

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__      ( __A    :Optional[int]	,__A    :Optional[int]	,__A    :int	,__A    :str	,__A    :Optional[int]		):


       """simple docstring"""




       for attribute in key.split("""."""		):
              __snake_case       	=							getattr(__A	,__A		)

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

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







def 						lowerCamelCase__      ( __A    :str	,__A    :Union[str, Any]		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Union[str, Any]	,__A    :Union[str, Any]	,__A    :List[str]		):


       """simple docstring"""




       __snake_case       	=							[]

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

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

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


       """simple docstring"""




       __snake_case       	=							full_name.split("""conv_layers."""		)[-1]
       __snake_case       	=							name.split("""."""		)
       __snake_case       	=							int(items[0]		)
       __snake_case       	=							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.'		)
                     __snake_case       	=							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.'		)
                     __snake_case       	=							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.'		)
                     __snake_case       	=							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.'		)
                     __snake_case       	=							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 						lowerCamelCase__      ( __A    :List[str]	,__A    :Dict	,__A    :Optional[Any]	,__A    :List[Any]=None	,__A    :Tuple=None	,__A    :Dict=None	,):


       """simple docstring"""




       if config_path is not None:
              __snake_case       	=							SpeechTaConfig.from_pretrained(__A		)
       else:
              __snake_case       	=							SpeechTaConfig()

       if task == "s2t":
              __snake_case       	=							config.max_text_positions
              __snake_case       	=							SpeechTaForSpeechToText(__A		)
       elif task == "t2s":
              __snake_case       	=							1_8_7_6
              __snake_case       	=							6_0_0
              __snake_case       	=							config.max_speech_positions
              __snake_case       	=							SpeechTaForTextToSpeech(__A		)
       elif task == "s2s":
              __snake_case       	=							1_8_7_6
              __snake_case       	=							config.max_speech_positions
              __snake_case       	=							SpeechTaForSpeechToSpeech(__A		)
       else:
              raise ValueError(F'Unknown task name: {task}'		)

       if vocab_path:
              __snake_case       	=							SpeechTaTokenizer(__A	,model_max_length=config.max_text_positions		)

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

       __snake_case       	=							SpeechTaFeatureExtractor()
       __snake_case       	=							SpeechTaProcessor(tokenizer=__A	,feature_extractor=__A		)
       processor.save_pretrained(__A		)

       __snake_case       	=							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__":
    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 enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


UpperCamelCase__		 =     get_logger(__name__)

class     __snake_case    (				enum.Enum  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							"all_checks"
       __SCREAMING_SNAKE_CASE				    =							"basic_checks"
       __SCREAMING_SNAKE_CASE				    =							"no_checks"






class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""














def 						lowerCamelCase__      ( __A    :Optional[dict]	,__A    :dict	,__A    :Any=None		):


       """simple docstring"""




       if expected_checksums is None:
              logger.info("""Unable to verify checksums."""		)
              return
       if len(set(__A		) - set(__A		)		) > 0:
              raise ExpectedMoreDownloadedFiles(str(set(__A		) - set(__A		)		)		)
       if len(set(__A		) - set(__A		)		) > 0:
              raise UnexpectedDownloadedFile(str(set(__A		) - set(__A		)		)		)
       __snake_case       	=							[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
       __snake_case       	=							""" for """ + verification_name if verification_name is not None else """"""
       if len(__A		) > 0:
              raise NonMatchingChecksumError(
                  F'Checksums didn\'t match{for_verification_name}:\n'
                  F'{bad_urls}\n'
                  """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""		)
       logger.info("""All the checksums matched successfully""" + for_verification_name		)

class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""














def 						lowerCamelCase__      ( __A    :Optional[dict]	,__A    :dict		):


       """simple docstring"""




       if expected_splits is None:
              logger.info("""Unable to verify splits sizes."""		)
              return
       if len(set(__A		) - set(__A		)		) > 0:
              raise ExpectedMoreSplits(str(set(__A		) - set(__A		)		)		)
       if len(set(__A		) - set(__A		)		) > 0:
              raise UnexpectedSplits(str(set(__A		) - set(__A		)		)		)
       __snake_case       	=							[
           {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
           for name in expected_splits
           if expected_splits[name].num_examples != recorded_splits[name].num_examples
       ]
       if len(__A		) > 0:
              raise NonMatchingSplitsSizesError(str(__A		)		)
       logger.info("""All the splits matched successfully."""		)







def 						lowerCamelCase__      ( __A    :str	,__A    :bool = True		):


       """simple docstring"""




       if record_checksum:
              __snake_case       	=							shaaaa()
              with open(__A	,"""rb"""		) as f:
                     for chunk in iter(lambda: f.read(1 << 2_0		)	,b""""""		):
                            m.update(__A		)
                     __snake_case       	=							m.hexdigest()
       else:
              __snake_case       	=							None
       return {"num_bytes": os.path.getsize(__A		), "checksum": checksum}







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       if dataset_size and config.IN_MEMORY_MAX_SIZE:
              return dataset_size < config.IN_MEMORY_MAX_SIZE
       else:
              return False

