
"""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       :						Tuple       		=		logging.get_logger('''transformers.models.speecht5''')

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


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    for attribute in key.split("""."""   ):
        _UpperCAmelCase        = getattr(A__       ,   A__   )

    if weight_type is not None:
        _UpperCAmelCase        = getattr(A__       ,   A__   ).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 			_UpperCamelCase						(    _A       ,   _A   )     ->	str:





    """simple docstring"""

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


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	List[Any]:





    """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(A__       ,   A__   ):
            logger.info(F"""{name} was ignored"""   )
            continue

        _UpperCAmelCase        = False
        if "conv_layers" in name:
            load_conv_layer(
                A__       ,   A__       ,   A__       ,   A__       ,   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        = 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(A__   )[0].split("""."""   )[-2]
                        _UpperCAmelCase        = mapped_key.replace("""*"""       ,   A__   )
                    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(A__       ,   A__       ,   A__       ,   A__       ,   A__   )
                continue
        if not is_used:
            unused_weights.append(A__   )

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


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A   )     ->	List[Any]:





    """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(A__   )


@torch.no_grad()
def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A=None       ,   _A=None       ,   _A=None       ,   )     ->	str:





    """simple docstring"""

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

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

    if vocab_path:
        _UpperCAmelCase        = SpeechTaTokenizer(A__       ,   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=A__       ,   rstrip=A__   )
        _UpperCAmelCase        = mask_token
        tokenizer.add_special_tokens({"""mask_token""": mask_token}   )
        tokenizer.add_tokens(["""<ctc_blank>"""]   )

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

    _UpperCAmelCase        = 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       :						List[str]       		=		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       :						List[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"""
import os
from typing import Dict, List, Tuple, TypeVar, Union


lowerCamelCase_							      = TypeVar('''T''')

lowerCamelCase_							      = Union[List[T], Tuple[T, ...]]
lowerCamelCase_							      = Union[T, List[T], Dict[str, T]]
lowerCamelCase_							      = Union[str, bytes, os.PathLike]



