
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def    A (      lowercase					, lowercase					, lowercase				)     ->						Any:
		'''simple docstring'''

		UpperCamelCase						=  AutoConfig.from_pretrained(lowercase				)
		UpperCamelCase						=  FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase				)
		UpperCamelCase						=  checkpoints.load_tax_checkpoint(lowercase				)

		UpperCamelCase						=  'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

		if config.model_type == "t5":
				UpperCamelCase						=  'SelfAttention'
		if config.model_type == "longt5" and config.encoder_attention_type == "local":
				UpperCamelCase						=  'LocalSelfAttention'
		elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				UpperCamelCase						=  '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				):
				UpperCamelCase						=  f'''layers_{str(lowercase				)}'''

				# Self-Attention
				UpperCamelCase						=  tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
				UpperCamelCase						=  tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
				UpperCamelCase						=  tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
				UpperCamelCase						=  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":
						UpperCamelCase						=  tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

				# Layer Normalization
				UpperCamelCase						=  tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

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

				UpperCamelCase						=  tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

				# Layer Normalization
				UpperCamelCase						=  tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

				# Assigning
				UpperCamelCase						=  flax_model.params['encoder']['block'][str(lowercase				)]['layer']
				UpperCamelCase						=  tax_attention_key
				UpperCamelCase						=  tax_attention_out
				UpperCamelCase						=  tax_attention_query
				UpperCamelCase						=  tax_attention_value

				UpperCamelCase						=  tax_attention_layer_norm

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

				if split_mlp_wi:
						UpperCamelCase						=  tax_mlp_wi_a
						UpperCamelCase						=  tax_mlp_wi_a
				else:
						UpperCamelCase						=  tax_mlp_wi

				UpperCamelCase						=  tax_mlp_wo
				UpperCamelCase						=  tax_mlp_layer_norm

				UpperCamelCase						=  flax_model_encoder_layer_block

		# Only for layer 0:
		UpperCamelCase						=  tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
		UpperCamelCase						=  tax_encoder_rel_embedding

		# Side/global relative position_bias + layer norm
		if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				UpperCamelCase						=  tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
				UpperCamelCase						=  tax_encoder_global_rel_embedding

		# Assigning
		UpperCamelCase						=  tax_model['target']['encoder']['encoder_norm']['scale']
		UpperCamelCase						=  tax_encoder_norm

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

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

				# Layer Normalization
				UpperCamelCase						=  tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
				    'scale'
				]

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

				# Layer Normalization
				UpperCamelCase						=  tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

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

				UpperCamelCase						=  tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

				# Layer Normalization
				UpperCamelCase						=  tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

				# Assigning
				UpperCamelCase						=  flax_model.params['decoder']['block'][str(lowercase				)]['layer']
				UpperCamelCase						=  tax_attention_key
				UpperCamelCase						=  tax_attention_out
				UpperCamelCase						=  tax_attention_query
				UpperCamelCase						=  tax_attention_value

				UpperCamelCase						=  tax_pre_attention_layer_norm

				UpperCamelCase						=  tax_enc_dec_attention_key
				UpperCamelCase						=  tax_enc_dec_attention_out
				UpperCamelCase						=  tax_enc_dec_attention_query
				UpperCamelCase						=  tax_enc_dec_attention_value

				UpperCamelCase						=  tax_cross_layer_norm

				if split_mlp_wi:
						UpperCamelCase						=  tax_mlp_wi_a
						UpperCamelCase						=  tax_mlp_wi_a
				else:
						UpperCamelCase						=  tax_mlp_wi

				UpperCamelCase						=  tax_mlp_wo

				UpperCamelCase						=  txa_mlp_layer_norm

				UpperCamelCase						=  flax_model_decoder_layer_block

		# Decoder Normalization
		UpperCamelCase						=  tax_model['target']['decoder']['decoder_norm']['scale']
		UpperCamelCase						=  txa_decoder_norm

		# Only for layer 0:
		UpperCamelCase						=  tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
		UpperCamelCase						=  tax_decoder_rel_embedding

		# Token Embeddings
		UpperCamelCase						=  tax_model['target']['token_embedder']['embedding']
		UpperCamelCase						=  txa_token_embeddings

		# LM Head (only in v1.1 and LongT5 checkpoints)
		if "logits_dense" in tax_model["target"]["decoder"]:
				UpperCamelCase						=  tax_model['target']['decoder']['logits_dense']['kernel']

		flax_model.save_pretrained(lowercase				)
		print('T5X Model was sucessfully converted!'				)


if __name__ == "__main__":
					_UpperCAmelCase      :				Union[str, Any]          = 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."
					)
					_UpperCAmelCase      :				List[str]          = parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


import re


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		if len(re.findall('[ATCG]'					, lowercase				)				) != len(lowercase				):
				raise ValueError('Invalid Strand'				)

		return dna.translate(dna.maketrans('ATCG'					, 'TAGC'				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
