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:	Optional[int]       			=		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:	Optional[Any]       			=		{
    '''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:	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''',
}
_lowercase:	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''',
}
_lowercase:	Union[str, Any]       			=		{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
_lowercase:	List[str]       			=		{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
_lowercase:	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''',
}
_lowercase:	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:	Tuple       			=		{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_lowercase:	int       			=		{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowercase:	str       			=		{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowercase:	Tuple       			=		[]
_lowercase:	Optional[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''',
]
_lowercase:	List[Any]       			=		IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
_lowercase:	Optional[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  (		snake_case     , snake_case     , snake_case     , snake_case     , snake_case  ):
							for attribute in key.split('.'  ):
														_lowerCAmelCase							=							getattr(snake_case     , snake_case  )

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

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






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






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case  ):
							_lowerCAmelCase							=							[]

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

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																												if key in name:
																																			_lowerCAmelCase							=							True
																																			if "*" in mapped_key:
																																										_lowerCAmelCase							=							name.split(snake_case  )[0].split('.'  )[-2]
																																										_lowerCAmelCase							=							mapped_key.replace('*'     , snake_case  )
																																			if "weight_g" in name:
																																										_lowerCAmelCase							=							'weight_g'
																																			elif "weight_v" in name:
																																										_lowerCAmelCase							=							'weight_v'
																																			elif "bias" in name:
																																										_lowerCAmelCase							=							'bias'
																																			elif "weight" in name:
																																										_lowerCAmelCase							=							'weight'
																																			elif "running_mean" in name:
																																										_lowerCAmelCase							=							'running_mean'
																																			elif "running_var" in name:
																																										_lowerCAmelCase							=							'running_var'
																																			elif "num_batches_tracked" in name:
																																										_lowerCAmelCase							=							'num_batches_tracked'
																																			else:
																																										_lowerCAmelCase							=							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 _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case     , snake_case  ):
							_lowerCAmelCase							=							full_name.split('conv_layers.'  )[-1]
							_lowerCAmelCase							=							name.split('.'  )
							_lowerCAmelCase							=							int(items[0]  )
							_lowerCAmelCase							=							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.'  )
																					_lowerCAmelCase							=							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.'  )
																					_lowerCAmelCase							=							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.'  )
																					_lowerCAmelCase							=							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.'  )
																					_lowerCAmelCase							=							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 _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case=None     , snake_case=None     , snake_case=None     , ):
							if config_path is not None:
														_lowerCAmelCase							=							SpeechTaConfig.from_pretrained(snake_case  )
							else:
														_lowerCAmelCase							=							SpeechTaConfig()

							if task == "s2t":
														_lowerCAmelCase							=							config.max_text_positions
														_lowerCAmelCase							=							SpeechTaForSpeechToText(snake_case  )
							elif task == "t2s":
														_lowerCAmelCase							=							1_876
														_lowerCAmelCase							=							600
														_lowerCAmelCase							=							config.max_speech_positions
														_lowerCAmelCase							=							SpeechTaForTextToSpeech(snake_case  )
							elif task == "s2s":
														_lowerCAmelCase							=							1_876
														_lowerCAmelCase							=							config.max_speech_positions
														_lowerCAmelCase							=							SpeechTaForSpeechToSpeech(snake_case  )
							else:
														raise ValueError(F'Unknown task name: {task}'  )

							if vocab_path:
														_lowerCAmelCase							=							SpeechTaTokenizer(snake_case     , model_max_length=config.max_text_positions  )

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

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

							_lowerCAmelCase							=							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__":
				_lowercase:	Optional[int]       			=		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:	Optional[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  (		snake_case = 10  ):
							if not isinstance(snake_case     , snake_case  ) or n < 0:
														raise ValueError('Invalid input'  )
							_lowerCAmelCase							=							10**n
							_lowerCAmelCase							=							28_433 * (pow(2     , 7_830_457     , snake_case  )) + 1
							return str(number % modulus  )


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				print(f"""{solution(1_0) = }""")






