





"""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_			=      logging.get_logger("""transformers.models.speecht5""")

a_			=      {
    """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_			=      {
    """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_			=      {
    """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_			=      {
    """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_			=      {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
a_			=      {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
a_			=      {
    """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_			=      {
    """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_			=      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a_			=      {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a_			=      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a_			=      []
a_			=      [
    """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_			=      IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
a_			=      IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
a_			=      IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]







def 		__lowercase					(       snake_case_  :  str    ,snake_case_  :  Dict    ,snake_case_  :  Tuple    ,snake_case_  :  Optional[Any]    ,snake_case_  :  str						)       ->List[Any]:



    '''simple docstring'''







    for attribute in key.split('''.'''						):
        __A   :						Union[str, Any]									=					getattr(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE						)

    if weight_type is not None:
        __A   :						Optional[Any]									=					getattr(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE						).shape
    else:
        __A   :						Optional[int]									=					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   :						int									=					value
    elif weight_type == "weight_v":
        __A   :						int									=					value
    elif weight_type == "bias":
        __A   :						Dict									=					value
    elif weight_type == "running_mean":
        __A   :						List[Any]									=					value
    elif weight_type == "running_var":
        __A   :						Optional[int]									=					value
    elif weight_type == "num_batches_tracked":
        __A   :						Optional[int]									=					value
    else:
        __A   :						List[str]									=					value

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







def 		__lowercase					(       snake_case_  :  Optional[int]    ,snake_case_  :  Union[str, Any]						)       ->Any:



    '''simple docstring'''







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







def 		__lowercase					(       snake_case_  :  Dict    ,snake_case_  :  Tuple    ,snake_case_  :  Dict						)       ->Union[str, Any]:



    '''simple docstring'''







    __A   :						Dict									=					[]

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

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

        __A   :						Tuple									=					False
        if "conv_layers" in name:
            load_conv_layer(
                _SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,hf_model.config.feat_extract_norm == '''group'''    ,)
            __A   :						List[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   :						Any									=					key.split('''.*.'''						)
                    if prefix in name and suffix in name:
                        __A   :						int									=					suffix

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

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







def 		__lowercase					(       snake_case_  :  Tuple    ,snake_case_  :  Dict    ,snake_case_  :  List[str]    ,snake_case_  :  int    ,snake_case_  :  List[Any]						)       ->str:



    '''simple docstring'''







    __A   :						List[str]									=					full_name.split('''conv_layers.'''						)[-1]
    __A   :						str									=					name.split('''.'''						)
    __A   :						str									=					int(items[0]						)
    __A   :						List[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."""						)
            __A   :						Dict									=					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   :						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."""						)
            __A   :						int									=					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   :						Any									=					value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
    else:
        unused_weights.append(_SCREAMING_SNAKE_CASE						)







@torch.no_grad()
def 		__lowercase					(       snake_case_  :  Tuple    ,snake_case_  :  Union[str, Any]    ,snake_case_  :  str    ,snake_case_  :  Tuple=None    ,snake_case_  :  Optional[Any]=None    ,snake_case_  :  Optional[int]=None    ,)       ->Any:



    '''simple docstring'''







    if config_path is not None:
        __A   :						Union[str, Any]									=					SpeechTaConfig.from_pretrained(_SCREAMING_SNAKE_CASE						)
    else:
        __A   :						Optional[Any]									=					SpeechTaConfig()

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

    if vocab_path:
        __A   :						List[str]									=					SpeechTaTokenizer(_SCREAMING_SNAKE_CASE    ,model_max_length=config.max_text_positions						)

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

    __A   :						Tuple									=					SpeechTaFeatureExtractor()
    __A   :						Any									=					SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,feature_extractor=_SCREAMING_SNAKE_CASE						)
    processor.save_pretrained(_SCREAMING_SNAKE_CASE						)

    __A   :						List[str]									=					torch.load(_SCREAMING_SNAKE_CASE						)
    recursively_load_weights(fairseq_checkpoint['''model''']    ,_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE						)

    model.save_pretrained(_SCREAMING_SNAKE_CASE						)

    if repo_id:
        print('''Pushing to the hub...'''						)
        processor.push_to_hub(_SCREAMING_SNAKE_CASE						)
        model.push_to_hub(_SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
       a_			=      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_			=      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"""



a_			=      {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
a_			=      {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}







def 		__lowercase					(       snake_case_  :  float    ,snake_case_  :  str    ,snake_case_  :  str						)       ->float:



    '''simple docstring'''







    __A   :						Tuple									=					from_type.lower().strip('''s'''						)
    __A   :						Optional[int]									=					to_type.lower().strip('''s'''						)

    __A   :						List[str]									=					UNIT_SYMBOL.get(snake_case_    ,snake_case_						)
    __A   :						Any									=					UNIT_SYMBOL.get(snake_case_    ,snake_case_						)

    if from_sanitized not in METRIC_CONVERSION:
        __A   :						int									=					(
            F"""Invalid 'from_type' value: {from_type!r}.\n"""
            F"""Conversion abbreviations are: {', '.join(snake_case_						)}"""
        )
        raise ValueError(snake_case_						)
    if to_sanitized not in METRIC_CONVERSION:
        __A   :						str									=					(
            F"""Invalid 'to_type' value: {to_type!r}.\n"""
            F"""Conversion abbreviations are: {', '.join(snake_case_						)}"""
        )
        raise ValueError(snake_case_						)
    __A   :						Optional[Any]									=					METRIC_CONVERSION[from_sanitized]
    __A   :						Optional[int]									=					METRIC_CONVERSION[to_sanitized]
    __A   :						Union[str, Any]									=					1

    if from_exponent > to_exponent:
        __A   :						Dict									=					from_exponent - to_exponent
    else:
        __A   :						Union[str, Any]									=					-(to_exponent - from_exponent)

    return value * pow(10    ,snake_case_						)


if __name__ == "__main__":
       from doctest import testmod

       testmod()




