
'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Tuple      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	Tuple			)					->							Any:





  """simple docstring"""

  __A		:  str       				=		TaConfig.from_pretrained(_A			)
  __A		:  List[Any]       				=		FlaxTaForConditionalGeneration(config=_A			)
  __A		:  str       				=		checkpoints.load_tax_checkpoint(_A			)

  __A		:  Union[str, Any]       				=		"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

  # Encoder
  for layer_index in range(config.num_layers			):
    __A		:  List[Any]       				=		F"layers_{str(_A			)}"

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

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

    if split_mlp_wi:
      __A		:  List[Any]       				=		tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      __A		:  List[str]       				=		tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      __A		:  Union[str, Any]       				=		tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    __A		:  Any       				=		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		:  List[Any]       				=		tax_attention_key
    __A		:  Dict       				=		tax_attention_out
    __A		:  Optional[Any]       				=		tax_attention_query
    __A		:  Dict       				=		tax_attention_value

    __A		:  str       				=		tax_attention_layer_norm

    if split_mlp_wi:
      __A		:  List[Any]       				=		tax_mlp_wi_a
      __A		:  List[str]       				=		tax_mlp_wi_a
    else:
      __A		:  Any       				=		tax_mlp_wi

    __A		:  int       				=		tax_mlp_wo
    __A		:  List[str]       				=		tax_mlp_layer_norm

  # Only for layer 0:
  __A		:  str       				=		tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  __A		:  List[str]       				=		tax_encoder_rel_embedding

  # Assigning
  __A		:  Dict       				=		tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  __A		:  int       				=		tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_decoder_layers			):
    __A		:  int       				=		F"layers_{str(_A			)}"

    # Self-Attention
    __A		:  Optional[Any]       				=		tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
    __A		:  Optional[int]       				=		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		:  Optional[Any]       				=		tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
        """scale"""
    ]

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

    # Layer Normalization
    __A		:  Union[str, Any]       				=		tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

    # MLP
    if split_mlp_wi:
      __A		:  List[Any]       				=		tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      __A		:  Dict       				=		tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      __A		:  Union[str, Any]       				=		tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    __A		:  Any       				=		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		:  Optional[Any]       				=		tax_attention_key
    __A		:  List[Any]       				=		tax_attention_out
    __A		:  str       				=		tax_attention_query
    __A		:  Tuple       				=		tax_attention_value

    __A		:  List[Any]       				=		tax_pre_attention_layer_norm

    __A		:  str       				=		tax_enc_dec_attention_key
    __A		:  List[str]       				=		tax_enc_dec_attention_out
    __A		:  Tuple       				=		tax_enc_dec_attention_query
    __A		:  Optional[Any]       				=		tax_enc_dec_attention_value

    __A		:  Dict       				=		tax_cross_layer_norm

    if split_mlp_wi:
      __A		:  List[str]       				=		tax_mlp_wi_a
      __A		:  Any       				=		tax_mlp_wi_a
    else:
      __A		:  List[Any]       				=		tax_mlp_wi

    __A		:  Optional[int]       				=		tax_mlp_wo

    __A		:  Optional[int]       				=		txa_mlp_layer_norm

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

  # Only for layer 0:
  __A		:  int       				=		tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  __A		:  str       				=		tax_decoder_rel_embedding

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

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

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


if __name__ == "__main__":
     A__ :		Union[str, Any]					=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__ :		Optional[int]					=parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)











'''simple docstring'''


lowerCamelCase_					:			List[Any]     						=				{0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

lowerCamelCase_					:			Tuple     						=				{0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}







def 						__magic_name__(			_A    ,   _A    ,   _A       ):
						'''simple docstring'''





						UpperCamelCase__       =			True
						UpperCamelCase__       =			[]

						for neighbour in graph[vert]:
												if not visited[neighbour]:
																		connected_verts += dfs(_A    ,   _A    ,   _A       )

						return [vert, *connected_verts]







def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			len(_A       )
						UpperCamelCase__       =			graph_size * [False]
						UpperCamelCase__       =			[]

						for i in range(_A       ):
												if not visited[i]:
																		UpperCamelCase__       =			dfs(_A    ,   _A    ,   _A       )
																		components_list.append(_A       )

						return components_list


if __name__ == "__main__":
					import doctest

					doctest.testmod()
