





'''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()
a__							:							Union[str, Any] =logging.get_logger('''transformers.models.speecht5''')

a__							:							Optional[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__							:							List[str] ={
    "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__							:							Any ={
    "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__							:							Any ={
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
a__							:							int ={
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
a__							:							int ={
    "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__							:							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",
}
a__							:							Tuple ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a__							:							Optional[Any] ={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a__							:							Optional[Any] ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a__							:							List[str] =[]
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__							:							Optional[int] =IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
a__							:							int =IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
a__							:							Tuple =IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
def 				lowercase__   (   __lowercase						:  Any				,  __lowercase						:  Dict				,  __lowercase						:  List[str]				,  __lowercase						:  Any				,  __lowercase						:  int     ) ->      Dict:



       """simple docstring"""






       for attribute in key.split('.'     ):
              __UpperCamelCase       =  getattr(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE     )

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

       logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''     )
def 				lowercase__   (   __lowercase						:  Any				,  __lowercase						:  str     ) ->      Dict:



       """simple docstring"""






       for key in ignore_keys:
              if key.endswith('.*'     ):
                     if name.startswith(key[:-1]     ):
                            return True
              elif ".*." in key:
                     __UpperCamelCase       =  key.split('.*.'     )
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Dict				,  __lowercase						:  str     ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  []

       if task == "s2t":
              __UpperCamelCase       =  hf_model.speechta.encoder.prenet.feature_encoder
              __UpperCamelCase       =  MAPPING_S2T
              __UpperCamelCase       =  IGNORE_KEYS_S2T
       elif task == "t2s":
              __UpperCamelCase       =  None
              __UpperCamelCase       =  MAPPING_T2S
              __UpperCamelCase       =  IGNORE_KEYS_T2S
       elif task == "s2s":
              __UpperCamelCase       =  hf_model.speechta.encoder.prenet.feature_encoder
              __UpperCamelCase       =  MAPPING_S2S
              __UpperCamelCase       =  IGNORE_KEYS_S2S
       else:
              raise ValueError(F'''Unsupported task: {task}'''     )

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

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

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

       logger.warning(F'''Unused weights: {unused_weights}'''     )
def 				lowercase__   (   __lowercase						:  Optional[Any]				,  __lowercase						:  List[Any]				,  __lowercase						:  List[str]				,  __lowercase						:  int				,  __lowercase						:  Dict     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  full_name.split('conv_layers.'     )[-1]
       __UpperCamelCase       =  name.split('.'     )
       __UpperCamelCase       =  int(items[0]     )
       __UpperCamelCase       =  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       =  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       =  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       =  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       =  value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
       else:
              unused_weights.append(__SCREAMING_SNAKE_CASE     )
@torch.no_grad()
def 				lowercase__   (   __lowercase						:  Dict				,  __lowercase						:  Any				,  __lowercase						:  Optional[int]				,  __lowercase						:  List[str]=None				,  __lowercase						:  Optional[int]=None				,  __lowercase						:  Tuple=None				,  ) ->      List[Any]:



       """simple docstring"""






       if config_path is not None:
              __UpperCamelCase       =  SpeechTaConfig.from_pretrained(__SCREAMING_SNAKE_CASE     )
       else:
              __UpperCamelCase       =  SpeechTaConfig()

       if task == "s2t":
              __UpperCamelCase       =  config.max_text_positions
              __UpperCamelCase       =  SpeechTaForSpeechToText(__SCREAMING_SNAKE_CASE     )
       elif task == "t2s":
              __UpperCamelCase       =  1876
              __UpperCamelCase       =  600
              __UpperCamelCase       =  config.max_speech_positions
              __UpperCamelCase       =  SpeechTaForTextToSpeech(__SCREAMING_SNAKE_CASE     )
       elif task == "s2s":
              __UpperCamelCase       =  1876
              __UpperCamelCase       =  config.max_speech_positions
              __UpperCamelCase       =  SpeechTaForSpeechToSpeech(__SCREAMING_SNAKE_CASE     )
       else:
              raise ValueError(F'''Unknown task name: {task}'''     )

       if vocab_path:
              __UpperCamelCase       =  SpeechTaTokenizer(__SCREAMING_SNAKE_CASE				,  model_max_length=config.max_text_positions     )

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

       __UpperCamelCase       =  SpeechTaFeatureExtractor()
       __UpperCamelCase       =  SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,  feature_extractor=__SCREAMING_SNAKE_CASE     )
       processor.save_pretrained(__SCREAMING_SNAKE_CASE     )

       __UpperCamelCase       =  torch.load(__SCREAMING_SNAKE_CASE     )
       recursively_load_weights(fairseq_checkpoint['model']				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE     )

       model.save_pretrained(__SCREAMING_SNAKE_CASE     )

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


if __name__ == "__main__":
    a__							:							List[str] =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__							:							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,
    )


'''simple docstring'''







from math import isqrt, loga
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Any													=       [True] * max_number
						for i in range(2      ,      isqrt(max_number - 1							) + 1							):
												if is_prime[i]:
																		for j in range(i**2      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
																								lowercase_	:     Optional[Any]													=       False

						return [i for i in range(2      ,      __SCREAMING_SNAKE_CASE							) if is_prime[i]]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int = 800800      ,      __SCREAMING_SNAKE_CASE					: int = 800800							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       degree * loga(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Any													=       int(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Optional[Any]													=       calculate_prime_numbers(__SCREAMING_SNAKE_CASE							)

						lowercase_	:     Optional[Any]													=       0
						lowercase_	:     List[Any]													=       0
						lowercase_	:     Union[str, Any]													=       len(__SCREAMING_SNAKE_CASE							) - 1
						while left < right:
												while (
												    prime_numbers[right] * loga(prime_numbers[left]							)
												    + prime_numbers[left] * loga(prime_numbers[right]							)
												    > upper_bound
												):
																		right -= 1
												hybrid_integers_count += right - left
												left += 1

						return hybrid_integers_count


if __name__ == "__main__":
							print(f"""{solution() = }""")



