


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()
_lowercase       :      Any	=	logging.get_logger("transformers.models.speecht5")

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





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     int						) ->   Optional[Any]:





	"""simple docstring"""







	for attribute in key.split("""."""						):
		A								=				getattr(UpperCamelCase__							,					UpperCamelCase__						)

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

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





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]						) ->   List[Any]:





	"""simple docstring"""







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





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





	"""simple docstring"""







	A								=				[]

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

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

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





	"""simple docstring"""







	A								=				full_name.split("""conv_layers."""						)[-1]
	A								=				name.split("""."""						)
	A								=				int(items[0]						)
	A								=				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.'						)
			A								=				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.'						)
			A								=				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.'						)
			A								=				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.'						)
			A								=				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__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     List[str]							,					UpperCamelCase__:     int=None							,					UpperCamelCase__:     List[Any]=None							,					UpperCamelCase__:     Union[str, Any]=None							,					) ->   Tuple:





	"""simple docstring"""







	if config_path is not None:
		A								=				SpeechTaConfig.from_pretrained(UpperCamelCase__						)
	else:
		A								=				SpeechTaConfig()

	if task == "s2t":
		A								=				config.max_text_positions
		A								=				SpeechTaForSpeechToText(UpperCamelCase__						)
	elif task == "t2s":
		A								=				18_76
		A								=				6_00
		A								=				config.max_speech_positions
		A								=				SpeechTaForTextToSpeech(UpperCamelCase__						)
	elif task == "s2s":
		A								=				18_76
		A								=				config.max_speech_positions
		A								=				SpeechTaForSpeechToSpeech(UpperCamelCase__						)
	else:
		raise ValueError(f'Unknown task name: {task}'						)

	if vocab_path:
		A								=				SpeechTaTokenizer(UpperCamelCase__							,					model_max_length=config.max_text_positions						)

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

	A								=				SpeechTaFeatureExtractor()
	A								=				SpeechTaProcessor(tokenizer=UpperCamelCase__							,					feature_extractor=UpperCamelCase__						)
	processor.save_pretrained(UpperCamelCase__						)

	A								=				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__":
				_lowercase       :      List[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."
				)

				_lowercase       :      Union[str, 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,
				)







import math





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5							,					int(math.sqrt(UpperCamelCase__						) + 1						)							,					6						):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def   _lowerCAmelCase				(   UpperCamelCase__:     float = 0.1						) ->   int:





	"""simple docstring"""







	A								=				3
	A								=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1							,					(j + 2) * (j + 2)							,					j + 1						):
			primes += is_prime(UpperCamelCase__						)
		j += 2
	return j


if __name__ == "__main__":
				import doctest

				doctest.testmod()



