


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

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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						str:


						'''simple docstring'''






						for attribute in key.split("."			):
												__snake_case				=		getattr(_lowerCAmelCase   ,						_lowerCAmelCase			)

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

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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Dict:


						'''simple docstring'''






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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Optional[Any]:


						'''simple docstring'''






						__snake_case				=		[]

						if task == "s2t":
												__snake_case				=		hf_model.speechta.encoder.prenet.feature_encoder
												__snake_case				=		MAPPING_S2T
												__snake_case				=		IGNORE_KEYS_S2T
						elif task == "t2s":
												__snake_case				=		None
												__snake_case				=		MAPPING_T2S
												__snake_case				=		IGNORE_KEYS_T2S
						elif task == "s2s":
												__snake_case				=		hf_model.speechta.encoder.prenet.feature_encoder
												__snake_case				=		MAPPING_S2S
												__snake_case				=		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				=		False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						hf_model.config.feat_extract_norm == "group"   ,						)
																		__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:
																														__snake_case		,       __snake_case				=		key.split(".*."			)
																														if prefix in name and suffix in name:
																																				__snake_case				=		suffix

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


						'''simple docstring'''






						__snake_case				=		full_name.split("conv_layers."			)[-1]
						__snake_case				=		name.split("."			)
						__snake_case				=		int(items[0]			)
						__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.'''			)
																		__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.'''			)
																		__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.'''			)
																		__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.'''			)
																		__snake_case				=		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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase=None   ,						_lowerCAmelCase=None   ,						_lowerCAmelCase=None   ,						)    ->						List[str]:


						'''simple docstring'''






						if config_path is not None:
												__snake_case				=		SpeechTaConfig.from_pretrained(_lowerCAmelCase			)
						else:
												__snake_case				=		SpeechTaConfig()

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

						if vocab_path:
												__snake_case				=		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				=		AddedToken("<mask>"   ,						lstrip=_lowerCAmelCase   ,						rstrip=_lowerCAmelCase			)
												__snake_case				=		mask_token
												tokenizer.add_special_tokens({"mask_token": mask_token}			)
												tokenizer.add_tokens(["<ctc_blank>"]			)

						__snake_case				=		SpeechTaFeatureExtractor()
						__snake_case				=		SpeechTaProcessor(tokenizer=_lowerCAmelCase   ,						feature_extractor=_lowerCAmelCase			)
						processor.save_pretrained(_lowerCAmelCase			)

						__snake_case				=		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__":
	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				:       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,
	)









def 						_lowerCAmelCase							(     _lowerCAmelCase = 100			)    ->						int:


						'''simple docstring'''






						__snake_case				=		n * (n + 1) * (2 * n + 1) / 6
						__snake_case				=		(n * (n + 1) / 2) ** 2
						return int(square_of_sum - sum_of_squares			)


if __name__ == "__main__":
	print(f'''{solution() = }''')





