






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 				__lowerCamelCase     (							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Optional[Any]							)     ->  Any:
   for attribute in key.split("""."""							):
      __UpperCamelCase	:			Union[str, Any]					=					getattr(__lowerCAmelCase					,							__lowerCAmelCase							)

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

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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					Any							)     ->  Any:
   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 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Tuple							)     ->  str:
   __UpperCamelCase	:			int					=					[]

   if task == "s2t":
      __UpperCamelCase	:			List[Any]					=					hf_model.speechta.encoder.prenet.feature_encoder
      __UpperCamelCase	:			int					=					MAPPING_S2T
      __UpperCamelCase	:			Union[str, Any]					=					IGNORE_KEYS_S2T
   elif task == "t2s":
      __UpperCamelCase	:			List[Any]					=					None
      __UpperCamelCase	:			Dict					=					MAPPING_T2S
      __UpperCamelCase	:			List[Any]					=					IGNORE_KEYS_T2S
   elif task == "s2s":
      __UpperCamelCase	:			int					=					hf_model.speechta.encoder.prenet.feature_encoder
      __UpperCamelCase	:			Tuple					=					MAPPING_S2S
      __UpperCamelCase	:			int					=					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

      __UpperCamelCase	:			List[str]					=					False
      if "conv_layers" in name:
         load_conv_layer(
             __lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							hf_model.config.feat_extract_norm == """group"""					,							)
         __UpperCamelCase	:			Union[str, Any]					=					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	:			int					=					suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
            if key in name:
               __UpperCamelCase	:			Optional[int]					=					True
               if "*" in mapped_key:
                  __UpperCamelCase	:			List[Any]					=					name.split(__lowerCAmelCase							)[0].split("""."""							)[-2]
                  __UpperCamelCase	:			Optional[Any]					=					mapped_key.replace("""*"""					,							__lowerCAmelCase							)
               if "weight_g" in name:
                  __UpperCamelCase	:			str					=					"""weight_g"""
               elif "weight_v" in name:
                  __UpperCamelCase	:			Optional[int]					=					"""weight_v"""
               elif "bias" in name:
                  __UpperCamelCase	:			str					=					"""bias"""
               elif "weight" in name:
                  __UpperCamelCase	:			Any					=					"""weight"""
               elif "running_mean" in name:
                  __UpperCamelCase	:			List[Any]					=					"""running_mean"""
               elif "running_var" in name:
                  __UpperCamelCase	:			Any					=					"""running_var"""
               elif "num_batches_tracked" in name:
                  __UpperCamelCase	:			Dict					=					"""num_batches_tracked"""
               else:
                  __UpperCamelCase	:			str					=					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 				__lowerCamelCase     (							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Tuple							)     ->  Any:
   __UpperCamelCase	:			str					=					full_name.split("""conv_layers."""							)[-1]
   __UpperCamelCase	:			List[Any]					=					name.split("""."""							)
   __UpperCamelCase	:			Dict					=					int(items[0]							)
   __UpperCamelCase	:			Optional[Any]					=					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	:			Union[str, Any]					=					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	:			int					=					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	:			List[Any]					=					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	:			Any					=					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 				__lowerCamelCase     (							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Any=None					,							__lowerCAmelCase							:					List[str]=None					,							__lowerCAmelCase							:					Dict=None					,							)     ->  Optional[Any]:
   if config_path is not None:
      __UpperCamelCase	:			int					=					SpeechTaConfig.from_pretrained(__lowerCAmelCase							)
   else:
      __UpperCamelCase	:			Optional[Any]					=					SpeechTaConfig()

   if task == "s2t":
      __UpperCamelCase	:			Optional[int]					=					config.max_text_positions
      __UpperCamelCase	:			Union[str, Any]					=					SpeechTaForSpeechToText(__lowerCAmelCase							)
   elif task == "t2s":
      __UpperCamelCase	:			str					=					1876
      __UpperCamelCase	:			Optional[int]					=					600
      __UpperCamelCase	:			str					=					config.max_speech_positions
      __UpperCamelCase	:			Union[str, Any]					=					SpeechTaForTextToSpeech(__lowerCAmelCase							)
   elif task == "s2s":
      __UpperCamelCase	:			List[Any]					=					1876
      __UpperCamelCase	:			Tuple					=					config.max_speech_positions
      __UpperCamelCase	:			str					=					SpeechTaForSpeechToSpeech(__lowerCAmelCase							)
   else:
      raise ValueError(f'Unknown task name: {task}'							)

   if vocab_path:
      __UpperCamelCase	:			Union[str, Any]					=					SpeechTaTokenizer(__lowerCAmelCase					,							model_max_length=config.max_text_positions							)

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

   __UpperCamelCase	:			Tuple					=					SpeechTaFeatureExtractor()
   __UpperCamelCase	:			List[Any]					=					SpeechTaProcessor(tokenizer=__lowerCAmelCase					,							feature_extractor=__lowerCAmelCase							)
   processor.save_pretrained(__lowerCAmelCase							)

   __UpperCamelCase	:			int					=					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__":
       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,
       )











import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase    							=      logging.get_logger(__name__)







class 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	str   ,				*lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     warnings.warn(
         """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use BeitImageProcessor instead."""   ,				lowerCamelCase__   ,				)
     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)



