






"""simple docstring"""







import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


_UpperCamelCase					      =  {
    'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt',
    'tiny': 'https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt',
    'base.en': 'https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt',
    'base': 'https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt',
    'small.en': 'https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt',
    'small': 'https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt',
    'medium.en': 'https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt',
    'medium': 'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt',
    'large': 'https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt',
    'large-v2': 'https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt',
}

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	["""layers""", """blocks"""]
      for k in ignore_keys:
            state_dict.pop(lowercase__				,	lowercase__							)


_UpperCamelCase					      =  {
    'blocks': 'layers',
    'mlp.0': 'fc1',
    'mlp.2': 'fc2',
    'mlp_ln': 'final_layer_norm',
    '.attn.query': '.self_attn.q_proj',
    '.attn.key': '.self_attn.k_proj',
    '.attn.value': '.self_attn.v_proj',
    '.attn_ln': '.self_attn_layer_norm',
    '.attn.out': '.self_attn.out_proj',
    '.cross_attn.query': '.encoder_attn.q_proj',
    '.cross_attn.key': '.encoder_attn.k_proj',
    '.cross_attn.value': '.encoder_attn.v_proj',
    '.cross_attn_ln': '.encoder_attn_layer_norm',
    '.cross_attn.out': '.encoder_attn.out_proj',
    'decoder.ln.': 'decoder.layer_norm.',
    'encoder.ln.': 'encoder.layer_norm.',
    'token_embedding': 'embed_tokens',
    'encoder.positional_embedding': 'encoder.embed_positions.weight',
    'decoder.positional_embedding': 'decoder.embed_positions.weight',
    'ln_post': 'layer_norm',
}

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	list(s_dict.keys()							)
      for key in keys:
            UpperCAmelCase          =	key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        UpperCAmelCase          =	new_key.replace(lowercase__				,	lowercase__							)

            print(F'''{key} -> {new_key}'''							)

            UpperCAmelCase          =	s_dict.pop(lowercase__							)
      return s_dict

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	emb.weight.shape
      UpperCAmelCase          =	nn.Linear(lowercase__				,	lowercase__				,	bias=lowercase__							)
      UpperCAmelCase          =	emb.weight.data
      return lin_layer

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      os.makedirs(lowercase__				,	exist_ok=lowercase__							)
      UpperCAmelCase          =	os.path.basename(lowercase__							)

      UpperCAmelCase          =	url.split("""/"""							)[-2]
      UpperCAmelCase          =	os.path.join(lowercase__				,	lowercase__							)

      if os.path.exists(lowercase__							) and not os.path.isfile(lowercase__							):
            raise RuntimeError(F'''{download_target} exists and is not a regular file'''							)

      if os.path.isfile(lowercase__							):
            UpperCAmelCase          =	open(lowercase__				,	"""rb"""							).read()
            if hashlib.shaaaa(lowercase__							).hexdigest() == expected_shaaaa:
                  return model_bytes
            else:
                  warnings.warn(F'''{download_target} exists, but the SHA256 checksum does not match; re-downloading the file'''							)

      with urllib.request.urlopen(lowercase__							) as source, open(lowercase__				,	"""wb"""							) as output:
            with tqdm(
                total=int(source.info().get("""Content-Length"""							)							)				,	ncols=80				,	unit="""iB"""				,	unit_scale=lowercase__				,	unit_divisor=1024							) as loop:
                  while True:
                        UpperCAmelCase          =	source.read(8192							)
                        if not buffer:
                              break

                        output.write(lowercase__							)
                        loop.update(len(lowercase__							)							)

