
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__ (				snake_case    ,       snake_case    ,       snake_case    ,       snake_case    ,       snake_case				):





  """simple docstring"""
  for attribute in key.split('''.'''				):
    __SCREAMING_SNAKE_CASE	:     List[str] =						getattr(lowerCAmelCase__    ,       lowerCAmelCase__				)

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

  if task == "s2t":
    __SCREAMING_SNAKE_CASE	:     Dict =						hf_model.speechta.encoder.prenet.feature_encoder
    __SCREAMING_SNAKE_CASE	:     Any =						MAPPING_S2T
    __SCREAMING_SNAKE_CASE	:     List[Any] =						IGNORE_KEYS_S2T
  elif task == "t2s":
    __SCREAMING_SNAKE_CASE	:     Optional[Any] =						None
    __SCREAMING_SNAKE_CASE	:     Optional[Any] =						MAPPING_T2S
    __SCREAMING_SNAKE_CASE	:     Tuple =						IGNORE_KEYS_T2S
  elif task == "s2s":
    __SCREAMING_SNAKE_CASE	:     str =						hf_model.speechta.encoder.prenet.feature_encoder
    __SCREAMING_SNAKE_CASE	:     Dict =						MAPPING_S2S
    __SCREAMING_SNAKE_CASE	:     List[Any] =						IGNORE_KEYS_S2S
  else:
    raise ValueError(F'''Unsupported task: {task}'''				)

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

    __SCREAMING_SNAKE_CASE	:     Optional[Any] =						False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__    ,       hf_model.config.feat_extract_norm == '''group'''    ,       )
      __SCREAMING_SNAKE_CASE	:     str =						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:
          __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE	:     Tuple =						key.split('''.*.'''				)
          if prefix in name and suffix in name:
            __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          __SCREAMING_SNAKE_CASE	:     int =						True
          if "*" in mapped_key:
            __SCREAMING_SNAKE_CASE	:     str =						name.split(lowerCAmelCase__				)[0].split('''.'''				)[-2]
            __SCREAMING_SNAKE_CASE	:     Tuple =						mapped_key.replace('''*'''    ,       lowerCAmelCase__				)
          if "weight_g" in name:
            __SCREAMING_SNAKE_CASE	:     str =						'''weight_g'''
          elif "weight_v" in name:
            __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						'''weight_v'''
          elif "bias" in name:
            __SCREAMING_SNAKE_CASE	:     Optional[Any] =						'''bias'''
          elif "weight" in name:
            __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						'''weight'''
          elif "running_mean" in name:
            __SCREAMING_SNAKE_CASE	:     Any =						'''running_mean'''
          elif "running_var" in name:
            __SCREAMING_SNAKE_CASE	:     Optional[Any] =						'''running_var'''
          elif "num_batches_tracked" in name:
            __SCREAMING_SNAKE_CASE	:     Optional[Any] =						'''num_batches_tracked'''
          else:
            __SCREAMING_SNAKE_CASE	:     Dict =						None
          set_recursively(lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__				)
        continue
    if not is_used:
      unused_weights.append(lowerCAmelCase__				)

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


def 				a__ (				snake_case    ,       snake_case    ,       snake_case    ,       snake_case    ,       snake_case				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						full_name.split('''conv_layers.'''				)[-1]
  __SCREAMING_SNAKE_CASE	:     Optional[Any] =						name.split('''.'''				)
  __SCREAMING_SNAKE_CASE	:     Tuple =						int(items[0]				)
  __SCREAMING_SNAKE_CASE	:     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.'''				)
      __SCREAMING_SNAKE_CASE	:     str =						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.'''				)
      __SCREAMING_SNAKE_CASE	:     List[str] =						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.'''				)
      __SCREAMING_SNAKE_CASE	:     str =						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.'''				)
      __SCREAMING_SNAKE_CASE	:     str =						value
      logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
  else:
    unused_weights.append(lowerCAmelCase__				)


@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:
    __SCREAMING_SNAKE_CASE	:     str =						SpeechTaConfig.from_pretrained(lowerCAmelCase__				)
  else:
    __SCREAMING_SNAKE_CASE	:     Tuple =						SpeechTaConfig()

