
'''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)







'''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     )





