


'''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__:    Optional[int]					    =       logging.get_logger('''transformers.models.speecht5''')

A__:    Union[str, Any]					    =       {
    '''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__:    Union[str, Any]					    =       {
    '''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__:    Dict					    =       {
    '''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__:    str					    =       {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
A__:    str					    =       {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
A__:    Union[str, Any]					    =       {
    '''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__:    Dict					    =       {
    '''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__:    Optional[Any]					    =       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
A__:    Optional[int]					    =       {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A__:    Union[str, Any]					    =       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A__:    Union[str, Any]					    =       []
A__:    List[Any]					    =       [
    '''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__:    Optional[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__:    Tuple					    =       IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Dict    ,_UpperCAmelCase	:	Dict    ,_UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	int    ,_UpperCAmelCase	:	List[str]  )  ->			Union[str, Any]:

       for attribute in key.split("""."""  ):
              _a    : Optional[Any]     					=getattr(__a    ,__a  )

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

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


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	str  )  ->			str:

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


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	Dict  )  ->			str:
       _a    : int     					=[]

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

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

              _a    : List[Any]     					=False
              if "conv_layers" in name:
                     load_conv_layer(
                         __a    ,__a    ,__a    ,__a    ,hf_model.config.feat_extract_norm == """group"""    ,)
                     _a    : 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:
                                   _a    : List[str]     					=key.split(""".*."""  )
                                   if prefix in name and suffix in name:
                                          _a    : List[str]     					=suffix

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

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


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Dict    ,_UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	Union[str, Any]  )  ->			Union[str, Any]:
       _a    : int     					=full_name.split("""conv_layers."""  )[-1]
       _a    : List[str]     					=name.split("""."""  )
       _a    : int     					=int(items[0]  )
       _a    : Dict     					=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."  )
                     _a    : Optional[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."  )
                     _a    : Union[str, Any]     					=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."  )
                     _a    : Tuple     					=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."  )
                     _a    : Tuple     					=value
                     logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
       else:
              unused_weights.append(__a  )


@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Union[str, Any]    ,_UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	Tuple=None    ,_UpperCAmelCase	:	List[Any]=None    ,_UpperCAmelCase	:	str=None    ,)  ->			Any:

       if config_path is not None:
              _a    : str     					=SpeechTaConfig.from_pretrained(__a  )
       else:
              _a    : str     					=SpeechTaConfig()

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

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

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

       _a    : Union[str, Any]     					=SpeechTaFeatureExtractor()
       _a    : Union[str, Any]     					=SpeechTaProcessor(tokenizer=__a    ,feature_extractor=__a  )
       processor.save_pretrained(__a  )

       _a    : Optional[int]     					=torch.load(__a  )
       recursively_load_weights(fairseq_checkpoint["""model"""]    ,__a    ,__a  )

       model.save_pretrained(__a  )

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


if __name__ == "__main__":
   A__:    int					    =       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__:    Tuple					    =       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 diffusers.utils.testing_utils import require_onnxruntime

@require_onnxruntime
class 				UpperCAmelCase_		:
							"""simple docstring"""







							pass



