'''simple docstring'''





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			Dict:




						'''simple docstring'''
						_UpperCamelCase			:			int   			=						AutoConfig.from_pretrained(UpperCAmelCase					)
						_UpperCamelCase			:			Tuple   			=						FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCAmelCase					)
						_UpperCamelCase			:			str   			=						checkpoints.load_tax_checkpoint(UpperCAmelCase					)

						_UpperCamelCase			:			str   			=						"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

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

												# Self-Attention
												_UpperCamelCase			:			Optional[int]   			=						tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
												_UpperCamelCase			:			List[Any]   			=						tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
												_UpperCamelCase			:			Optional[Any]   			=						tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
												_UpperCamelCase			:			Optional[int]   			=						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			:			Optional[Any]   			=						tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

												# Layer Normalization
												_UpperCamelCase			:			Any   			=						tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

												if split_mlp_wi:
																		_UpperCamelCase			:			Dict   			=						tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
																		_UpperCamelCase			:			List[str]   			=						tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
												else:
																		_UpperCamelCase			:			int   			=						tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

												_UpperCamelCase			:			Tuple   			=						tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

												# Layer Normalization
												_UpperCamelCase			:			List[Any]   			=						tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

												# Assigning
												_UpperCamelCase			:			int   			=						flax_model.params["encoder"]["block"][str(UpperCAmelCase					)]["layer"]
												_UpperCamelCase			:			str   			=						tax_attention_key
												_UpperCamelCase			:			Union[str, Any]   			=						tax_attention_out
												_UpperCamelCase			:			int   			=						tax_attention_query
												_UpperCamelCase			:			Optional[int]   			=						tax_attention_value

												_UpperCamelCase			:			str   			=						tax_attention_layer_norm

												# Global input layer norm
												if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																		_UpperCamelCase			:			Optional[Any]   			=						tax_global_layer_norm

												if split_mlp_wi:
																		_UpperCamelCase			:			str   			=						tax_mlp_wi_a
																		_UpperCamelCase			:			str   			=						tax_mlp_wi_a
												else:
																		_UpperCamelCase			:			Dict   			=						tax_mlp_wi

												_UpperCamelCase			:			List[Any]   			=						tax_mlp_wo
												_UpperCamelCase			:			Tuple   			=						tax_mlp_layer_norm

												_UpperCamelCase			:			Optional[int]   			=						flax_model_encoder_layer_block

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

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

						# Assigning
						_UpperCamelCase			:			Dict   			=						tax_model["target"]["encoder"]["encoder_norm"]["scale"]
						_UpperCamelCase			:			str   			=						tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_layers					):
												_UpperCamelCase			:			List[Any]   			=						f'''layers_{str(UpperCAmelCase					)}'''

												# Self-Attention
												_UpperCamelCase			:			List[Any]   			=						tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
												_UpperCamelCase			:			Optional[int]   			=						tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
												_UpperCamelCase			:			Optional[Any]   			=						tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
												_UpperCamelCase			:			Optional[Any]   			=						tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

												# Layer Normalization
												_UpperCamelCase			:			Any   			=						tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
												    "scale"
												]

												# Encoder-Decoder-Attention
												_UpperCamelCase			:			Union[str, Any]   			=						tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
												_UpperCamelCase			:			str   			=						tax_enc_dec_attention_module["key"]["kernel"]
												_UpperCamelCase			:			Optional[Any]   			=						tax_enc_dec_attention_module["out"]["kernel"]
												_UpperCamelCase			:			Dict   			=						tax_enc_dec_attention_module["query"]["kernel"]
												_UpperCamelCase			:			Any   			=						tax_enc_dec_attention_module["value"]["kernel"]

												# Layer Normalization
												_UpperCamelCase			:			Union[str, Any]   			=						tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

												# MLP
												if split_mlp_wi:
																		_UpperCamelCase			:			List[str]   			=						tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
																		_UpperCamelCase			:			List[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			:			Dict   			=						tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

												# Assigning
												_UpperCamelCase			:			str   			=						flax_model.params["decoder"]["block"][str(UpperCAmelCase					)]["layer"]
												_UpperCamelCase			:			List[str]   			=						tax_attention_key
												_UpperCamelCase			:			Optional[Any]   			=						tax_attention_out
												_UpperCamelCase			:			Dict   			=						tax_attention_query
												_UpperCamelCase			:			Dict   			=						tax_attention_value

												_UpperCamelCase			:			Union[str, Any]   			=						tax_pre_attention_layer_norm

												_UpperCamelCase			:			Dict   			=						tax_enc_dec_attention_key
												_UpperCamelCase			:			str   			=						tax_enc_dec_attention_out
												_UpperCamelCase			:			int   			=						tax_enc_dec_attention_query
												_UpperCamelCase			:			Optional[Any]   			=						tax_enc_dec_attention_value

												_UpperCamelCase			:			Optional[int]   			=						tax_cross_layer_norm

												if split_mlp_wi:
																		_UpperCamelCase			:			Tuple   			=						tax_mlp_wi_a
																		_UpperCamelCase			:			Optional[Any]   			=						tax_mlp_wi_a
												else:
																		_UpperCamelCase			:			str   			=						tax_mlp_wi

												_UpperCamelCase			:			List[Any]   			=						tax_mlp_wo

												_UpperCamelCase			:			Dict   			=						txa_mlp_layer_norm

												_UpperCamelCase			:			Tuple   			=						flax_model_decoder_layer_block

						# Decoder Normalization
						_UpperCamelCase			:			str   			=						tax_model["target"]["decoder"]["decoder_norm"]["scale"]
						_UpperCamelCase			:			Any   			=						txa_decoder_norm

						# Only for layer 0:
						_UpperCamelCase			:			int   			=						tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
						_UpperCamelCase			:			Union[str, Any]   			=						tax_decoder_rel_embedding

						# Token Embeddings
						_UpperCamelCase			:			List[Any]   			=						tax_model["target"]["token_embedder"]["embedding"]
						_UpperCamelCase			:			Optional[Any]   			=						txa_token_embeddings

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

						flax_model.save_pretrained(UpperCAmelCase					)
						print("T5X Model was sucessfully converted!"					)


if __name__ == "__main__":
			lowerCAmelCase_	:		int			=     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_	:		List[str]			=     parser.parse_args()
			convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



'''simple docstring'''





def 						__A							(     UpperCAmelCase					)     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						[]
						_UpperCamelCase			:			Optional[Any]   			=						set({"(", "[", "{"}					)
						_UpperCamelCase			:			int   			=						set({")", "]", "}"}					)
						_UpperCamelCase			:			Dict   			=						{"{": "}", "[": "]", "(": ")"}

						for i in range(len(UpperCAmelCase					)					):
												if s[i] in open_brackets:
																		stack.append(s[i]					)

												elif s[i] in closed_brackets and (
												    len(UpperCAmelCase					) == 0 or (len(UpperCAmelCase					) > 0 and open_to_closed[stack.pop()] != s[i])
												):
																		return False

						return len(UpperCAmelCase					) == 0
def 						__A							(     )     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						input("Enter sequence of brackets: "					)
						if is_balanced(UpperCAmelCase					):
												print(UpperCAmelCase     ,"is balanced"					)
						else:
												print(UpperCAmelCase     ,"is not balanced"					)


if __name__ == "__main__":
			main()


