

'''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()
_UpperCamelCase						:  List[str]							       =				logging.get_logger("transformers.models.speecht5")

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


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


    """simple docstring"""


    for attribute in key.split('.'			):
        lowerCAmelCase 						=							getattr(A__					,		A__			)

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

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


def       snake_case   (	snake_case      : List[Any]					,		snake_case      : str			) ->  List[str]:


    """simple docstring"""


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


def       snake_case   (	snake_case      : List[str]					,		snake_case      : Union[str, Any]					,		snake_case      : Any			) ->  Optional[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							[]

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

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

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


    """simple docstring"""


    lowerCAmelCase 						=							full_name.split('conv_layers.'			)[-1]
    lowerCAmelCase 						=							name.split('.'			)
    lowerCAmelCase 						=							int(items[0]			)
    lowerCAmelCase 						=							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.'			)
            lowerCAmelCase 						=							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.'			)
            lowerCAmelCase 						=							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.'			)
            lowerCAmelCase 						=							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.'			)
            lowerCAmelCase 						=							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       snake_case   (	snake_case      : Union[str, Any]					,		snake_case      : Dict					,		snake_case      : Dict					,		snake_case      : List[Any]=None					,		snake_case      : int=None					,		snake_case      : List[str]=None					,		) ->  List[str]:


    """simple docstring"""


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

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

    if vocab_path:
        lowerCAmelCase 						=							SpeechTaTokenizer(A__					,		model_max_length=config.max_text_positions			)

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

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

    lowerCAmelCase 						=							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						:  Union[str, Any]							       =				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						:  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,
     )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__			:      str								={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__			:      Optional[Any]								=[
				    'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'CLIPSegModel',
				    'CLIPSegPreTrainedModel',
				    'CLIPSegTextModel',
				    'CLIPSegVisionModel',
				    'CLIPSegForImageSegmentation',
				]

if TYPE_CHECKING:
				from .configuration_clipseg import (
				    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    CLIPSegConfig,
				    CLIPSegTextConfig,
				    CLIPSegVisionConfig,
				)
				from .processing_clipseg import CLIPSegProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_clipseg import (
								    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CLIPSegForImageSegmentation,
								    CLIPSegModel,
								    CLIPSegPreTrainedModel,
								    CLIPSegTextModel,
								    CLIPSegVisionModel,
								)

else:
				import sys

				lowerCAmelCase__			:      str								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





