





'''simple docstring'''




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def        _lowerCAmelCase			(  __snake_case  : Tuple ,							__snake_case  : str ,							__snake_case  : Union[str, Any]			)     ->     str:
     __A						: Any      =       AutoConfig.from_pretrained(__snake_case			)
     __A						: Optional[Any]      =       FlaxAutoModelForSeqaSeqLM.from_config(config=__snake_case			)
     __A						: Any      =       checkpoints.load_tax_checkpoint(__snake_case			)

     __A						: Dict      =       'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

     if config.model_type == "t5":
          __A						: List[str]      =       'SelfAttention'
     if config.model_type == "longt5" and config.encoder_attention_type == "local":
          __A						: List[str]      =       'LocalSelfAttention'
     elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
          __A						: int      =       'TransientGlobalSelfAttention'
     else:
          raise ValueError(
              'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
              ' attribute with a value from [\'local\', \'transient-global].'			)

     # Encoder
     for layer_index in range(config.num_layers			):
          __A						: Tuple      =       f'layers_{str(__snake_case			)}'

          # Self-Attention
          __A						: Any      =       tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
          __A						: Tuple      =       tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
          __A						: Optional[int]      =       tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
          __A						: Dict      =       tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

          # Global input layer norm
          if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
               __A						: Tuple      =       tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

          # Layer Normalization
          __A						: Optional[int]      =       tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

          if split_mlp_wi:
               __A						: Dict      =       tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
               __A						: int      =       tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
          else:
               __A						: Tuple      =       tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

          __A						: List[str]      =       tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

          # Layer Normalization
          __A						: str      =       tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

          # Assigning
          __A						: str      =       flax_model.params['encoder']['block'][str(__snake_case			)]['layer']
          __A						: List[str]      =       tax_attention_key
          __A						: int      =       tax_attention_out
          __A						: List[Any]      =       tax_attention_query
          __A						: List[str]      =       tax_attention_value

          __A						: List[str]      =       tax_attention_layer_norm

          # Global input layer norm
          if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
               __A						: Dict      =       tax_global_layer_norm

          if split_mlp_wi:
               __A						: Optional[int]      =       tax_mlp_wi_a
               __A						: Tuple      =       tax_mlp_wi_a
          else:
               __A						: Optional[Any]      =       tax_mlp_wi

          __A						: int      =       tax_mlp_wo
          __A						: Tuple      =       tax_mlp_layer_norm

          __A						: int      =       flax_model_encoder_layer_block

     # Only for layer 0:
     __A						: List[str]      =       tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
     __A						: Tuple      =       tax_encoder_rel_embedding

     # Side/global relative position_bias + layer norm
     if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
          __A						: Optional[Any]      =       tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
          __A						: Optional[int]      =       tax_encoder_global_rel_embedding

     # Assigning
     __A						: List[Any]      =       tax_model['target']['encoder']['encoder_norm']['scale']
     __A						: List[Any]      =       tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_layers			):
          __A						: Any      =       f'layers_{str(__snake_case			)}'

          # Self-Attention
          __A						: Optional[int]      =       tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
          __A						: List[str]      =       tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
          __A						: Optional[Any]      =       tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
          __A						: Optional[Any]      =       tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

          # Layer Normalization
          __A						: str      =       tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
              'scale'
          ]

          # Encoder-Decoder-Attention
          __A						: Dict      =       tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
          __A						: List[Any]      =       tax_enc_dec_attention_module['key']['kernel']
          __A						: List[str]      =       tax_enc_dec_attention_module['out']['kernel']
          __A						: Dict      =       tax_enc_dec_attention_module['query']['kernel']
          __A						: Any      =       tax_enc_dec_attention_module['value']['kernel']

          # Layer Normalization
          __A						: List[Any]      =       tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

          # MLP
          if split_mlp_wi:
               __A						: Optional[int]      =       tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
               __A						: Tuple      =       tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
          else:
               __A						: Optional[int]      =       tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

          __A						: int      =       tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

          # Layer Normalization
          __A						: Dict      =       tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

          # Assigning
          __A						: Dict      =       flax_model.params['decoder']['block'][str(__snake_case			)]['layer']
          __A						: Union[str, Any]      =       tax_attention_key
          __A						: int      =       tax_attention_out
          __A						: List[Any]      =       tax_attention_query
          __A						: Dict      =       tax_attention_value

          __A						: Union[str, Any]      =       tax_pre_attention_layer_norm

          __A						: Any      =       tax_enc_dec_attention_key
          __A						: Dict      =       tax_enc_dec_attention_out
          __A						: Optional[Any]      =       tax_enc_dec_attention_query
          __A						: Optional[int]      =       tax_enc_dec_attention_value

          __A						: List[str]      =       tax_cross_layer_norm

          if split_mlp_wi:
               __A						: List[Any]      =       tax_mlp_wi_a
               __A						: Dict      =       tax_mlp_wi_a
          else:
               __A						: Optional[int]      =       tax_mlp_wi

          __A						: Optional[int]      =       tax_mlp_wo

          __A						: int      =       txa_mlp_layer_norm

          __A						: Optional[int]      =       flax_model_decoder_layer_block

     # Decoder Normalization
     __A						: List[str]      =       tax_model['target']['decoder']['decoder_norm']['scale']
     __A						: Union[str, Any]      =       txa_decoder_norm

     # Only for layer 0:
     __A						: Union[str, Any]      =       tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
     __A						: int      =       tax_decoder_rel_embedding

     # Token Embeddings
     __A						: str      =       tax_model['target']['token_embedder']['embedding']
     __A						: str      =       txa_token_embeddings

     # LM Head (only in v1.1 and LongT5 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          __A						: Any      =       tax_model['target']['decoder']['logits_dense']['kernel']

     flax_model.save_pretrained(__snake_case			)
     print('T5X Model was sucessfully converted!'			)


if __name__ == "__main__":
  lowercase__    :      Tuple						=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
  )
  parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
  parser.add_argument(
      '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
  )
  lowercase__    :      int						=    parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






'''simple docstring'''




import math






def        _lowerCAmelCase			(  __snake_case  : 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(__snake_case			) + 1			) ,							6			):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True






def        _lowerCAmelCase			(  __snake_case  : float = 0.1			)     ->     int:
     __A						: Tuple      =       3
     __A						: Optional[int]      =       3

     while primes / (2 * j - 1) >= ratio:
          for i in range(j * j + j + 1 ,							(j + 2) * (j + 2) ,							j + 1			):
               primes += is_prime(__snake_case			)
          j += 2
     return j


if __name__ == "__main__":
  import doctest

  doctest.testmod()