

'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def       _A	(			snake_case	,							snake_case	,							snake_case  )    ->			List[Any]:
   _lowercase    :   str      =     TaConfig.from_pretrained(snake_case  )
   _lowercase    :   List[str]      =     FlaxTaForConditionalGeneration(config=snake_case  )
   _lowercase    :   str      =     checkpoints.load_tax_checkpoint(snake_case  )

   _lowercase    :   str      =     "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

   # Encoder
   for layer_index in range(config.num_layers  ):
      _lowercase    :   Union[str, Any]      =     F'''layers_{str(snake_case  )}'''

      # Self-Attention
      _lowercase    :   List[Any]      =     tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
      _lowercase    :   str      =     tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
      _lowercase    :   Tuple      =     tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
      _lowercase    :   Optional[Any]      =     tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

      # Layer Normalization
      _lowercase    :   List[str]      =     tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

      if split_mlp_wi:
         _lowercase    :   Optional[Any]      =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         _lowercase    :   int      =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         _lowercase    :   List[Any]      =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

      _lowercase    :   Union[str, Any]      =     tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      _lowercase    :   int      =     tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      _lowercase    :   List[str]      =     tax_attention_key
      _lowercase    :   str      =     tax_attention_out
      _lowercase    :   Tuple      =     tax_attention_query
      _lowercase    :   Union[str, Any]      =     tax_attention_value

      _lowercase    :   Any      =     tax_attention_layer_norm

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

      _lowercase    :   str      =     tax_mlp_wo
      _lowercase    :   Any      =     tax_mlp_layer_norm

   # Only for layer 0:
   _lowercase    :   Optional[int]      =     tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
   _lowercase    :   Tuple      =     tax_encoder_rel_embedding

   # Assigning
   _lowercase    :   Optional[int]      =     tax_model["target"]["encoder"]["encoder_norm"]["scale"]
   _lowercase    :   List[str]      =     tax_encoder_norm

   # Decoder
   for layer_index in range(config.num_decoder_layers  ):
      _lowercase    :   str      =     F'''layers_{str(snake_case  )}'''

      # Self-Attention
      _lowercase    :   Optional[int]      =     tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
      _lowercase    :   Union[str, Any]      =     tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
      _lowercase    :   List[Any]      =     tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
      _lowercase    :   Union[str, Any]      =     tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

      # Layer Normalization
      _lowercase    :   List[str]      =     tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
          "scale"
      ]

      # Encoder-Decoder-Attention
      _lowercase    :   Tuple      =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
          "kernel"
      ]
      _lowercase    :   Tuple      =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
          "kernel"
      ]
      _lowercase    :   List[Any]      =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
          "kernel"
      ]
      _lowercase    :   List[str]      =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
          "kernel"
      ]

      # Layer Normalization
      _lowercase    :   Optional[Any]      =     tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

      # MLP
      if split_mlp_wi:
         _lowercase    :   str      =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         _lowercase    :   List[Any]      =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         _lowercase    :   Optional[Any]      =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

      _lowercase    :   int      =     tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      _lowercase    :   Optional[Any]      =     tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      _lowercase    :   List[Any]      =     tax_attention_key
      _lowercase    :   int      =     tax_attention_out
      _lowercase    :   Tuple      =     tax_attention_query
      _lowercase    :   Tuple      =     tax_attention_value

      _lowercase    :   Tuple      =     tax_pre_attention_layer_norm

      _lowercase    :   Optional[int]      =     tax_enc_dec_attention_key
      _lowercase    :   Tuple      =     tax_enc_dec_attention_out
      _lowercase    :   str      =     tax_enc_dec_attention_query
      _lowercase    :   List[str]      =     tax_enc_dec_attention_value

      _lowercase    :   str      =     tax_cross_layer_norm

      if split_mlp_wi:
         _lowercase    :   List[str]      =     tax_mlp_wi_a
         _lowercase    :   int      =     tax_mlp_wi_a
      else:
         _lowercase    :   Optional[int]      =     tax_mlp_wi

      _lowercase    :   Optional[int]      =     tax_mlp_wo

      _lowercase    :   Optional[int]      =     txa_mlp_layer_norm

   # Decoder Normalization
   _lowercase    :   Any      =     tax_model["target"]["decoder"]["decoder_norm"]["scale"]
   _lowercase    :   List[str]      =     txa_decoder_norm

   # Only for layer 0:
   _lowercase    :   str      =     tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
   _lowercase    :   Optional[Any]      =     tax_decoder_rel_embedding

   # Token Embeddings
   _lowercase    :   List[str]      =     tax_model["target"]["token_embedder"]["embedding"]
   _lowercase    :   str      =     txa_token_embeddings

   # LM Head (only in v1.1 checkpoints)
   if "logits_dense" in tax_model["target"]["decoder"]:
      _lowercase    :   Union[str, Any]      =     tax_model["target"]["decoder"]["logits_dense"]["kernel"]

   flax_model.save_pretrained(snake_case  )
   print("T5X Model was sucessfully converted!"  )


if __name__ == "__main__":
    __magic_name__     						=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
    )
    parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
    parser.add_argument(
        '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
    )
    __magic_name__     						=						parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









'''simple docstring'''
def       _A	(			snake_case  )    ->			list:
   _lowercase    :   Optional[Any]      =     1.3
   _lowercase    :   Optional[Any]      =     len(snake_case  )
   _lowercase    :   Optional[int]      =     False

   while not completed:
      # Update the gap value for a next comb
      _lowercase    :   Tuple      =     int(gap / shrink_factor  )
      if gap <= 1:
         _lowercase    :   List[str]      =     True

      _lowercase    :   Optional[int]      =     0
      while index + gap < len(snake_case  ):
         if data[index] > data[index + gap]:
            # Swap values
            _lowercase  ,   _lowercase    :   Any      =     data[index + gap], data[index]
            _lowercase    :   Tuple      =     False
         index += 1

   return data


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _snake_case     						=						input('Enter numbers separated by a comma:\n').strip()
    _snake_case     						=						[int(item) for item in user_input.split(',')]
    print(comb_sort(unsorted))






