



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

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





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			str			,_UpperCamelCase      :			Optional[Any]			,_UpperCamelCase      :			Optional[int]			,_UpperCamelCase      :			int				):
   for attribute in key.split('''.'''				):
      __lowerCamelCase    				=     getattr(_UpperCamelCase			,_UpperCamelCase				)

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

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





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			List[str]				):
   for key in ignore_keys:
      if key.endswith('''.*'''				):
         if name.startswith(key[:-1]				):
            return True
      elif ".*." in key:
         __lowerCamelCase				,__lowerCamelCase    				=     key.split('''.*.'''				)
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False





def 		a__      (		_UpperCamelCase      :			int			,_UpperCamelCase      :			Dict			,_UpperCamelCase      :			List[Any]				):
   __lowerCamelCase    				=     []

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

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

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

   if task == "s2t":
      __lowerCamelCase    				=     config.max_text_positions
      __lowerCamelCase    				=     SpeechTaForSpeechToText(_UpperCamelCase				)
   elif task == "t2s":
      __lowerCamelCase    				=     18_76
      __lowerCamelCase    				=     6_00
      __lowerCamelCase    				=     config.max_speech_positions
      __lowerCamelCase    				=     SpeechTaForTextToSpeech(_UpperCamelCase				)
   elif task == "s2s":
      __lowerCamelCase    				=     18_76
      __lowerCamelCase    				=     config.max_speech_positions
      __lowerCamelCase    				=     SpeechTaForSpeechToSpeech(_UpperCamelCase				)
   else:
      raise ValueError(F"""Unknown task name: {task}"""				)

   if vocab_path:
      __lowerCamelCase    				=     SpeechTaTokenizer(_UpperCamelCase			,model_max_length=config.max_text_positions				)

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

   __lowerCamelCase    				=     SpeechTaFeatureExtractor()
   __lowerCamelCase    				=     SpeechTaProcessor(tokenizer=_UpperCamelCase			,feature_extractor=_UpperCamelCase				)
   processor.save_pretrained(_UpperCamelCase				)

   __lowerCamelCase    				=     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_								=       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_								=       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,
  )











from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_								=       logging.get_logger(__name__)

a_								=       {
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""canine"""




 def __init__(    self						,					__UpperCAmelCase=768						,					__UpperCAmelCase=12						,					__UpperCAmelCase=12						,					__UpperCAmelCase=3072						,					__UpperCAmelCase="gelu"						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=16384						,					__UpperCAmelCase=16						,					__UpperCAmelCase=0.02						,					__UpperCAmelCase=1E-1_2						,					__UpperCAmelCase=0						,					__UpperCAmelCase=0Xe_000						,					__UpperCAmelCase=0Xe_001						,					__UpperCAmelCase=4						,					__UpperCAmelCase=4						,					__UpperCAmelCase=8						,					__UpperCAmelCase=16384						,					__UpperCAmelCase=128						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    super().__init__(pad_token_id=__UpperCAmelCase						,					bos_token_id=__UpperCAmelCase						,					eos_token_id=__UpperCAmelCase						,					**__UpperCAmelCase					)

    __lowerCamelCase    				=     max_position_embeddings
    __lowerCamelCase    				=     hidden_size
    __lowerCamelCase    				=     num_hidden_layers
    __lowerCamelCase    				=     num_attention_heads
    __lowerCamelCase    				=     intermediate_size
    __lowerCamelCase    				=     hidden_act
    __lowerCamelCase    				=     hidden_dropout_prob
    __lowerCamelCase    				=     attention_probs_dropout_prob
    __lowerCamelCase    				=     initializer_range
    __lowerCamelCase    				=     type_vocab_size
    __lowerCamelCase    				=     layer_norm_eps

    # Character config:
    __lowerCamelCase    				=     downsampling_rate
    __lowerCamelCase    				=     upsampling_kernel_size
    __lowerCamelCase    				=     num_hash_functions
    __lowerCamelCase    				=     num_hash_buckets
    __lowerCamelCase    				=     local_transformer_stride






