


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Union[str, Any]				      =     TaConfig.from_pretrained(__UpperCamelCase			)
     a_     :  Dict				      =     FlaxTaForConditionalGeneration(config=__UpperCamelCase			)
     a_     :  Tuple				      =     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_     :  Dict				      =     F'''layers_{str(__UpperCamelCase			)}'''

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

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

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

          a_     :  Optional[Any]				      =     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_     :  Dict				      =     tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

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

          a_     :  List[str]				      =     tax_attention_layer_norm

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

          a_     :  Union[str, Any]				      =     tax_mlp_wo
          a_     :  Tuple				      =     tax_mlp_layer_norm

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

     # Assigning
     a_     :  Optional[int]				      =     tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
     a_     :  Tuple				      =     tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers			):
          a_     :  List[Any]				      =     F'''layers_{str(__UpperCamelCase			)}'''

          # Self-Attention
          a_     :  int				      =     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
          a_     :  Union[str, Any]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
          a_     :  List[str]				      =     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_     :  List[str]				      =     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_     :  int				      =     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
              """kernel"""
          ]
          a_     :  Any				      =     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_     :  int				      =     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_     :  List[Any]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_     :  Optional[int]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_     :  Optional[Any]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_     :  List[str]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_     :  str				      =     tax_attention_key
          a_     :  Any				      =     tax_attention_out
          a_     :  Dict				      =     tax_attention_query
          a_     :  Any				      =     tax_attention_value

          a_     :  Tuple				      =     tax_pre_attention_layer_norm

          a_     :  int				      =     tax_enc_dec_attention_key
          a_     :  Tuple				      =     tax_enc_dec_attention_out
          a_     :  Optional[Any]				      =     tax_enc_dec_attention_query
          a_     :  Tuple				      =     tax_enc_dec_attention_value

          a_     :  int				      =     tax_cross_layer_norm

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

          a_     :  Union[str, Any]				      =     tax_mlp_wo

          a_     :  Tuple				      =     txa_mlp_layer_norm

     # Decoder Normalization
     a_     :  Union[str, Any]				      =     tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
     a_     :  str				      =     txa_decoder_norm

     # Only for layer 0:
     a_     :  Tuple				      =     tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
     a_     :  Dict				      =     tax_decoder_rel_embedding

     # Token Embeddings
     a_     :  str				      =     tax_model["""target"""]["""token_embedder"""]["""embedding"""]
     a_     :  Optional[int]				      =     txa_token_embeddings

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

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


if __name__ == "__main__":
       __lowerCamelCase   	=  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.'''
       )
       __lowerCamelCase   	=  parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









from .stack import Stack


def   A		(__A  :       str			)  -> bool:







     """simple docstring"""


     UpperCAmelCase_		=				Stack()
     UpperCAmelCase_		=				{'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(__A			)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


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

    testmod()

    snake_case_					:					Dict       						=			["((()))", "((())", "(()))"]
    print("Balanced parentheses demonstration:\n")
    for example in examples:
        snake_case_					:					Optional[int]       						=			"" if balanced_parentheses(example) else "not "
        print(f"{example} is {not_str}balanced")






