



import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_snake_case		=  logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_snake_case		=  {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_snake_case		=  {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_snake_case		=  sorted(arg_to_scheduler.keys())
_snake_case		=  "{" + ", ".join(arg_to_scheduler_choices) + "}"



class   UpperCAmelCase_    (       pl.LightningModule):







							def __init__(   self,			__a,			__a=None,			__a="base",			__a=None,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__()
														# TODO: move to self.save_hyperparameters()
														# self.save_hyperparameters()
														# can also expand arguments into trainer signature for easier reading

														self.save_hyperparameters(__a)
														_lowerCAmelCase     :							str    =    0
														_lowerCAmelCase     :							Optional[int]    =    Path(self.hparams.output_dir)
														_lowerCAmelCase     :							Any    =    self.hparams.cache_dir if self.hparams.cache_dir else None
														if config is None:
																					_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.from_pretrained(
																					    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,			**({"num_labels": num_labels} if num_labels is not None else {}),			cache_dir=__a,			**__a,			)
														else:
																					_lowerCAmelCase     :							PretrainedConfig    =    config

														_lowerCAmelCase     :							Dict    =    ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
														for p in extra_model_params:
																					if getattr(self.hparams,			__a,			__a):
																												assert hasattr(self.config,			__a), f"model config doesn't have a `{p}` attribute"
																												setattr(self.config,			__a,			getattr(self.hparams,			__a))

														if tokenizer is None:
																					_lowerCAmelCase     :							str    =    AutoTokenizer.from_pretrained(
																					    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,			cache_dir=__a,			)
														else:
																					_lowerCAmelCase     :							PreTrainedTokenizer    =    tokenizer
														_lowerCAmelCase     :							Optional[int]    =    MODEL_MODES[mode]
														if model is None:
																					_lowerCAmelCase     :							Dict    =    self.model_type.from_pretrained(
																					    self.hparams.model_name_or_path,			from_tf=bool(".ckpt" in self.hparams.model_name_or_path),			config=self.config,			cache_dir=__a,			)
														else:
																					_lowerCAmelCase     :							str    =    model







							def    snake_case__      (   self,			*__a,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    self.model_type.from_pretrained(*__a,			**__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    arg_to_scheduler[self.hparams.lr_scheduler]
														_lowerCAmelCase     :							Any    =    get_schedule_func(
														    self.opt,			num_warmup_steps=self.hparams.warmup_steps,			num_training_steps=self.total_steps())
														_lowerCAmelCase     :							str    =    {"scheduler": scheduler, "interval": "step", "frequency": 1}
														return scheduler







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.model
														_lowerCAmelCase     :							Any    =    ["bias", "LayerNorm.weight"]
														_lowerCAmelCase     :							List[str]    =    [
														    {
														        "params": [
														            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)
														        ],  # check this named paramters
														        "weight_decay": self.hparams.weight_decay,
														    },
														    {
														        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
														        "weight_decay": 0.0,
														    },
														]
														if self.hparams.adafactor:
																					_lowerCAmelCase     :							Any    =    Adafactor(
																					    __a,			lr=self.hparams.learning_rate,			scale_parameter=__a,			relative_step=__a)

														else:
																					_lowerCAmelCase     :							List[str]    =    AdamW(
																					    __a,			lr=self.hparams.learning_rate,			eps=self.hparams.adam_epsilon)
														_lowerCAmelCase     :							Optional[Any]    =    optimizer

														_lowerCAmelCase     :							Optional[Any]    =    self.get_lr_scheduler()

														return [optimizer], [scheduler]







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														return self.validation_step(__a,			__a)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return self.validation_end(__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    max(1,			self.hparams.gpus)  # TODO: consider num_tpu_cores
														_lowerCAmelCase     :							Optional[int]    =    self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
														return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														if stage == "test":
																					_lowerCAmelCase     :							Dict    =    len(self.test_dataloader().dataset)
														else:
																					_lowerCAmelCase     :							List[str]    =    self.get_dataloader("train",			self.hparams.train_batch_size,			shuffle=__a)
																					_lowerCAmelCase     :							int    =    len(self.train_dataloader().dataset)







							def    snake_case__      (   self,			__a,			__a,			__a = False):

