code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  _lowerCamelCase				:	int				    =       {
      'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
  }
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				_lowerCamelCase				:	Union[str, Any]				    =       [
  				    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'NystromformerForMaskedLM',
  				    'NystromformerForMultipleChoice',
  				    'NystromformerForQuestionAnswering',
  				    'NystromformerForSequenceClassification',
  				    'NystromformerForTokenClassification',
  				    'NystromformerLayer',
  				    'NystromformerModel',
  				    'NystromformerPreTrainedModel',
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_nystromformer import (
  								    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    NystromformerForMaskedLM,
  								    NystromformerForMultipleChoice,
  								    NystromformerForQuestionAnswering,
  								    NystromformerForSequenceClassification,
  								    NystromformerForTokenClassification,
  								    NystromformerLayer,
  								    NystromformerModel,
  								    NystromformerPreTrainedModel,
  								)
  
  
  else:
  				import sys
  
  				_lowerCamelCase				:	str				    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->List[Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      TaConfig.from_pretrained(UpperCAmelCase    )
  	A							  =      FlaxTaForConditionalGeneration(config=UpperCAmelCase    )
  	A							  =      checkpoints.load_tax_checkpoint(UpperCAmelCase    )
  
  	A							  =      """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
  	# Encoder
  	for layer_index in range(config.num_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_attention_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  		A							  =      tax_mlp_layer_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_encoder_rel_embedding
  
  	# Assigning
  	A							  =      tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  	A							  =      tax_encoder_norm
  
  	# Decoder
  	for layer_index in range(config.num_decoder_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
  		    """scale"""
  		]
  
  		# Encoder-Decoder-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
  		    """kernel"""
  		]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
  		# MLP
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_pre_attention_layer_norm
  
  		A							  =      tax_enc_dec_attention_key
  		A							  =      tax_enc_dec_attention_out
  		A							  =      tax_enc_dec_attention_query
  		A							  =      tax_enc_dec_attention_value
  
  		A							  =      tax_cross_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  
  		A							  =      txa_mlp_layer_norm
  
  	# Decoder Normalization
  	A							  =      tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  	A							  =      txa_decoder_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_decoder_rel_embedding
  
  	# Token Embeddings
  	A							  =      tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  	A							  =      txa_token_embeddings
  
  	# LM Head (only in v1.1 checkpoints)
  	if "logits_dense" in tax_model["target"]["decoder"]:
  		A							  =      tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
  
  	flax_model.save_pretrained(UpperCAmelCase    )
  	print("""T5X Model was sucessfully converted!"""    )
  
  
  if __name__ == "__main__":
  				_lowerCamelCase				:	Tuple				    =       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				:	Union[str, Any]				    =       parser.parse_args()
  				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  