

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__  :      Dict			=    logging.get_logger("transformers.models.speecht5")

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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


    for attribute in key.split('.'       ):
        SCREAMING_SNAKE_CASE_									=						getattr(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

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

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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		Tuple       ):





    """simple docstring"""


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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]

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

        SCREAMING_SNAKE_CASE_									=						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' ,   )
            SCREAMING_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:
                    SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						key.split('.*.'       )
                    if prefix in name and suffix in name:
                        SCREAMING_SNAKE_CASE_									=						suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                if key in name:
                    SCREAMING_SNAKE_CASE_									=						True
                    if "*" in mapped_key:
                        SCREAMING_SNAKE_CASE_									=						name.split(_SCREAMING_SNAKE_CASE       )[0].split('.'       )[-2]
                        SCREAMING_SNAKE_CASE_									=						mapped_key.replace('*' ,   _SCREAMING_SNAKE_CASE       )
                    if "weight_g" in name:
                        SCREAMING_SNAKE_CASE_									=						'weight_g'
                    elif "weight_v" in name:
                        SCREAMING_SNAKE_CASE_									=						'weight_v'
                    elif "bias" in name:
                        SCREAMING_SNAKE_CASE_									=						'bias'
                    elif "weight" in name:
                        SCREAMING_SNAKE_CASE_									=						'weight'
                    elif "running_mean" in name:
                        SCREAMING_SNAKE_CASE_									=						'running_mean'
                    elif "running_var" in name:
                        SCREAMING_SNAKE_CASE_									=						'running_var'
                    elif "num_batches_tracked" in name:
                        SCREAMING_SNAKE_CASE_									=						'num_batches_tracked'
                    else:
                        SCREAMING_SNAKE_CASE_									=						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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						full_name.split('conv_layers.'       )[-1]
    SCREAMING_SNAKE_CASE_									=						name.split('.'       )
    SCREAMING_SNAKE_CASE_									=						int(items[0]       )
    SCREAMING_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."""       )
            SCREAMING_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."""       )
            SCREAMING_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."""       )
            SCREAMING_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."""       )
            SCREAMING_SNAKE_CASE_									=						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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]=None ,   _SCREAMING_SNAKE_CASE					:		List[str]=None ,   _SCREAMING_SNAKE_CASE					:		Dict=None ,   ):





    """simple docstring"""


    if config_path is not None:
        SCREAMING_SNAKE_CASE_									=						SpeechTaConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
    else:
        SCREAMING_SNAKE_CASE_									=						SpeechTaConfig()

    if task == "s2t":
        SCREAMING_SNAKE_CASE_									=						config.max_text_positions
        SCREAMING_SNAKE_CASE_									=						SpeechTaForSpeechToText(_SCREAMING_SNAKE_CASE       )
    elif task == "t2s":
        SCREAMING_SNAKE_CASE_									=						1_876
        SCREAMING_SNAKE_CASE_									=						600
        SCREAMING_SNAKE_CASE_									=						config.max_speech_positions
        SCREAMING_SNAKE_CASE_									=						SpeechTaForTextToSpeech(_SCREAMING_SNAKE_CASE       )
    elif task == "s2s":
        SCREAMING_SNAKE_CASE_									=						1_876
        SCREAMING_SNAKE_CASE_									=						config.max_speech_positions
        SCREAMING_SNAKE_CASE_									=						SpeechTaForSpeechToSpeech(_SCREAMING_SNAKE_CASE       )
    else:
        raise ValueError(f"""Unknown task name: {task}"""       )

    if vocab_path:
        SCREAMING_SNAKE_CASE_									=						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
        SCREAMING_SNAKE_CASE_									=						AddedToken('<mask>' ,   lstrip=_SCREAMING_SNAKE_CASE ,   rstrip=_SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE_									=						mask_token
        tokenizer.add_special_tokens({'mask_token': mask_token}       )
        tokenizer.add_tokens(['<ctc_blank>']       )

    SCREAMING_SNAKE_CASE_									=						SpeechTaFeatureExtractor()
    SCREAMING_SNAKE_CASE_									=						SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,   feature_extractor=_SCREAMING_SNAKE_CASE       )
    processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						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__":
       UpperCamelCase__  :      Tuple			=    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__  :      int			=    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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__  :      Any			=    {
    "configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
    "tokenization_mvp": ["MvpTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Optional[int]			=    ["MvpTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      str			=    [
           "MVP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MvpForCausalLM",
           "MvpForConditionalGeneration",
           "MvpForQuestionAnswering",
           "MvpForSequenceClassification",
           "MvpModel",
           "MvpPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__  :      Dict			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