      UpperCAmelCase          =	open(lowercase__				,	"""rb"""							).read()
      if hashlib.shaaaa(lowercase__							).hexdigest() != expected_shaaaa:
            raise RuntimeError(
                """Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."""							)

      return model_bytes

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      if ".pt" not in checkpoint_path:
            UpperCAmelCase          =	_download(_MODELS[checkpoint_path]							)
      else:
            UpperCAmelCase          =	torch.load(lowercase__				,	map_location="""cpu"""							)
      UpperCAmelCase          =	original_checkpoint["""dims"""]
      UpperCAmelCase          =	original_checkpoint["""model_state_dict"""]
      UpperCAmelCase          =	state_dict["""decoder.token_embedding.weight"""]
      remove_ignore_keys_(lowercase__							)
      rename_keys(lowercase__							)
      UpperCAmelCase          =	True
      UpperCAmelCase          =	state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

      UpperCAmelCase          =	WhisperConfig(
          vocab_size=dimensions["""n_vocab"""]				,	encoder_ffn_dim=lowercase__				,	decoder_ffn_dim=lowercase__				,	num_mel_bins=dimensions["""n_mels"""]				,	d_model=dimensions["""n_audio_state"""]				,	max_target_positions=dimensions["""n_text_ctx"""]				,	encoder_layers=dimensions["""n_audio_layer"""]				,	encoder_attention_heads=dimensions["""n_audio_head"""]				,	decoder_layers=dimensions["""n_text_layer"""]				,	decoder_attention_heads=dimensions["""n_text_state"""]				,	max_source_positions=dimensions["""n_audio_ctx"""]				,	)

      UpperCAmelCase          =	WhisperForConditionalGeneration(lowercase__							)
      UpperCAmelCase      , UpperCAmelCase          =	model.model.load_state_dict(lowercase__				,	strict=lowercase__							)
      if len(lowercase__							) > 0 and not set(lowercase__							) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                F''' but all the following weights are missing {missing}'''							)

      if tie_embeds:
            UpperCAmelCase          =	make_linear_from_emb(model.model.decoder.embed_tokens							)
      else:
            UpperCAmelCase          =	proj_out_weights

      model.save_pretrained(lowercase__							)


if __name__ == "__main__":
  _UpperCamelCase					      =  argparse.ArgumentParser()
  # # Required parameters
  parser.add_argument("""--checkpoint_path""", type=str, help="""Patht to the downloaded checkpoints""")
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  _UpperCamelCase					      =  parser.parse_args()

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)














"""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()
_UpperCamelCase					      =  logging.get_logger("""transformers.models.speecht5""")

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

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      for attribute in key.split("""."""							):
            UpperCAmelCase          =	getattr(_snake_case				,	_snake_case							)

      if weight_type is not None:
            UpperCAmelCase          =	getattr(_snake_case				,	_snake_case							).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 _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      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 _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      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(_snake_case				,	_snake_case							):
                  logger.info(F'''{name} was ignored'''							)
                  continue

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

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

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      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(_snake_case							)

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







      """simple docstring"""






      if config_path is not None:
            UpperCAmelCase          =	SpeechTaConfig.from_pretrained(_snake_case							)
      else:
            UpperCAmelCase          =	SpeechTaConfig()

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

      if vocab_path:
            UpperCAmelCase          =	SpeechTaTokenizer(_snake_case				,	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=_snake_case				,	rstrip=_snake_case							)
            UpperCAmelCase          =	mask_token
            tokenizer.add_special_tokens({"""mask_token""": mask_token}							)
            tokenizer.add_tokens(["""<ctc_blank>"""]							)

      UpperCAmelCase          =	SpeechTaFeatureExtractor()
      UpperCAmelCase          =	SpeechTaProcessor(tokenizer=_snake_case				,	feature_extractor=_snake_case							)
      processor.save_pretrained(_snake_case							)

      UpperCAmelCase          =	torch.load(_snake_case							)
      recursively_load_weights(fairseq_checkpoint["""model"""]				,	_snake_case				,	_snake_case							)

      model.save_pretrained(_snake_case							)

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


if __name__ == "__main__":
  _UpperCamelCase					      =  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."""
  )

  _UpperCamelCase					      =  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,
  )



