





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				List[Any]     ):
		SCREAMING_SNAKE_CASE__								=   AutoConfig.from_pretrained(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   checkpoints.load_tax_checkpoint(UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

		if config.model_type == "t5":
				SCREAMING_SNAKE_CASE__								=   """SelfAttention"""
		if config.model_type == "longt5" and config.encoder_attention_type == "local":
				SCREAMING_SNAKE_CASE__								=   """LocalSelfAttention"""
		elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				SCREAMING_SNAKE_CASE__								=   """TransientGlobalSelfAttention"""
		else:
				raise ValueError(
				    """Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"""
				    """ attribute with a value from ['local', 'transient-global]."""     )

		# Encoder
		for layer_index in range(config.num_layers     ):
				SCREAMING_SNAKE_CASE__								=   f'''layers_{str(UpperCamelCase__     )}'''

				# Self-Attention
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

				# Layer Normalization
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

				if split_mlp_wi:
						SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
						SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
				else:
						SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

				# Layer Normalization
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

				# Assigning
				SCREAMING_SNAKE_CASE__								=   flax_model.params["""encoder"""]["""block"""][str(UpperCamelCase__     )]["""layer"""]
				SCREAMING_SNAKE_CASE__								=   tax_attention_key
				SCREAMING_SNAKE_CASE__								=   tax_attention_out
				SCREAMING_SNAKE_CASE__								=   tax_attention_query
				SCREAMING_SNAKE_CASE__								=   tax_attention_value

				SCREAMING_SNAKE_CASE__								=   tax_attention_layer_norm

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						SCREAMING_SNAKE_CASE__								=   tax_global_layer_norm

				if split_mlp_wi:
						SCREAMING_SNAKE_CASE__								=   tax_mlp_wi_a
						SCREAMING_SNAKE_CASE__								=   tax_mlp_wi_a
				else:
						SCREAMING_SNAKE_CASE__								=   tax_mlp_wi

				SCREAMING_SNAKE_CASE__								=   tax_mlp_wo
				SCREAMING_SNAKE_CASE__								=   tax_mlp_layer_norm

				SCREAMING_SNAKE_CASE__								=   flax_model_encoder_layer_block

		# Only for layer 0:
		SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
		SCREAMING_SNAKE_CASE__								=   tax_encoder_rel_embedding

		# Side/global relative position_bias + layer norm
		if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
				SCREAMING_SNAKE_CASE__								=   tax_encoder_global_rel_embedding

		# Assigning
		SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
		SCREAMING_SNAKE_CASE__								=   tax_encoder_norm

		# Decoder
		for layer_index in range(config.num_layers     ):
				SCREAMING_SNAKE_CASE__								=   f'''layers_{str(UpperCamelCase__     )}'''

				# Self-Attention
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

				# Layer Normalization
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
				    """scale"""
				]

				# Encoder-Decoder-Attention
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
				SCREAMING_SNAKE_CASE__								=   tax_enc_dec_attention_module["""key"""]["""kernel"""]
				SCREAMING_SNAKE_CASE__								=   tax_enc_dec_attention_module["""out"""]["""kernel"""]
				SCREAMING_SNAKE_CASE__								=   tax_enc_dec_attention_module["""query"""]["""kernel"""]
				SCREAMING_SNAKE_CASE__								=   tax_enc_dec_attention_module["""value"""]["""kernel"""]

				# Layer Normalization
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

				# MLP
				if split_mlp_wi:
						SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
						SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
				else:
						SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

				# Layer Normalization
				SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

				# Assigning
				SCREAMING_SNAKE_CASE__								=   flax_model.params["""decoder"""]["""block"""][str(UpperCamelCase__     )]["""layer"""]
				SCREAMING_SNAKE_CASE__								=   tax_attention_key
				SCREAMING_SNAKE_CASE__								=   tax_attention_out
				SCREAMING_SNAKE_CASE__								=   tax_attention_query
				SCREAMING_SNAKE_CASE__								=   tax_attention_value

				SCREAMING_SNAKE_CASE__								=   tax_pre_attention_layer_norm

				SCREAMING_SNAKE_CASE__								=   tax_enc_dec_attention_key
				SCREAMING_SNAKE_CASE__								=   tax_enc_dec_attention_out
				SCREAMING_SNAKE_CASE__								=   tax_enc_dec_attention_query
				SCREAMING_SNAKE_CASE__								=   tax_enc_dec_attention_value

				SCREAMING_SNAKE_CASE__								=   tax_cross_layer_norm

				if split_mlp_wi:
						SCREAMING_SNAKE_CASE__								=   tax_mlp_wi_a
						SCREAMING_SNAKE_CASE__								=   tax_mlp_wi_a
				else:
						SCREAMING_SNAKE_CASE__								=   tax_mlp_wi

				SCREAMING_SNAKE_CASE__								=   tax_mlp_wo

				SCREAMING_SNAKE_CASE__								=   txa_mlp_layer_norm

				SCREAMING_SNAKE_CASE__								=   flax_model_decoder_layer_block

		# Decoder Normalization
		SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
		SCREAMING_SNAKE_CASE__								=   txa_decoder_norm

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

		# Token Embeddings
		SCREAMING_SNAKE_CASE__								=   tax_model["""target"""]["""token_embedder"""]["""embedding"""]
		SCREAMING_SNAKE_CASE__								=   txa_token_embeddings

		# LM Head (only in v1.1 and LongT5 checkpoints)
		if "logits_dense" in tax_model["target"]["decoder"]:
				SCREAMING_SNAKE_CASE__								=   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 T5X checkpoint.'
	)
	parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "AutoImageProcessor"
							lowerCamelCase_							     =       "AutoTokenizer"


							def __init__(				self     :Optional[int]				,					__A     :Optional[Any]				,					__A     :Dict		) -> Dict:



									"""simple docstring"""



									super().__init__(__A				,					__A		)
									SCREAMING_SNAKE_CASE__								=   self.image_processor


							def __call__(				self     :int				,					__A     :str=None				,					__A     :int=None				,					__A     :Union[str, Any]=None				,					**__A     :str		) -> Optional[Any]:



									"""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:
											SCREAMING_SNAKE_CASE__								=   self.tokenizer(__A				,					return_tensors=__A				,					**__A		)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=   self.image_processor(__A				,					return_tensors=__A				,					**__A		)

									if text is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif text is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**__A		)				,					tensor_type=__A		)


							def   _snake_case	(				self     :str				,					*__A     :List[str]				,					**__A     :List[str]		) -> List[Any]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :List[str]				,					*__A     :Any				,					**__A     :Any		) -> Tuple:



									"""simple docstring"""



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


							@property
							def   _snake_case	(				self     :Dict		) -> List[Any]:



									"""simple docstring"""



									return ["input_ids", "attention_mask", "pixel_values"]