


'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     int   ,_UpperCAmelCase     :     Union[str, Any]   ,_UpperCAmelCase     :     List[Any]  )  ->      Optional[Any]:
       _a   :			Tuple					 =TaConfig.from_pretrained(_UpperCAmelCase  )
       _a   :			int					 =FlaxTaForConditionalGeneration(config=_UpperCAmelCase  )
       _a   :			List[Any]					 =checkpoints.load_tax_checkpoint(_UpperCAmelCase  )

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

       # Encoder
       for layer_index in range(config.num_layers  ):
              _a   :			str					 =F"layers_{str(_UpperCAmelCase  )}"

              # Self-Attention
              _a   :			Dict					 =tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
              _a   :			str					 =tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
              _a   :			int					 =tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
              _a   :			Any					 =tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

              # Layer Normalization
              _a   :			List[Any]					 =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   :			Optional[int]					 =tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                     _a   :			List[str]					 =tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

              _a   :			List[str]					 =tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

              # Layer Normalization
              _a   :			Any					 =tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

              # Assigning
              _a   :			Dict					 =tax_attention_key
              _a   :			Optional[Any]					 =tax_attention_out
              _a   :			List[str]					 =tax_attention_query
              _a   :			List[Any]					 =tax_attention_value

              _a   :			List[Any]					 =tax_attention_layer_norm

              if split_mlp_wi:
                     _a   :			int					 =tax_mlp_wi_a
                     _a   :			str					 =tax_mlp_wi_a
              else:
                     _a   :			Tuple					 =tax_mlp_wi

              _a   :			Dict					 =tax_mlp_wo
              _a   :			Optional[Any]					 =tax_mlp_layer_norm

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

       # Assigning
       _a   :			Dict					 =tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
       _a   :			Any					 =tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_decoder_layers  ):
              _a   :			Optional[Any]					 =F"layers_{str(_UpperCAmelCase  )}"

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

              # Layer Normalization
              _a   :			int					 =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"""]["""key"""][
                  """kernel"""
              ]
              _a   :			List[str]					 =tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
                  """kernel"""
              ]
              _a   :			List[str]					 =tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
                  """kernel"""
              ]
              _a   :			List[Any]					 =tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""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   :			List[str]					 =tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                     _a   :			List[str]					 =tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                     _a   :			List[str]					 =tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

              _a   :			List[Any]					 =tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

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

              # Assigning
              _a   :			str					 =tax_attention_key
              _a   :			Union[str, Any]					 =tax_attention_out
              _a   :			Optional[int]					 =tax_attention_query
              _a   :			Any					 =tax_attention_value

              _a   :			Tuple					 =tax_pre_attention_layer_norm

              _a   :			Dict					 =tax_enc_dec_attention_key
              _a   :			Union[str, Any]					 =tax_enc_dec_attention_out
              _a   :			Tuple					 =tax_enc_dec_attention_query
              _a   :			List[str]					 =tax_enc_dec_attention_value

              _a   :			int					 =tax_cross_layer_norm

              if split_mlp_wi:
                     _a   :			Dict					 =tax_mlp_wi_a
                     _a   :			List[Any]					 =tax_mlp_wi_a
              else:
                     _a   :			Any					 =tax_mlp_wi

              _a   :			str					 =tax_mlp_wo

              _a   :			Dict					 =txa_mlp_layer_norm

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

       # Only for layer 0:
       _a   :			List[str]					 =tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
       _a   :			Tuple					 =tax_decoder_rel_embedding

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

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

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


if __name__ == "__main__":
   A__:   List[str]    =	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.'''
   )
   A__:   Tuple    =	parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






'''simple docstring'''







def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Union[str, Any]  )  ->      int:
       _a   :			List[str]					 =[701, 301, 132, 57, 23, 10, 4, 1]
       for gap in gaps:
              for i in range(_UpperCAmelCase   ,len(_UpperCAmelCase  )  ):
                     _a   :			Tuple					 =collection[i]
                     _a   :			Optional[Any]					 =i
                     while j >= gap and collection[j - gap] > insert_value:
                            _a   :			Union[str, Any]					 =collection[j - gap]
                            j -= gap
                     if j != i:
                            _a   :			Union[str, Any]					 =insert_value
       return collection


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

   testmod()
   A__:   int    =	input('''Enter numbers separated by a comma:\n''').strip()
   A__:   Optional[Any]    =	[int(item) for item in user_input.split(''',''')]
   print(shell_sort(unsorted))


