


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)







"""simple docstring"""






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Union[str, Any]				  =  ['''image_processor''', '''tokenizer''']
							__magic_name__					:     Optional[int]				  =  '''EfficientNetImageProcessor'''
							__magic_name__					:     Optional[Any]				  =  ('''BertTokenizer''', '''BertTokenizerFast''')




							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__(lowerCamelCase__	,					lowerCamelCase__)




							def __call__(						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="max_length"	,					lowerCamelCase__=64	,					lowerCamelCase__=None	,					**lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none.")

								if text is not None:
									snake_case__  :						int		=      self.tokenizer(
									    lowerCamelCase__	,					padding=lowerCamelCase__	,					max_length=lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if images is not None:
									snake_case__  :						int		=      self.image_processor(lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if text is not None and images is not None:
									snake_case__  :						List[str]		=      image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**lowerCamelCase__)	,					tensor_type=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




								return self.tokenizer.batch_decode(*lowerCamelCase__	,					**lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								return self.tokenizer.decode(*lowerCamelCase__	,					**lowerCamelCase__)







							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.tokenizer.model_input_names
								snake_case__  :						Optional[int]		=      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





