


'''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()
__snake_case											=					logging.get_logger('''transformers.models.speecht5''')

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


def       a				(      __a			, __a			, __a			, __a			, __a       )  ->     List[str]:



					'''simple docstring'''






					for attribute in key.split('''.'''       ):
										UpperCamelCase__       :Dict							 =			getattr(__a			, __a       )

					if weight_type is not None:
										UpperCamelCase__       :List[str]							 =			getattr(__a			, __a       ).shape
					else:
										UpperCamelCase__       :str							 =			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":
										UpperCamelCase__       :List[str]							 =			value
					elif weight_type == "weight_g":
										UpperCamelCase__       :List[Any]							 =			value
					elif weight_type == "weight_v":
										UpperCamelCase__       :Any							 =			value
					elif weight_type == "bias":
										UpperCamelCase__       :Dict							 =			value
					elif weight_type == "running_mean":
										UpperCamelCase__       :Union[str, Any]							 =			value
					elif weight_type == "running_var":
										UpperCamelCase__       :List[str]							 =			value
					elif weight_type == "num_batches_tracked":
										UpperCamelCase__       :Optional[Any]							 =			value
					else:
										UpperCamelCase__       :Dict							 =			value

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


def       a				(      __a			, __a       )  ->     Tuple:



					'''simple docstring'''






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


def       a				(      __a			, __a			, __a       )  ->     str:



					'''simple docstring'''






					UpperCamelCase__       :Dict							 =			[]

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

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

										UpperCamelCase__       :int							 =			False
										if "conv_layers" in name:
															load_conv_layer(
															    __a			, __a			, __a			, __a			, hf_model.config.feat_extract_norm == '''group'''			, )
															UpperCamelCase__       :Tuple							 =			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:
																									UpperCamelCase__ ,    UpperCamelCase__       :Dict							 =			key.split('''.*.'''       )
																									if prefix in name and suffix in name:
																														UpperCamelCase__       :List[Any]							 =			suffix

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

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


def       a				(      __a			, __a			, __a			, __a			, __a       )  ->     Dict:



					'''simple docstring'''






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


@torch.no_grad()
def       a				(      __a			, __a			, __a			, __a=None			, __a=None			, __a=None			, )  ->     Any:



					'''simple docstring'''






					if config_path is not None:
										UpperCamelCase__       :Optional[int]							 =			SpeechTaConfig.from_pretrained(__a       )
					else:
										UpperCamelCase__       :Optional[int]							 =			SpeechTaConfig()

					if task == "s2t":
										UpperCamelCase__       :List[str]							 =			config.max_text_positions
										UpperCamelCase__       :int							 =			SpeechTaForSpeechToText(__a       )
					elif task == "t2s":
										UpperCamelCase__       :Union[str, Any]							 =			1876
										UpperCamelCase__       :Dict							 =			600
										UpperCamelCase__       :Dict							 =			config.max_speech_positions
										UpperCamelCase__       :List[Any]							 =			SpeechTaForTextToSpeech(__a       )
					elif task == "s2s":
										UpperCamelCase__       :int							 =			1876
										UpperCamelCase__       :int							 =			config.max_speech_positions
										UpperCamelCase__       :Tuple							 =			SpeechTaForSpeechToSpeech(__a       )
					else:
										raise ValueError(f'''Unknown task name: {task}'''       )

					if vocab_path:
										UpperCamelCase__       :Optional[int]							 =			SpeechTaTokenizer(__a			, model_max_length=config.max_text_positions       )

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

					UpperCamelCase__       :List[Any]							 =			SpeechTaFeatureExtractor()
					UpperCamelCase__       :Any							 =			SpeechTaProcessor(tokenizer=__a			, feature_extractor=__a       )
					processor.save_pretrained(__a       )

					UpperCamelCase__       :List[Any]							 =			torch.load(__a       )
					recursively_load_weights(fairseq_checkpoint['''model''']			, __a			, __a       )

					model.save_pretrained(__a       )

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


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

		__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'''
from __future__ import annotations

import math


def       a				(      __a       )  ->     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(__a       ) + 1       )			, 6       ):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True


def       a				(      __a       )  ->     list[int]:



					'''simple docstring'''






					UpperCamelCase__       :List[Any]							 =			str(__a       )
					UpperCamelCase__       :Dict							 =			[n]
					for i in range(1			, len(__a       )       ):
										list_nums.append(int(str_num[i:]       )       )
										list_nums.append(int(str_num[:-i]       )       )
					return list_nums


def       a				(      __a       )  ->     bool:



					'''simple docstring'''






					if len(str(__a       )       ) > 3:
										if not is_prime(int(str(__a       )[-3:]       )       ) or not is_prime(int(str(__a       )[:3]       )       ):
															return False
					return True


def       a				(      __a = 11       )  ->     list[int]:



					'''simple docstring'''






					UpperCamelCase__       :list[int]							 =			[]
					UpperCamelCase__       :int							 =			13
					while len(__a       ) != count:
										if validate(__a       ):
															UpperCamelCase__       :Optional[int]							 =			list_truncated_nums(__a       )
															if all(is_prime(__a       ) for i in list_nums       ):
																				list_truncated_primes.append(__a       )
										num += 2
					return list_truncated_primes


def       a				(      )  ->     int:



					'''simple docstring'''






					return sum(compute_truncated_primes(11       )       )


if __name__ == "__main__":
		print(F"""{sum(compute_truncated_primes(11)) = }""")