
"""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()
A__      :	str          =    logging.get_logger("""transformers.models.speecht5""")

A__      :	List[str]          =    {
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
A__      :	Union[str, Any]          =    {
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
A__      :	str          =    {
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
A__      :	Optional[int]          =    {
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
A__      :	Tuple          =    {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
A__      :	int          =    {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
A__      :	int          =    {
    """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__      :	List[Any]          =    {
    """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__      :	List[Any]          =    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
A__      :	Tuple          =    {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A__      :	int          =    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A__      :	List[Any]          =    []
A__      :	int          =    [
    """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__      :	Any          =    IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
A__      :	Tuple          =    IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
A__      :	Union[str, Any]          =    IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]



def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Tuple		):






     '''simple docstring'''
     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__							: Tuple 							=						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__							: Union[str, Any] 							=						value
     elif weight_type == "weight_g":
          UpperCAmelCase__							: Optional[int] 							=						value
     elif weight_type == "weight_v":
          UpperCAmelCase__							: str 							=						value
     elif weight_type == "bias":
          UpperCAmelCase__							: str 							=						value
     elif weight_type == "running_mean":
          UpperCAmelCase__							: Tuple 							=						value
     elif weight_type == "running_var":
          UpperCAmelCase__							: Dict 							=						value
     elif weight_type == "num_batches_tracked":
          UpperCAmelCase__							: int 							=						value
     else:
          UpperCAmelCase__							: Any 							=						value

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



def 							a__ (				lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''
     for key in ignore_keys:
          if key.endswith(".*"		):
               if name.startswith(key[:-1]		):
                    return True
          elif ".*." in key:
               UpperCAmelCase__							: Dict 							=						key.split(".*."		)
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False



def 							a__ (				lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						[]

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                    if key in name:
                         UpperCAmelCase__							: str 							=						True
                         if "*" in mapped_key:
                              UpperCAmelCase__							: List[Any] 							=						name.split(lowerCAmelCase		)[0].split("."		)[-2]
                              UpperCAmelCase__							: Union[str, Any] 							=						mapped_key.replace("*"       ,    lowerCAmelCase		)
                         if "weight_g" in name:
                              UpperCAmelCase__							: Optional[int] 							=						"weight_g"
                         elif "weight_v" in name:
                              UpperCAmelCase__							: str 							=						"weight_v"
                         elif "bias" in name:
                              UpperCAmelCase__							: Union[str, Any] 							=						"bias"
                         elif "weight" in name:
                              UpperCAmelCase__							: List[Any] 							=						"weight"
                         elif "running_mean" in name:
                              UpperCAmelCase__							: int 							=						"running_mean"
                         elif "running_var" in name:
                              UpperCAmelCase__							: str 							=						"running_var"
                         elif "num_batches_tracked" in name:
                              UpperCAmelCase__							: Optional[Any] 							=						"num_batches_tracked"
                         else:
                              UpperCAmelCase__							: 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__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     UpperCAmelCase__							: List[Any] 							=						full_name.split("conv_layers."		)[-1]
     UpperCAmelCase__							: List[Any] 							=						name.split("."		)
     UpperCAmelCase__							: List[str] 							=						int(items[0]		)
     UpperCAmelCase__							: Union[str, 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__							: 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__							: Tuple 							=						value
               logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        F"{full_name} has size {value.shape}, but"
                        F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."		)
               UpperCAmelCase__							: 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__							: Optional[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 							a__ (				lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Union[str, Any]=None       ,    lowerCAmelCase	:       Union[str, Any]=None       ,    lowerCAmelCase	:       Any=None       ,    ):






     '''simple docstring'''
     if config_path is not None:
          UpperCAmelCase__							: Any 							=						SpeechTaConfig.from_pretrained(lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Any 							=						SpeechTaConfig()

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

     if vocab_path:
          UpperCAmelCase__							: List[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__							: Optional[int] 							=						SpeechTaFeatureExtractor()
     UpperCAmelCase__							: Union[str, 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__":
    A__      :	Dict          =    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__      :	Any          =    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 typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 7_68 ,			)->					Union[str, Any]:
          super().__init__()

          UpperCAmelCase__							: str 							=						nn.Parameter(torch.zeros(1 ,			__UpperCamelCase  )  )
          UpperCAmelCase__							: Optional[int] 							=						nn.Parameter(torch.ones(1 ,			__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					Any:
          UpperCAmelCase__							: Dict 							=						nn.Parameter(self.mean.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          UpperCAmelCase__							: Any 							=						nn.Parameter(self.std.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          return self



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						(embeds - self.mean) * 1.0 / self.std
          return embeds



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						(embeds * self.std) + self.mean
          return embeds



