

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def 		UpperCamelCase   (lowercase_:					List[Any]       , lowercase_:					List[str]       , lowercase_:					str   )	-> List[str]:
	return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]

def 		UpperCamelCase   (lowercase_:					Optional[Any]       , lowercase_:					str       , lowercase_:					int       , lowercase_:					str="attention"   )	-> Any:
	A__				:       Dict	  =						np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]   )
	A__				:       Tuple	  =						k_tmp.reshape(k_tmp.shape[0]       , k_tmp.shape[1] * k_tmp.shape[2]   )
	A__				:       Optional[Any]	  =						np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]   )
	A__				:       Optional[int]	  =						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]       , o_tmp.shape[2]   )
	A__				:       Union[str, Any]	  =						np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]   )
	A__				:       Optional[Any]	  =						q_tmp.reshape(q_tmp.shape[0]       , q_tmp.shape[1] * q_tmp.shape[2]   )
	A__				:       Optional[int]	  =						np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]   )
	A__				:       Optional[int]	  =						v_tmp.reshape(v_tmp.shape[0]       , v_tmp.shape[1] * v_tmp.shape[2]   )
	return k, o, q, v

def 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					Union[str, Any]       , lowercase_:					Optional[Any]       , lowercase_:					List[Any]=False   )	-> int:
	if split_mlp_wi:
		A__				:       str	  =						params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
		A__				:       List[str]	  =						params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
		A__				:       Dict	  =						(wi_a, wi_a)
	else:
		A__				:       str	  =						params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

	A__				:       Dict	  =						params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
	return wi, wo

def 		UpperCamelCase   (lowercase_:					Optional[Any]       , lowercase_:					str       , lowercase_:					Union[str, Any]       , lowercase_:					int   )	-> Union[str, Any]:
	return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]

def 		UpperCamelCase   (lowercase_:					dict       , *, lowercase_:					int       , lowercase_:					bool       , lowercase_:					bool = False   )	-> Union[str, Any]:
	A__				:       List[Any]	  =						traverse_util.flatten_dict(variables["""target"""]   )
	A__				:       Tuple	  =						{"""/""".join(lowercase_   ): v for k, v in old.items()}

	# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
	A__				:       int	  =						"""encoder/encoder/mlp/wi_0/kernel""" in old
	print("""Split MLP:"""       , lowercase_   )

	A__				:       List[str]	  =						collections.OrderedDict()

	# Shared embeddings.
	A__				:       Optional[int]	  =						old["""token_embedder/embedding"""]

	# Encoder.
	for i in range(lowercase_   ):
		# Block i, layer 0 (Self Attention).
		A__				:       List[Any]	  =						tax_layer_norm_lookup(lowercase_       , lowercase_       , """encoder"""       , """pre_attention_layer_norm"""   )
		A__  ,  A__  ,  A__  ,  A__				:       Union[str, Any]	  =						tax_attention_lookup(lowercase_       , lowercase_       , """encoder"""       , """attention"""   )
		A__				:       Union[str, Any]	  =						layer_norm
		A__				:       Tuple	  =						k.T
		A__				:       Optional[Any]	  =						o.T
		A__				:       str	  =						q.T
		A__				:       Tuple	  =						v.T

		# Block i, layer 1 (MLP).
		A__				:       Tuple	  =						tax_layer_norm_lookup(lowercase_       , lowercase_       , """encoder"""       , """pre_mlp_layer_norm"""   )
		A__  ,  A__				:       List[Any]	  =						tax_mlp_lookup(lowercase_       , lowercase_       , """encoder"""       , lowercase_   )
		A__				:       Optional[Any]	  =						layer_norm
		if split_mlp_wi:
			A__				:       str	  =						wi[0].T
			A__				:       Optional[Any]	  =						wi[1].T
		else:
			A__				:       Union[str, Any]	  =						wi.T
		A__				:       str	  =						wo.T
		if scalable_attention:
			# convert the rel_embedding of each layer
			A__				:       List[str]	  =						tax_relpos_bias_lookup(
			    lowercase_       , lowercase_       , """encoder"""   ).T

