






'''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()
__lowerCamelCase :       Dict 				=       logging.get_logger('''transformers.models.speecht5''')

__lowerCamelCase :       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''',
}
__lowerCamelCase :       Dict 				=       {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__lowerCamelCase :       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''',
}
__lowerCamelCase :       Tuple 				=       {
    '''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''',
}
__lowerCamelCase :       int 				=       {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__lowerCamelCase :       Optional[Any] 				=       {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__lowerCamelCase :       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''',
}
__lowerCamelCase :       Tuple 				=       {
    '''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''',
}
__lowerCamelCase :       Dict 				=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__lowerCamelCase :       Optional[Any] 				=       {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase :       List[Any] 				=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase :       Optional[int] 				=       []
__lowerCamelCase :       Any 				=       [
    '''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''',
]
__lowerCamelCase :       Optional[Any] 				=       IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
__lowerCamelCase :       Any 				=       IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
__lowerCamelCase :       List[Any] 				=       IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]

def 						__UpperCAmelCase							(			__magic_name__  ,__magic_name__  ,__magic_name__  ,__magic_name__  ,__magic_name__    )->     Optional[Any]:



			"""simple docstring"""



			for attribute in key.split("."    ):
						snake_case_					:       int =							getattr(lowerCAmelCase_  ,lowerCAmelCase_    )

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

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

def 						__UpperCAmelCase							(			__magic_name__  ,__magic_name__    )->     Optional[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_					:       Optional[Any] =							key.split(".*."    )
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False

def 						__UpperCAmelCase							(			__magic_name__  ,__magic_name__  ,__magic_name__    )->     int:



			"""simple docstring"""



			snake_case_					:       Optional[int] =							[]

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

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

						snake_case_					:       List[Any] =							False
						if "conv_layers" in name:
									load_conv_layer(
									    lowerCAmelCase_  ,lowerCAmelCase_  ,lowerCAmelCase_  ,lowerCAmelCase_  ,hf_model.config.feat_extract_norm == "group"  ,)
									snake_case_					:       Tuple =							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_					:       int =							key.split(".*."    )
															if prefix in name and suffix in name:
																		snake_case_					:       Union[str, Any] =							suffix

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

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

def 						__UpperCAmelCase							(			__magic_name__  ,__magic_name__  ,__magic_name__  ,__magic_name__  ,__magic_name__    )->     Any:



			"""simple docstring"""



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

@torch.no_grad()
def 						__UpperCAmelCase							(			__magic_name__  ,__magic_name__  ,__magic_name__  ,__magic_name__=None  ,__magic_name__=None  ,__magic_name__=None  ,)->     Tuple:



			"""simple docstring"""



			if config_path is not None:
						snake_case_					:       List[Any] =							SpeechTaConfig.from_pretrained(lowerCAmelCase_    )
			else:
						snake_case_					:       Union[str, Any] =							SpeechTaConfig()

			if task == "s2t":
						snake_case_					:       Optional[int] =							config.max_text_positions
						snake_case_					:       List[str] =							SpeechTaForSpeechToText(lowerCAmelCase_    )
			elif task == "t2s":
						snake_case_					:       List[str] =							1876
						snake_case_					:       str =							600
						snake_case_					:       Dict =							config.max_speech_positions
						snake_case_					:       Dict =							SpeechTaForTextToSpeech(lowerCAmelCase_    )
			elif task == "s2s":
						snake_case_					:       str =							1876
						snake_case_					:       List[str] =							config.max_speech_positions
						snake_case_					:       Tuple =							SpeechTaForSpeechToSpeech(lowerCAmelCase_    )
			else:
						raise ValueError(F'''Unknown task name: {task}'''    )

			if vocab_path:
						snake_case_					:       Any =							SpeechTaTokenizer(lowerCAmelCase_  ,model_max_length=config.max_text_positions    )

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

			snake_case_					:       List[str] =							SpeechTaFeatureExtractor()
			snake_case_					:       Optional[Any] =							SpeechTaProcessor(tokenizer=lowerCAmelCase_  ,feature_extractor=lowerCAmelCase_    )
			processor.save_pretrained(lowerCAmelCase_    )

			snake_case_					:       List[Any] =							torch.load(lowerCAmelCase_    )
			recursively_load_weights(fairseq_checkpoint["model"]  ,lowerCAmelCase_  ,lowerCAmelCase_    )

			model.save_pretrained(lowerCAmelCase_    )

			if repo_id:
						print("Pushing to the hub..."    )
						processor.push_to_hub(lowerCAmelCase_    )
						model.push_to_hub(lowerCAmelCase_    )


if __name__ == "__main__":
		__lowerCamelCase :       Dict 				=       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.'''
		)

		__lowerCamelCase :       Tuple 				=       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"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


snake_case   					=				{'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case   					=				['''HerbertTokenizerFast''']


if TYPE_CHECKING:
     from .tokenization_herbert import HerbertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_herbert_fast import HerbertTokenizerFast

else:
     import sys

     snake_case   					=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





