



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Tuple    ):
				"""simple docstring"""







				__a       =		AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
				__a       =		FlaxAutoModelForSeqaSeqLM.from_config(config=_SCREAMING_SNAKE_CASE    )
				__a       =		checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE    )

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

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

								# 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"""]

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

								# 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       =		flax_model.params["""encoder"""]["""block"""][str(_SCREAMING_SNAKE_CASE    )]["""layer"""]
								__a       =		tax_attention_key
								__a       =		tax_attention_out
								__a       =		tax_attention_query
								__a       =		tax_attention_value

								__a       =		tax_attention_layer_norm

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

								__a       =		flax_model_encoder_layer_block

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

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

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

				# Decoder
				for layer_index in range(config.num_layers    ):
								__a       =		f"layers_{str(_SCREAMING_SNAKE_CASE    )}"

								# 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"""]
								__a       =		tax_enc_dec_attention_module["""key"""]["""kernel"""]
								__a       =		tax_enc_dec_attention_module["""out"""]["""kernel"""]
								__a       =		tax_enc_dec_attention_module["""query"""]["""kernel"""]
								__a       =		tax_enc_dec_attention_module["""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       =		flax_model.params["""decoder"""]["""block"""][str(_SCREAMING_SNAKE_CASE    )]["""layer"""]
								__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

								__a       =		flax_model_decoder_layer_block

				# 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 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								__a       =		tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

				flax_model.save_pretrained(_SCREAMING_SNAKE_CASE    )
				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)





def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int = 3				,     _SCREAMING_SNAKE_CASE       :			int = 7				,     _SCREAMING_SNAKE_CASE       :			int = 100_0000    ):
				"""simple docstring"""







				__a       =		0
				__a       =		1

				for current_denominator in range(1				,     limit + 1    ):
								__a       =		current_denominator * numerator // denominator
								if current_denominator % denominator == 0:
												current_numerator -= 1
								if current_numerator * max_denominator > current_denominator * max_numerator:
												__a       =		current_numerator
												__a       =		current_denominator
				return max_numerator


if __name__ == "__main__":
						print(solution(numerator=3, denominator=7, limit=100_0000))