	A__				:       List[Any]	  =						old["""encoder/encoder_norm/scale"""]

	if not scalable_attention:
		A__				:       Optional[int]	  =						tax_relpos_bias_lookup(
		    lowercase_       , 0       , """encoder"""   ).T
		A__				:       Union[str, Any]	  =						tax_relpos_bias_lookup(
		    lowercase_       , 0       , """decoder"""   ).T

	if not is_encoder_only:
		# Decoder.
		for i in range(lowercase_   ):
			# Block i, layer 0 (Self Attention).
			A__				:       int	  =						tax_layer_norm_lookup(lowercase_       , lowercase_       , """decoder"""       , """pre_self_attention_layer_norm"""   )
			A__  ,  A__  ,  A__  ,  A__				:       Dict	  =						tax_attention_lookup(lowercase_       , lowercase_       , """decoder"""       , """self_attention"""   )
			A__				:       Optional[Any]	  =						layer_norm
			A__				:       Union[str, Any]	  =						k.T
			A__				:       Optional[Any]	  =						o.T
			A__				:       Any	  =						q.T
			A__				:       Optional[Any]	  =						v.T

			# Block i, layer 1 (Cross Attention).
			A__				:       List[Any]	  =						tax_layer_norm_lookup(lowercase_       , lowercase_       , """decoder"""       , """pre_cross_attention_layer_norm"""   )
			A__  ,  A__  ,  A__  ,  A__				:       Any	  =						tax_attention_lookup(lowercase_       , lowercase_       , """decoder"""       , """encoder_decoder_attention"""   )
			A__				:       Any	  =						layer_norm
			A__				:       int	  =						k.T
			A__				:       Union[str, Any]	  =						o.T
			A__				:       int	  =						q.T
			A__				:       Tuple	  =						v.T

			# Block i, layer 2 (MLP).
			A__				:       Tuple	  =						tax_layer_norm_lookup(lowercase_       , lowercase_       , """decoder"""       , """pre_mlp_layer_norm"""   )
			A__  ,  A__				:       Union[str, Any]	  =						tax_mlp_lookup(lowercase_       , lowercase_       , """decoder"""       , lowercase_   )
			A__				:       Optional[int]	  =						layer_norm
			if split_mlp_wi:
				A__				:       Dict	  =						wi[0].T
				A__				:       Optional[int]	  =						wi[1].T
			else:
				A__				:       Optional[int]	  =						wi.T
			A__				:       Any	  =						wo.T

			if scalable_attention:
				# convert the rel_embedding of each layer
				A__				:       Optional[Any]	  =						tax_relpos_bias_lookup(lowercase_       , lowercase_       , """decoder"""   ).T

		A__				:       Any	  =						old["""decoder/decoder_norm/scale"""]

		# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
		if "decoder/logits_dense/kernel" in old:
			A__				:       str	  =						old["""decoder/logits_dense/kernel"""].T

	return new

def 		UpperCamelCase   (lowercase_:					str       , lowercase_:					bool   )	-> Union[str, Any]:
	A__				:       Union[str, Any]	  =						collections.OrderedDict([(k, torch.from_numpy(v.copy()   )) for (k, v) in converted_params.items()]   )

	# Add what is missing.
	if "encoder.embed_tokens.weight" not in state_dict:
		A__				:       Union[str, Any]	  =						state_dict["""shared.weight"""]

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			A__				:       Any	  =						state_dict["""shared.weight"""]

		if "lm_head.weight" not in state_dict:  # For old 1.0 models.
			print("""Using shared word embeddings as lm_head."""   )
			A__				:       List[Any]	  =						state_dict["""shared.weight"""]

	return state_dict

def 		UpperCamelCase   (lowercase_:					Union[str, Any]       , lowercase_:					Dict       , lowercase_:					Tuple       , lowercase_:					int       , lowercase_:					Any   )	-> Tuple:
	A__				:       Optional[int]	  =						checkpoints.load_tax_checkpoint(lowercase_   )
	A__				:       int	  =						convert_tax_to_pytorch(
	    lowercase_       , num_layers=config.num_layers       , is_encoder_only=lowercase_       , scalable_attention=lowercase_   )
	A__				:       Dict	  =						make_state_dict(lowercase_       , lowercase_   )
	model.load_state_dict(lowercase_       , strict=lowercase_   )

