




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

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





							'''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 						snake_case_       (UpperCamelCase			:    Tuple					,		UpperCamelCase			:    Optional[int]					,		UpperCamelCase			:    Tuple ):





							'''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 						snake_case_       (UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    str					,		UpperCamelCase			:    Dict					,		UpperCamelCase			:    Dict					,		UpperCamelCase			:    int ):





							'''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 						snake_case_       (UpperCamelCase			:    Tuple					,		UpperCamelCase			:    List[str]					,		UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Optional[Any]=None					,		UpperCamelCase			:    Any=None					,		UpperCamelCase			:    Any=None					,		):





							'''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      	=							1876
														_a      	=							600
														_a      	=							config.max_speech_positions
														_a      	=							SpeechTaForTextToSpeech(UpperCamelCase )
							elif task == "s2s":
														_a      	=							1876
														_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__":
				_snake_case : 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.'
				)

				_snake_case : int  				=						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(       _SCREAMING_SNAKE_CASE				)							-> bool:




    """simple docstring"""




    if not isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        raise ValueError('check_bouncy() accepts only integer arguments'				)
    _A     =							str(_SCREAMING_SNAKE_CASE				)
    _A     =							''.join(sorted(_SCREAMING_SNAKE_CASE				)				)
    return sorted_str_n != str_n and sorted_str_n[::-1] != str_n





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 99				)							-> int:




    """simple docstring"""




    if not 0 < percent < 100:
        raise ValueError('solution() only accepts values from 0 to 100'				)
    _A     =							0
    _A     =							1

    while True:
        if check_bouncy(_SCREAMING_SNAKE_CASE				):
            bouncy_num += 1
        if (bouncy_num / num) * 100 >= percent:
            return num
        num += 1


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

 testmod()
 print(f"{solution(99)}")