  if task == "s2t":
    __SCREAMING_SNAKE_CASE	:     Tuple =						config.max_text_positions
    __SCREAMING_SNAKE_CASE	:     Optional[Any] =						SpeechTaForSpeechToText(lowerCAmelCase__				)
  elif task == "t2s":
    __SCREAMING_SNAKE_CASE	:     Dict =						1_876
    __SCREAMING_SNAKE_CASE	:     Optional[int] =						600
    __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						config.max_speech_positions
    __SCREAMING_SNAKE_CASE	:     Tuple =						SpeechTaForTextToSpeech(lowerCAmelCase__				)
  elif task == "s2s":
    __SCREAMING_SNAKE_CASE	:     str =						1_876
    __SCREAMING_SNAKE_CASE	:     Dict =						config.max_speech_positions
    __SCREAMING_SNAKE_CASE	:     Optional[Any] =						SpeechTaForSpeechToSpeech(lowerCAmelCase__				)
  else:
    raise ValueError(F'''Unknown task name: {task}'''				)

  if vocab_path:
    __SCREAMING_SNAKE_CASE	:     str =						SpeechTaTokenizer(lowerCAmelCase__    ,       model_max_length=config.max_text_positions				)

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

  __SCREAMING_SNAKE_CASE	:     Tuple =						SpeechTaFeatureExtractor()
  __SCREAMING_SNAKE_CASE	:     Dict =						SpeechTaProcessor(tokenizer=lowerCAmelCase__    ,       feature_extractor=lowerCAmelCase__				)
  processor.save_pretrained(lowerCAmelCase__				)

  __SCREAMING_SNAKE_CASE	:     str =						torch.load(lowerCAmelCase__				)
  recursively_load_weights(fairseq_checkpoint['''model''']    ,       lowerCAmelCase__    ,       lowerCAmelCase__				)

  model.save_pretrained(lowerCAmelCase__				)

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


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,
   )




"""simple docstring"""




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def        lowercase							(		lowerCAmelCase__ :     Namespace			) ->      Tuple:
		return ConvertCommand(
		    args.model_type    ,					args.tf_checkpoint    ,					args.pytorch_dump_output    ,					args.config    ,					args.finetuning_task_name			)


lowercase_			     =  "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"




class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		@staticmethod
		def 			__UpperCAmelCase       (  _a      ):
				__a           =			parser.add_parser(
				    '''convert'''							, help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''							, )
				train_parser.add_argument('''--model_type'''							, type=_a							, required=_a							, help='''Model\'s type.'''      )
				train_parser.add_argument(
				    '''--tf_checkpoint'''							, type=_a							, required=_a							, help='''TensorFlow checkpoint path or folder.'''      )
				train_parser.add_argument(
				    '''--pytorch_dump_output'''							, type=_a							, required=_a							, help='''Path to the PyTorch saved model output.'''      )
				train_parser.add_argument('''--config'''							, type=_a							, default=''''''							, help='''Configuration file path or folder.'''      )
				train_parser.add_argument(
				    '''--finetuning_task_name'''							, type=_a							, default=_a							, help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							, )
				train_parser.set_defaults(func=_a      )

		def __init__(  self							, _a							, _a							, _a							, _a							, _a							, *_a							, ):
				__a           =			logging.get_logger('''transformers-cli/converting'''      )

				self._logger.info(f'''Loading model {model_type}'''      )
				__a           =			model_type
				__a           =			tf_checkpoint
				__a           =			pytorch_dump_output
				__a           =			config
				__a           =			finetuning_task_name





		def 			__UpperCAmelCase       (  self      ):
				if self._model_type == "albert":
						try:
								from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "bert":
						try:
								from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "funnel":
						try:
								from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "t5":
						try:
								from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "gpt":
						from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
						    convert_openai_checkpoint_to_pytorch,
						)

						convert_openai_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "transfo_xl":
						try:
								from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
								    convert_transfo_xl_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						if "ckpt" in self._tf_checkpoint.lower():
								__a           =			self._tf_checkpoint
								__a           =			''''''
						else:
								__a           =			self._tf_checkpoint
								__a           =			''''''
						convert_transfo_xl_checkpoint_to_pytorch(
						    _a							, self._config							, self._pytorch_dump_output							, _a      )
				elif self._model_type == "gpt2":
						try:
								from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
								    convert_gpta_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "xlnet":
						try:
								from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
								    convert_xlnet_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_xlnet_checkpoint_to_pytorch(
						    self._tf_checkpoint							, self._config							, self._pytorch_dump_output							, self._finetuning_task_name      )
				elif self._model_type == "xlm":
						from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
						    convert_xlm_checkpoint_to_pytorch,
						)

						convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint							, self._pytorch_dump_output      )
				elif self._model_type == "lxmert":
						from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
						    convert_lxmert_checkpoint_to_pytorch,
						)

						convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint							, self._pytorch_dump_output      )
				elif self._model_type == "rembert":
						from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
						    convert_rembert_tf_checkpoint_to_pytorch,
						)

						convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				else:
						raise ValueError(
						    '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''      )