def 		UpperCamelCase   (lowercase_:					Optional[Any]       , lowercase_:					Tuple       , lowercase_:					int       , lowercase_:					bool = False       , lowercase_:					bool = False       , )	-> Dict:
	A__				:       Dict	  =						MTaConfig.from_json_file(lowercase_   )
	print(f"""Building PyTorch model from configuration: {config}"""   )
	# Non-v1.1 checkpoints could also use T5Model, but this works for all.
	# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
	if is_encoder_only:
		A__				:       Any	  =						UMTaEncoderModel(lowercase_   )
	else:
		A__				:       str	  =						UMTaForConditionalGeneration(lowercase_   )

	# Load weights from tf checkpoint
	load_tax_weights_in_ta(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
	model.save_pretrained(lowercase_   )

	# Verify that we can load the checkpoint.
	model.from_pretrained(lowercase_   )
	print("""Done"""   )


if __name__ == "__main__":
	A_			:					int    			=     argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
	# Required parameters
	parser.add_argument(
	    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
	)
	parser.add_argument(
	    '--config_file',
	    default=None,
	    type=str,
	    required=True,
	    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
	)
	parser.add_argument(
	    '--scalable_attention',
	    action='store_true',
	    help='Whether the model uses scaled attention (umt5 model)',
	    default=False,
	)
	A_			:					Union[str, Any]    			=     parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path,
	    args.config_file,
	    args.pytorch_dump_path,
	    args.is_encoder_only,
	    args.scalable_attention,
	)






import argparse
import copy

def 		UpperCamelCase   (lowercase_:					Any   )	-> Union[str, Any]:
	A__				:       int	  =						{}

	with open(lowercase_   ) as f:
		for line in f:
			if line.split()[0] not in dict_of_neighbours:
				A__				:       int	  =						[]
				_list.append([line.split()[1], line.split()[2]]   )
				A__				:       Any	  =						_list
			else:
				dict_of_neighbours[line.split()[0]].append(
				    [line.split()[1], line.split()[2]]   )
			if line.split()[1] not in dict_of_neighbours:
				A__				:       Optional[int]	  =						[]
				_list.append([line.split()[0], line.split()[2]]   )
				A__				:       Any	  =						_list
			else:
				dict_of_neighbours[line.split()[1]].append(
				    [line.split()[0], line.split()[2]]   )

	return dict_of_neighbours

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					List[str]   )	-> Tuple:

	with open(lowercase_   ) as f:
		A__				:       List[Any]	  =						f.read(1   )
	A__				:       Union[str, Any]	  =						start_node

	A__				:       List[Any]	  =						[]

	A__				:       str	  =						start_node

	A__				:       List[str]	  =						0
	while visiting not in first_solution:
		A__				:       Dict	  =						10000
		for k in dict_of_neighbours[visiting]:
			if int(k[1]   ) < int(lowercase_   ) and k[0] not in first_solution:
				A__				:       Tuple	  =						k[1]
				A__				:       Optional[Any]	  =						k[0]

		first_solution.append(lowercase_   )
		A__				:       Optional[int]	  =						distance_of_first_solution + int(lowercase_   )
		A__				:       str	  =						best_node

	first_solution.append(lowercase_   )

	A__				:       Any	  =						0
	for k in dict_of_neighbours[first_solution[-2]]:
		if k[0] == start_node:
			break
		position += 1

	A__				:       int	  =						(
	    distance_of_first_solution
	    + int(dict_of_neighbours[first_solution[-2]][position][1]   )
	    - 10000
	)
	return first_solution, distance_of_first_solution

def 		UpperCamelCase   (lowercase_:					Any       , lowercase_:					str   )	-> Any:
	A__				:       int	  =						[]

	for n in solution[1:-1]:
		A__				:       Union[str, Any]	  =						solution.index(lowercase_   )
		for kn in solution[1:-1]:
			A__				:       List[Any]	  =						solution.index(lowercase_   )
			if n == kn:
				continue

