperCamelCase_:   Any        =							'weight_g'
          elif "weight_v" in name:
            UpperCamelCase_:   List[Any]        =							'weight_v'
          elif "bias" in name:
            UpperCamelCase_:   Union[str, Any]        =							'bias'
          elif "weight" in name:
            UpperCamelCase_:   int        =							'weight'
          elif "running_mean" in name:
            UpperCamelCase_:   Dict        =							'running_mean'
          elif "running_var" in name:
            UpperCamelCase_:   List[str]        =							'running_var'
          elif "num_batches_tracked" in name:
            UpperCamelCase_:   str        =							'num_batches_tracked'
          else:
            UpperCamelCase_:   int        =							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     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Optional[int]:
  UpperCamelCase_:   str        =							full_name.split('conv_layers.'    )[-1]
  UpperCamelCase_:   List[Any]        =							name.split('.'    )
  UpperCamelCase_:   Any        =							int(items[0]    )
  UpperCamelCase_:   List[str]        =							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_:   Optional[int]        =							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_:   Tuple        =							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_:   int        =							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_:   Tuple        =							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     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=None      ,       )			->							Tuple:


  if config_path is not None:
    UpperCamelCase_:   Tuple        =							SpeechTaConfig.from_pretrained(UpperCAmelCase__    )
  else:
    UpperCamelCase_:   Optional[Any]        =							SpeechTaConfig()

  if task == "s2t":
    UpperCamelCase_:   int        =							config.max_text_positions
    UpperCamelCase_:   Optional[Any]        =							SpeechTaForSpeechToText(UpperCAmelCase__    )
  elif task == "t2s":
    UpperCamelCase_:   Optional[int]        =							1_8_7_6
    UpperCamelCase_:   str        =							6_0_0
    UpperCamelCase_:   List[str]        =							config.max_speech_positions
    UpperCamelCase_:   str        =							SpeechTaForTextToSpeech(UpperCAmelCase__    )
  elif task == "s2s":
    UpperCamelCase_:   Dict        =							1_8_7_6
    UpperCamelCase_:   str        =							config.max_speech_positions
    UpperCamelCase_:   Dict        =							SpeechTaForSpeechToSpeech(UpperCAmelCase__    )
  else:
    raise ValueError(F'''Unknown task name: {task}'''    )

  if vocab_path:
    UpperCamelCase_:   Optional[int]        =							SpeechTaTokenizer(UpperCAmelCase__      ,       model_max_length=config.max_text_positions    )

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

  UpperCamelCase_:   Tuple        =							SpeechTaFeatureExtractor()
  UpperCamelCase_:   List[str]        =							SpeechTaProcessor(tokenizer=UpperCAmelCase__      ,       feature_extractor=UpperCAmelCase__    )
  processor.save_pretrained(UpperCAmelCase__    )

  UpperCamelCase_:   Union[str, Any]        =							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__":
   A_						:						List[Any]						      =					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_						:						int						      =					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'''


from math import pi, sqrt
def 						snake_case_       (UpperCamelCase			:    float ):





							'''simple docstring'''


							if num <= 0:
														raise ValueError('''math domain error''' )
							if num > 171.5:
														raise OverflowError('''math range error''' )
							elif num - int(UpperCamelCase ) not in (0, 0.5):
														raise NotImplementedError('''num must be an integer or a half-integer''' )
							elif num == 0.5:
														return sqrt(UpperCamelCase )
							else:
														return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def 						snake_case_       ():





							'''simple docstring'''


							assert gamma(0.5 ) == sqrt(UpperCamelCase )
							assert gamma(1 ) == 1.0
							assert gamma(2 ) == 1.0


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				_snake_case : Optional[Any]  				=						1.0
				while num:
								_snake_case : Dict  				=						float(input('Gamma of: '))
								print(F'''gamma({num}) = {gamma(num)}''')
								print('\nEnter 0 to exit...')