														'''simple docstring'''

														raise NotImplementedError("You must implement this for your task")







							def    snake_case__      (   self):

														'''simple docstring'''

														return self.train_loader







							def    snake_case__      (   self):

														'''simple docstring'''

														return self.get_dataloader("dev",			self.hparams.eval_batch_size,			shuffle=__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														return self.get_dataloader("test",			self.hparams.eval_batch_size,			shuffle=__a)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return os.path.join(
														    self.hparams.data_dir,			"cached_{}_{}_{}".format(
														        __a,			list(filter(__a,			self.hparams.model_name_or_path.split("/"))).pop(),			str(self.hparams.max_seq_length),			),			)







							@pl.utilities.rank_zero_only
							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    self.output_dir.joinpath("best_tfmr")
														_lowerCAmelCase     :							Optional[int]    =    self.step_count
														self.model.save_pretrained(__a)
														self.tokenizer.save_pretrained(__a)







							@staticmethod
							def    snake_case__      (   __a,			__a):

														'''simple docstring'''

														parser.add_argument(
														    "--model_name_or_path",			default=__a,			type=__a,			required=__a,			help="Path to pretrained model or model identifier from huggingface.co/models",			)
														parser.add_argument(
														    "--config_name",			default="",			type=__a,			help="Pretrained config name or path if not the same as model_name")
														parser.add_argument(
														    "--tokenizer_name",			default=__a,			type=__a,			help="Pretrained tokenizer name or path if not the same as model_name",			)
														parser.add_argument(
														    "--cache_dir",			default=str(Path(__a).parent / "test_run" / "cache"),			type=__a,			help="Where do you want to store the pre-trained models downloaded from huggingface.co",			)
														parser.add_argument(
														    "--encoder_layerdrop",			type=__a,			help="Encoder layer dropout probability (Optional). Goes into model.config",			)
														parser.add_argument(
														    "--decoder_layerdrop",			type=__a,			help="Decoder layer dropout probability (Optional). Goes into model.config",			)
														parser.add_argument(
														    "--dropout",			type=__a,			help="Dropout probability (Optional). Goes into model.config",			)
														parser.add_argument(
														    "--attention_dropout",			type=__a,			help="Attention dropout probability (Optional). Goes into model.config",			)
														parser.add_argument("--learning_rate",			default=5E-5,			type=__a,			help="The initial learning rate for Adam.")
														parser.add_argument(
														    "--lr_scheduler",			default="linear",			choices=__a,			metavar=__a,			type=__a,			help="Learning rate scheduler",			)
														parser.add_argument("--weight_decay",			default=0.0,			type=__a,			help="Weight decay if we apply some.")
														parser.add_argument("--adam_epsilon",			default=1E-8,			type=__a,			help="Epsilon for Adam optimizer.")
														parser.add_argument("--warmup_steps",			default=0,			type=__a,			help="Linear warmup over warmup_steps.")
														parser.add_argument("--num_workers",			default=4,			type=__a,			help="kwarg passed to DataLoader")
														parser.add_argument("--num_train_epochs",			dest="max_epochs",			default=3,			type=__a)
														parser.add_argument("--train_batch_size",			default=32,			type=__a)
														parser.add_argument("--eval_batch_size",			default=32,			type=__a)
														parser.add_argument("--adafactor",			action="store_true")



class   UpperCAmelCase_    (       pl.Callback):







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														if (
														    trainer.is_global_zero and trainer.global_rank == 0
														):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
																					pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.



class   UpperCAmelCase_    (       pl.Callback):







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														for name, param in pl_module.model.rag.named_parameters():
																					if param.grad is None:
																												print(__a)



class   UpperCAmelCase_    (       pl.Callback):







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    trainer.lr_schedulers[0]["scheduler"]
														_lowerCAmelCase     :							int    =    {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
														pl_module.logger.log_metrics(__a)







