






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

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



def 						lowercase    (				__snake_case				: List[str]		,					__snake_case				: Union[str, Any]		,					__snake_case				: Tuple		,					__snake_case				: int		,					__snake_case				: int	):
		for attribute in key.split('''.'''	):
				lowercase_ : int							       =   getattr(__snake_case		,					__snake_case	)

		if weight_type is not None:
				lowercase_ : Tuple							       =   getattr(__snake_case		,					__snake_case	).shape
		else:
				lowercase_ : Tuple							       =   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":
				lowercase_ : List[Any]							       =   value
		elif weight_type == "weight_g":
				lowercase_ : Optional[Any]							       =   value
		elif weight_type == "weight_v":
				lowercase_ : int							       =   value
		elif weight_type == "bias":
				lowercase_ : Optional[Any]							       =   value
		elif weight_type == "running_mean":
				lowercase_ : Any							       =   value
		elif weight_type == "running_var":
				lowercase_ : Tuple							       =   value
		elif weight_type == "num_batches_tracked":
				lowercase_ : Union[str, Any]							       =   value
		else:
				lowercase_ : 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				: Union[str, Any]		,					__snake_case				: str	):
		for key in ignore_keys:
				if key.endswith('''.*'''	):
						if name.startswith(key[:-1]	):
								return True
				elif ".*." in key:
						lowercase_       ,					lowercase_ : List[str]							       =   key.split('''.*.'''	)
						if prefix in name and suffix in name:
								return True
				elif key in name:
						return True
		return False



def 						lowercase    (				__snake_case				: int		,					__snake_case				: Any		,					__snake_case				: Optional[Any]	):
		lowercase_ : Optional[int]							       =   []

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

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

				lowercase_ : Union[str, Any]							       =   False
				if "conv_layers" in name:
						load_conv_layer(
						    __snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					hf_model.config.feat_extract_norm == '''group'''		,					)
						lowercase_ : Union[str, 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:
										lowercase_       ,					lowercase_ : int							       =   key.split('''.*.'''	)
										if prefix in name and suffix in name:
												lowercase_ : Optional[Any]							       =   suffix

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

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



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



@torch.no_grad()
def 						lowercase    (				__snake_case				: str		,					__snake_case				: List[Any]		,					__snake_case				: Optional[Any]		,					__snake_case				: List[Any]=None		,					__snake_case				: Union[str, Any]=None		,					__snake_case				: List[str]=None		,					):
		if config_path is not None:
				lowercase_ : List[Any]							       =   SpeechTaConfig.from_pretrained(__snake_case	)
		else:
				lowercase_ : Union[str, Any]							       =   SpeechTaConfig()

		if task == "s2t":
				lowercase_ : Dict							       =   config.max_text_positions
				lowercase_ : Union[str, Any]							       =   SpeechTaForSpeechToText(__snake_case	)
		elif task == "t2s":
				lowercase_ : int							       =   1_8_7_6
				lowercase_ : Any							       =   6_0_0
				lowercase_ : int							       =   config.max_speech_positions
				lowercase_ : Optional[Any]							       =   SpeechTaForTextToSpeech(__snake_case	)
		elif task == "s2s":
				lowercase_ : Dict							       =   1_8_7_6
				lowercase_ : Any							       =   config.max_speech_positions
				lowercase_ : int							       =   SpeechTaForSpeechToSpeech(__snake_case	)
		else:
				raise ValueError(F'''Unknown task name: {task}'''	)

		if vocab_path:
				lowercase_ : str							       =   SpeechTaTokenizer(__snake_case		,					model_max_length=config.max_text_positions	)

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

		lowercase_ : Dict							       =   SpeechTaFeatureExtractor()
		lowercase_ : Any							       =   SpeechTaProcessor(tokenizer=__snake_case		,					feature_extractor=__snake_case	)
		processor.save_pretrained(__snake_case	)

		lowercase_ : Optional[int]							       =   torch.load(__snake_case	)
		recursively_load_weights(fairseq_checkpoint['''model''']		,					__snake_case		,					__snake_case	)

		model.save_pretrained(__snake_case	)

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


if __name__ == "__main__":
						__A		:       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.'''
						)

						__A		:       Optional[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,
						)











"""simple docstring"""



__A		:       Any  				=		{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}



