





'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def  _lowerCamelCase				( lowercase						:      List[str]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Dict						)	->	Optional[Any]:
				_a								= AutoConfig.from_pretrained(lowercase						)
				_a								= FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase						)
				_a								= checkpoints.load_tax_checkpoint(lowercase						)

				_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(lowercase						)}'

								# 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(lowercase						)]["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(lowercase						)}'

								# 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(lowercase						)]["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(lowercase						)
				print("T5X Model was sucessfully converted!"						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Dict      	=			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_					:    Optional[Any]      	=			parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCAmelCase					: Tuple      =       tuple[int, int]


class 				lowercase					:



		def __init__( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				UpperCamelCase		=     vertices
				UpperCamelCase		=     {
				    (min(A_       ), max(A_       )): weight for edge, weight in edges.items()
				}



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				self.vertices.add(edge[0]       )
				self.vertices.add(edge[1]       )
				UpperCamelCase		=     weight



		def __UpperCamelCase ( self       )  ->					Graph:
				"""simple docstring"""
				UpperCamelCase		=     Graph({min(self.vertices       )}				,      {}       )
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42

				while len(subgraph.vertices       ) < len(self.vertices       ):
						UpperCamelCase		=     max(self.edges.values()       ) + 1
						for edge, weight in self.edges.items():
								if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
										if weight < min_weight:
												UpperCamelCase		=     edge
												UpperCamelCase		=     weight

						subgraph.add_edge(A_				,      A_       )

				return subgraph






def       A       (					lowercase = "p107_network.txt" )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     os.path.abspath(os.path.dirname(lowercase ) )
		UpperCamelCase		=     os.path.join(lowercase    ,       lowercase )
		UpperCamelCase		=     {}
		UpperCamelCase		=     42
		UpperCamelCase		=     42
		UpperCamelCase		=     42

		with open(lowercase ) as f:
				UpperCamelCase		=     f.read().strip().split('\n' )

		UpperCamelCase		=     [line.split(',' ) for line in data]

		for edgea in range(1    ,       len(lowercase ) ):
				for edgea in range(lowercase ):
						if adjaceny_matrix[edgea][edgea] != "-":
								UpperCamelCase		=     int(adjaceny_matrix[edgea][edgea] )

		UpperCamelCase		=     Graph(set(range(len(lowercase ) ) )    ,       lowercase )

		UpperCamelCase		=     graph.prims_algorithm()

		UpperCamelCase		=     sum(graph.edges.values() )
		UpperCamelCase		=     sum(subgraph.edges.values() )

		return initial_total - optimal_total


if __name__ == "__main__":
					print(F'''{solution() = }''')
