"""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()
SCREAMING_SNAKE_CASE									=						logging.get_logger('transformers.models.speecht5')

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	for attribute in key.split('.'			):
		_lowercase :    Union[str, Any]							=							getattr(__UpperCAmelCase     ,__UpperCAmelCase			)

	if weight_type is not None:
		_lowercase :    Any							=							getattr(__UpperCAmelCase     ,__UpperCAmelCase			).shape
	else:
		_lowercase :    Dict							=							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 :    Optional[Any]							=							value
	elif weight_type == "weight_g":
		_lowercase :    Dict							=							value
	elif weight_type == "weight_v":
		_lowercase :    str							=							value
	elif weight_type == "bias":
		_lowercase :    Optional[Any]							=							value
	elif weight_type == "running_mean":
		_lowercase :    str							=							value
	elif weight_type == "running_var":
		_lowercase :    int							=							value
	elif weight_type == "num_batches_tracked":
		_lowercase :    Optional[Any]							=							value
	else:
		_lowercase :    Tuple							=							value

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[]

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

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

		_lowercase :    Optional[int]							=							False
		if "conv_layers" in name:
			load_conv_layer(
			    __UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,hf_model.config.feat_extract_norm == 'group'     ,)
			_lowercase :    Optional[int]							=							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 :    int							=							key.split('.*.'			)
					if prefix in name and suffix in name:
						_lowercase :    Tuple							=							suffix

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

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Dict							=							full_name.split('conv_layers.'			)[-1]
	_lowercase :    Any							=							name.split('.'			)
	_lowercase :    Optional[Any]							=							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 :    Dict							=							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 :    int							=							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 :    Optional[Any]							=							value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
	else:
		unused_weights.append(__UpperCAmelCase			)


@torch.no_grad()
def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None     ,):






	"""simple docstring"""





	if config_path is not None:
		_lowercase :    Optional[Any]							=							SpeechTaConfig.from_pretrained(__UpperCAmelCase			)
	else:
		_lowercase :    Optional[int]							=							SpeechTaConfig()

	if task == "s2t":
		_lowercase :    Union[str, Any]							=							config.max_text_positions
		_lowercase :    Optional[Any]							=							SpeechTaForSpeechToText(__UpperCAmelCase			)
	elif task == "t2s":
		_lowercase :    Union[str, Any]							=							1_876
		_lowercase :    Optional[Any]							=							600
		_lowercase :    str							=							config.max_speech_positions
		_lowercase :    Union[str, Any]							=							SpeechTaForTextToSpeech(__UpperCAmelCase			)
	elif task == "s2s":
		_lowercase :    Union[str, Any]							=							1_876
		_lowercase :    Union[str, Any]							=							config.max_speech_positions
		_lowercase :    Any							=							SpeechTaForSpeechToSpeech(__UpperCAmelCase			)
	else:
		raise ValueError(F'''Unknown task name: {task}'''			)

	if vocab_path:
		_lowercase :    List[str]							=							SpeechTaTokenizer(__UpperCAmelCase     ,model_max_length=config.max_text_positions			)

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

	_lowercase :    Dict							=							SpeechTaFeatureExtractor()
	_lowercase :    List[Any]							=							SpeechTaProcessor(tokenizer=__UpperCAmelCase     ,feature_extractor=__UpperCAmelCase			)
	processor.save_pretrained(__UpperCAmelCase			)

	_lowercase :    Union[str, Any]							=							torch.load(__UpperCAmelCase			)
	recursively_load_weights(fairseq_checkpoint['model']     ,__UpperCAmelCase     ,__UpperCAmelCase			)

	model.save_pretrained(__UpperCAmelCase			)

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


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

						SCREAMING_SNAKE_CASE									=						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"""







def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    str							=							[0 for i in range(len(__UpperCAmelCase			)			)]

	# initialize interval's left pointer and right pointer
	_lowercase				,      _lowercase :    str							=							0, 0

	for i in range(1     ,len(__UpperCAmelCase			)			):
		# case when current index is inside the interval
		if i <= right_pointer:
			_lowercase :    Union[str, Any]							=							min(right_pointer - i + 1     ,z_result[i - left_pointer]			)
			_lowercase :    Any							=							min_edge

		while go_next(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):
			z_result[i] += 1

		# if new index's result gives us more right interval,
		# we've to update left_pointer and right_pointer
		if i + z_result[i] - 1 > right_pointer:
			_lowercase				,      _lowercase :    Optional[int]							=							i, i + z_result[i] - 1

	return z_result


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	return i + z_result[i] < len(__UpperCAmelCase			) and s[z_result[i]] == s[i + z_result[i]]


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							0
	# concatenate 'pattern' and 'input_str' and call z_function
	# with concatenated string
	_lowercase :    List[str]							=							z_function(pattern + input_str			)

	for val in z_result:
		# if value is greater then length of the pattern string
		# that means this index is starting position of substring
		# which is equal to pattern string
		if val >= len(__UpperCAmelCase			):
			answer += 1

	return answer


if __name__ == "__main__":
						import doctest

						doctest.testmod()



