code: 
  '''simple docstring'''
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase	) ->      Optional[Any]:
  		_UpperCamelCase       : Optional[Any]    =      TaConfig.from_pretrained(UpperCamelCase	)
  		_UpperCamelCase       : Optional[int]    =      FlaxTaForConditionalGeneration(config=UpperCamelCase	)
  		_UpperCamelCase       : Dict    =      checkpoints.load_tax_checkpoint(UpperCamelCase	)
  
  		_UpperCamelCase       : int    =      '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
  
  		# Encoder
  		for layer_index in range(config.num_layers	):
  				_UpperCamelCase       : Tuple    =      f'''layers_{str(UpperCamelCase	)}'''
  
  				# Self-Attention
  				_UpperCamelCase       : Optional[int]    =      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
  				_UpperCamelCase       : int    =      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
  				_UpperCamelCase       : Tuple    =      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
  				_UpperCamelCase       : int    =      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
  
  				# Layer Normalization
  				_UpperCamelCase       : List[Any]    =      tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
  
  				if split_mlp_wi:
  						_UpperCamelCase       : int    =      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
  						_UpperCamelCase       : Optional[Any]    =      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
  				else:
  						_UpperCamelCase       : Optional[Any]    =      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
  				_UpperCamelCase       : Optional[Any]    =      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
  				# Layer Normalization
  				_UpperCamelCase       : Union[str, Any]    =      tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
  				# Assigning
  				_UpperCamelCase       : List[Any]    =      tax_attention_key
  				_UpperCamelCase       : Any    =      tax_attention_out
  				_UpperCamelCase       : Optional[Any]    =      tax_attention_query
  				_UpperCamelCase       : Optional[Any]    =      tax_attention_value
  
  				_UpperCamelCase       : int    =      tax_attention_layer_norm
  
  				if split_mlp_wi:
  						_UpperCamelCase       : List[str]    =      tax_mlp_wi_a
  						_UpperCamelCase       : List[Any]    =      tax_mlp_wi_a
  				else:
  						_UpperCamelCase       : str    =      tax_mlp_wi
  
  				_UpperCamelCase       : Optional[Any]    =      tax_mlp_wo
  				_UpperCamelCase       : Tuple    =      tax_mlp_layer_norm
  
  		# Only for layer 0:
  		_UpperCamelCase       : int    =      tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
  		_UpperCamelCase       : Optional[Any]    =      tax_encoder_rel_embedding
  
  		# Assigning
  		_UpperCamelCase       : Dict    =      tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
  		_UpperCamelCase       : List[Any]    =      tax_encoder_norm
  
  		# Decoder
  		for layer_index in range(config.num_decoder_layers	):
  				_UpperCamelCase       : str    =      f'''layers_{str(UpperCamelCase	)}'''
  
  				# Self-Attention
  				_UpperCamelCase       : Tuple    =      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
  				_UpperCamelCase       : int    =      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
  				_UpperCamelCase       : List[str]    =      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
  				_UpperCamelCase       : Any    =      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
  
  				# Layer Normalization
  				_UpperCamelCase       : Optional[int]    =      tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
  				    '''scale'''
  				]
  
  				# Encoder-Decoder-Attention
  				_UpperCamelCase       : List[Any]    =      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
  				    '''kernel'''
  				]
  				_UpperCamelCase       : Optional[int]    =      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
  				    '''kernel'''
  				]
  				_UpperCamelCase       : List[str]    =      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
  				    '''kernel'''
  				]
  				_UpperCamelCase       : Optional[Any]    =      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
  				    '''kernel'''
  				]
  
  				# Layer Normalization
  				_UpperCamelCase       : Any    =      tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
  
  				# MLP
  				if split_mlp_wi:
  						_UpperCamelCase       : List[Any]    =      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
  						_UpperCamelCase       : Optional[Any]    =      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
  				else:
  						_UpperCamelCase       : List[Any]    =      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
  				_UpperCamelCase       : Any    =      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
  				# Layer Normalization
  				_UpperCamelCase       : Any    =      tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
  				# Assigning
  				_UpperCamelCase       : Union[str, Any]    =      tax_attention_key
  				_UpperCamelCase       : List[Any]    =      tax_attention_out
  				_UpperCamelCase       : List[str]    =      tax_attention_query
  				_UpperCamelCase       : Dict    =      tax_attention_value
  
  				_UpperCamelCase       : Dict    =      tax_pre_attention_layer_norm
  
  				_UpperCamelCase       : str    =      tax_enc_dec_attention_key
  				_UpperCamelCase       : int    =      tax_enc_dec_attention_out
  				_UpperCamelCase       : List[Any]    =      tax_enc_dec_attention_query
  				_UpperCamelCase       : Tuple    =      tax_enc_dec_attention_value
  
  				_UpperCamelCase       : Any    =      tax_cross_layer_norm
  
  				if split_mlp_wi:
  						_UpperCamelCase       : Union[str, Any]    =      tax_mlp_wi_a
  						_UpperCamelCase       : int    =      tax_mlp_wi_a
  				else:
  						_UpperCamelCase       : Union[str, Any]    =      tax_mlp_wi
  
  				_UpperCamelCase       : Dict    =      tax_mlp_wo
  
  				_UpperCamelCase       : Tuple    =      txa_mlp_layer_norm
  
  		# Decoder Normalization
  		_UpperCamelCase       : List[Any]    =      tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
  		_UpperCamelCase       : Optional[int]    =      txa_decoder_norm
  
  		# Only for layer 0:
  		_UpperCamelCase       : Optional[int]    =      tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
  		_UpperCamelCase       : str    =      tax_decoder_rel_embedding
  
  		# Token Embeddings
  		_UpperCamelCase       : int    =      tax_model['''target''']['''token_embedder''']['''embedding''']
  		_UpperCamelCase       : Optional[int]    =      txa_token_embeddings
  
  		# LM Head (only in v1.1 checkpoints)
  		if "logits_dense" in tax_model["target"]["decoder"]:
  				_UpperCamelCase       : Union[str, Any]    =      tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
  
  		flax_model.save_pretrained(UpperCamelCase	)
  		print('''T5X Model was sucessfully converted!'''	)
  
  
  if __name__ == "__main__":
  	_UpperCAmelCase   :      Optional[int] 						=  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."""
  	)
  	_UpperCAmelCase   :      Union[str, Any] 						=  parser.parse_args()
  	convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  import warnings
  
  from ...utils import logging
  from .image_processing_mobilenet_va import MobileNetVaImageProcessor
  
  
  _UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					*_snake_case  ,					**_snake_case     )				->    None:
  				warnings.warn(
  				    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
  				    ''' Please use MobileNetV2ImageProcessor instead.'''  ,					_snake_case  ,					)
  				super().__init__(*_snake_case  ,					**_snake_case     )
  
  
  
  
  
  