			A__				:       Union[str, Any]	  =						copy.deepcopy(lowercase_   )
			A__				:       int	  =						kn
			A__				:       Optional[int]	  =						n

			A__				:       Dict	  =						0

			for k in _tmp[:-1]:
				A__				:       Dict	  =						_tmp[_tmp.index(lowercase_   ) + 1]
				for i in dict_of_neighbours[k]:
					if i[0] == next_node:
						A__				:       List[Any]	  =						distance + int(i[1]   )
			_tmp.append(lowercase_   )

			if _tmp not in neighborhood_of_solution:
				neighborhood_of_solution.append(_tmp   )

	A__				:       Optional[Any]	  =						len(neighborhood_of_solution[0]   ) - 1

	neighborhood_of_solution.sort(key=lambda lowercase_   : x[index_of_last_item_in_the_list]   )
	return neighborhood_of_solution

def 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					Tuple       , lowercase_:					Dict       , lowercase_:					Dict       , lowercase_:					Tuple   )	-> Union[str, Any]:
	A__				:       Union[str, Any]	  =						1
	A__				:       List[str]	  =						first_solution
	A__				:       Dict	  =						[]
	A__				:       Union[str, Any]	  =						distance_of_first_solution
	A__				:       Any	  =						solution

	while count <= iters:
		A__				:       Optional[int]	  =						find_neighborhood(lowercase_       , lowercase_   )
		A__				:       Union[str, Any]	  =						0
		A__				:       str	  =						neighborhood[index_of_best_solution]
		A__				:       int	  =						len(lowercase_   ) - 1

		A__				:       List[str]	  =						False
		while not found:
			A__				:       Dict	  =						0
			while i < len(lowercase_   ):
				if best_solution[i] != solution[i]:
					A__				:       Any	  =						best_solution[i]
					A__				:       Optional[int]	  =						solution[i]
					break
				A__				:       Any	  =						i + 1

			if [first_exchange_node, second_exchange_node] not in tabu_list and [
			    second_exchange_node,
			    first_exchange_node,
			] not in tabu_list:
				tabu_list.append([first_exchange_node, second_exchange_node]   )
				A__				:       List[str]	  =						True
				A__				:       List[Any]	  =						best_solution[:-1]
				A__				:       Optional[Any]	  =						neighborhood[index_of_best_solution][best_cost_index]
				if cost < best_cost:
					A__				:       Tuple	  =						cost
					A__				:       int	  =						solution
			else:
				A__				:       List[Any]	  =						index_of_best_solution + 1
				A__				:       Tuple	  =						neighborhood[index_of_best_solution]

		if len(lowercase_   ) >= size:
			tabu_list.pop(0   )

		A__				:       Any	  =						count + 1

	return best_solution_ever, best_cost

def 		UpperCamelCase   (lowercase_:					Union[str, Any]=None   )	-> List[str]:
	A__				:       Union[str, Any]	  =						generate_neighbours(args.File   )

	A__  ,  A__				:       int	  =						generate_first_solution(
	    args.File       , lowercase_   )

	A__  ,  A__				:       str	  =						tabu_search(
	    lowercase_       , lowercase_       , lowercase_       , args.Iterations       , args.Size       , )

	print(f"""Best solution: {best_sol}, with total distance: {best_cost}."""   )


if __name__ == "__main__":
	A_			:					str    			=     argparse.ArgumentParser(description='Tabu Search')
	parser.add_argument(
	    '-f',
	    '--File',
	    type=str,
	    help='Path to the file containing the data',
	    required=True,
	)
	parser.add_argument(
	    '-i',
	    '--Iterations',
	    type=int,
	    help='How many iterations the algorithm should perform',
	    required=True,
	)
	parser.add_argument(
	    '-s', '--Size', type=int, help='Size of the tabu list', required=True
	)

	# Pass the arguments to main method
	main(parser.parse_args())



