





"""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()
lowerCAmelCase_			=							logging.get_logger('''transformers.models.speecht5''')

lowerCAmelCase_			=							{
    '''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''',
}
lowerCAmelCase_			=							{
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
lowerCAmelCase_			=							{
    '''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''',
}
lowerCAmelCase_			=							{
    '''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''',
}
lowerCAmelCase_			=							{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
lowerCAmelCase_			=							{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
lowerCAmelCase_			=							{
    '''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''',
}
lowerCAmelCase_			=							{
    '''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''',
}
lowerCAmelCase_			=							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCAmelCase_			=							{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase_			=							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase_			=							[]
lowerCAmelCase_			=							[
    '''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''',
]
lowerCAmelCase_			=							IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
lowerCAmelCase_			=							IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
lowerCAmelCase_			=							IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Any:






       """simple docstring"""




       for attribute in key.split('.'							):
              _UpperCAmelCase     =     getattr(lowerCAmelCase__,lowerCAmelCase__							)

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

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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Optional[int]:






       """simple docstring"""




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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     []

       if task == "s2t":
              _UpperCAmelCase     =     hf_model.speechta.encoder.prenet.feature_encoder
              _UpperCAmelCase     =     MAPPING_S2T
              _UpperCAmelCase     =     IGNORE_KEYS_S2T
       elif task == "t2s":
              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     MAPPING_T2S
              _UpperCAmelCase     =     IGNORE_KEYS_T2S
       elif task == "s2s":
              _UpperCAmelCase     =     hf_model.speechta.encoder.prenet.feature_encoder
              _UpperCAmelCase     =     MAPPING_S2S
              _UpperCAmelCase     =     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     =     False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase__,lowerCAmelCase__,lowerCAmelCase__,lowerCAmelCase__,hf_model.config.feat_extract_norm == 'group',)
                     _UpperCAmelCase     =     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     =     key.split('.*.'							)
                                   if prefix in name and suffix in name:
                                          _UpperCAmelCase     =     suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                            if key in name:
                                   _UpperCAmelCase     =     True
                                   if "*" in mapped_key:
                                          _UpperCAmelCase     =     name.split(lowerCAmelCase__							)[0].split('.'							)[-2]
                                          _UpperCAmelCase     =     mapped_key.replace('*',lowerCAmelCase__							)
                                   if "weight_g" in name:
                                          _UpperCAmelCase     =     'weight_g'
                                   elif "weight_v" in name:
                                          _UpperCAmelCase     =     'weight_v'
                                   elif "bias" in name:
                                          _UpperCAmelCase     =     'bias'
                                   elif "weight" in name:
                                          _UpperCAmelCase     =     'weight'
                                   elif "running_mean" in name:
                                          _UpperCAmelCase     =     'running_mean'
                                   elif "running_var" in name:
                                          _UpperCAmelCase     =     'running_var'
                                   elif "num_batches_tracked" in name:
                                          _UpperCAmelCase     =     'num_batches_tracked'
                                   else:
                                          _UpperCAmelCase     =     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    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       List[str]:






       """simple docstring"""




       _UpperCAmelCase     =     full_name.split('conv_layers.'							)[-1]
       _UpperCAmelCase     =     name.split('.'							)
       _UpperCAmelCase     =     int(items[0]							)
       _UpperCAmelCase     =     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     =     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     =     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     =     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     =     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    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=None,)		->       List[Any]:






       """simple docstring"""




       if config_path is not None:
              _UpperCAmelCase     =     SpeechTaConfig.from_pretrained(lowerCAmelCase__							)
       else:
              _UpperCAmelCase     =     SpeechTaConfig()

       if task == "s2t":
              _UpperCAmelCase     =     config.max_text_positions
              _UpperCAmelCase     =     SpeechTaForSpeechToText(lowerCAmelCase__							)
       elif task == "t2s":
              _UpperCAmelCase     =     1876
              _UpperCAmelCase     =     600
              _UpperCAmelCase     =     config.max_speech_positions
              _UpperCAmelCase     =     SpeechTaForTextToSpeech(lowerCAmelCase__							)
       elif task == "s2s":
              _UpperCAmelCase     =     1876
              _UpperCAmelCase     =     config.max_speech_positions
              _UpperCAmelCase     =     SpeechTaForSpeechToSpeech(lowerCAmelCase__							)
       else:
              raise ValueError(F"""Unknown task name: {task}"""							)

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

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

       _UpperCAmelCase     =     SpeechTaFeatureExtractor()
       _UpperCAmelCase     =     SpeechTaProcessor(tokenizer=lowerCAmelCase__,feature_extractor=lowerCAmelCase__							)
       processor.save_pretrained(lowerCAmelCase__							)

       _UpperCAmelCase     =     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__":
    lowerCAmelCase_			=							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.'''
    )

    lowerCAmelCase_			=							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"""






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           'decoder.output_projection.weight',
           '_float_tensor',
           'encoder.embed_positions._float_tensor',
           'decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase    ,							_UpperCAmelCase     =     emb.weight.shape
       _UpperCAmelCase     =     nn.Linear(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,bias=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     emb.weight.data
       return lin_layer

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     torch.load(SCREAMING_SNAKE_CASE,map_location='cpu'							)
       _UpperCAmelCase     =     mam_aaa['args'] or mam_aaa['cfg']['model']
       _UpperCAmelCase     =     mam_aaa['model']
       remove_ignore_keys_(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     state_dict['encoder.embed_tokens.weight'].shape[0]

       _UpperCAmelCase     =     MaMaaaConfig(
           vocab_size=SCREAMING_SNAKE_CASE,max_position_embeddings=1024,encoder_layers=args.encoder_layers,decoder_layers=args.decoder_layers,encoder_attention_heads=args.encoder_attention_heads,decoder_attention_heads=args.decoder_attention_heads,encoder_ffn_dim=args.encoder_ffn_embed_dim,decoder_ffn_dim=args.decoder_ffn_embed_dim,d_model=args.encoder_embed_dim,encoder_layerdrop=args.encoder_layerdrop,decoder_layerdrop=args.decoder_layerdrop,dropout=args.dropout,attention_dropout=args.attention_dropout,activation_dropout=args.activation_dropout,activation_function='relu',)

       _UpperCAmelCase     =     state_dict['decoder.embed_tokens.weight']
       _UpperCAmelCase     =     MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE							)
       model.model.load_state_dict(SCREAMING_SNAKE_CASE,strict=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     make_linear_from_emb(model.model.shared							)

       return model


if __name__ == "__main__":
    lowerCAmelCase_			=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    lowerCAmelCase_			=							parser.parse_args()
    lowerCAmelCase_			=							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)






