





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()
a__:      Any									=		logging.get_logger('transformers.models.speecht5')

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


def 	UpperCamelCase__(       UpperCamelCase__			:       Union[str, Any] ,     UpperCamelCase__			:       Optional[Any] ,     UpperCamelCase__			:       List[str] ,     UpperCamelCase__			:       Optional[Any] ,     UpperCamelCase__			:       List[Any]  )->Any:
       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 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int] ,     UpperCamelCase__			:       Union[str, Any]  )->List[Any]:
       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 	UpperCamelCase__(       UpperCamelCase__			:       List[Any] ,     UpperCamelCase__			:       str ,     UpperCamelCase__			:       Any  )->Any:
       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 	UpperCamelCase__(       UpperCamelCase__			:       Union[str, Any] ,     UpperCamelCase__			:       Optional[Any] ,     UpperCamelCase__			:       Tuple ,     UpperCamelCase__			:       str ,     UpperCamelCase__			:       Any  )->int:
       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 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int] ,     UpperCamelCase__			:       Union[str, Any] ,     UpperCamelCase__			:       Dict ,     UpperCamelCase__			:       int=None ,     UpperCamelCase__			:       Optional[int]=None ,     UpperCamelCase__			:       Any=None ,     )->Tuple:
       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__":
       a__:      Dict									=		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.'
       )

       a__:      Optional[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,
       )












from __future__ import annotations

import math


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->bool:

       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 	UpperCamelCase__(       UpperCamelCase__			:       int  )->list[int]:
       A__											=						str(UpperCamelCase__  )
       A__											=						[n]
       for i in range(1 ,     len(UpperCamelCase__  )  ):
              list_nums.append(int(str_num[i:]  )  )
              list_nums.append(int(str_num[:-i]  )  )
       return list_nums


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


def 	UpperCamelCase__(       UpperCamelCase__			:       int = 11  )->list[int]:
       A__											=						[]
       A__											=						13
       while len(UpperCamelCase__  ) != count:
              if validate(UpperCamelCase__  ):
                     A__											=						list_truncated_nums(UpperCamelCase__  )
                     if all(is_prime(UpperCamelCase__  ) for i in list_nums  ):
                            list_truncated_primes.append(UpperCamelCase__  )
              num += 2
       return list_truncated_primes


def 	UpperCamelCase__(       )->int:
       return sum(compute_truncated_primes(11  )  )


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