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														rank_zero_info("***** Validation results *****")
														_lowerCAmelCase     :							Dict    =    trainer.callback_metrics
														# Log results
														for key in sorted(__a):
																					if key not in ["log", "progress_bar"]:
																												rank_zero_info("{} = {}\n".format(__a,			str(metrics[key])))







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														rank_zero_info("***** Test results *****")
														_lowerCAmelCase     :							Tuple    =    trainer.callback_metrics
														# Log and save results to file
														_lowerCAmelCase     :							Any    =    os.path.join(pl_module.hparams.output_dir,			"test_results.txt")
														with open(__a,			"w") as writer:
																					for key in sorted(__a):
																												if key not in ["log", "progress_bar"]:
																																			rank_zero_info("{} = {}\n".format(__a,			str(metrics[key])))
																																			writer.write("{} = {}\n".format(__a,			str(metrics[key])))






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							parser.add_argument(
							    "--output_dir"				, default=str(Path(_lowerCamelCase  ).parent / "test_run" / "model_checkpoints"  )				, type=_lowerCamelCase				, help="The output directory where the model predictions and checkpoints will be written."				, )
							parser.add_argument(
							    "--fp16"				, action="store_true"				, help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"				, )

							parser.add_argument(
							    "--fp16_opt_level"				, type=_lowerCamelCase				, default="O2"				, help=(
							        "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
							        "See details at https://nvidia.github.io/apex/amp.html"
							    )				, )
							parser.add_argument("--n_tpu_cores"				, dest="tpu_cores"				, type=_lowerCamelCase  )
							parser.add_argument("--max_grad_norm"				, dest="gradient_clip_val"				, default=1.0				, type=_lowerCamelCase				, help="Max gradient norm"  )
							parser.add_argument("--do_train"				, action="store_true"				, help="Whether to run training."  )
							parser.add_argument("--do_predict"				, action="store_true"				, help="Whether to run predictions on the test set."  )
							parser.add_argument(
							    "--gradient_accumulation_steps"				, dest="accumulate_grad_batches"				, type=_lowerCamelCase				, default=1				, help="Number of updates steps to accumulate before performing a backward/update pass."				, )
							parser.add_argument("--seed"				, type=_lowerCamelCase				, default=42				, help="random seed for initialization"  )
							parser.add_argument(
							    "--data_dir"				, default=str(Path(_lowerCamelCase  ).parent / "test_run" / "dummy-train-data"  )				, type=_lowerCamelCase				, help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."				, )






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=None				, _lowerCamelCase=True				, _lowerCamelCase=[]				, _lowerCamelCase=None				, _lowerCamelCase=None				, **_lowerCamelCase				, ):





							'''simple docstring'''




							pl.seed_everything(args.seed  )

							# init model
							_lowerCAmelCase     :							List[str]    =    Path(model.hparams.output_dir  )
							odir.mkdir(exist_ok=_lowerCamelCase  )

							# add custom checkpoints
							if checkpoint_callback is None:
														_lowerCAmelCase     :							List[str]    =    pl.callbacks.ModelCheckpoint(
														    filepath=args.output_dir				, prefix="checkpoint"				, monitor="val_loss"				, mode="min"				, save_top_k=1  )
							if early_stopping_callback:
														extra_callbacks.append(_lowerCamelCase  )
							if logging_callback is None:
														_lowerCAmelCase     :							int    =    LoggingCallback()

							_lowerCAmelCase     :							Optional[int]    =    {}

							if args.fpaa:
														_lowerCAmelCase     :							str    =    16

							if args.gpus > 1:
														_lowerCAmelCase     :							Any    =    "auto"
														_lowerCAmelCase     :							Optional[Any]    =    "ddp"

							_lowerCAmelCase     :							Optional[int]    =    args.accumulate_grad_batches
							_lowerCAmelCase     :							int    =    None
							_lowerCAmelCase     :							Optional[Any]    =    "auto"

							_lowerCAmelCase     :							int    =    pl.Trainer.from_argparse_args(
							    _lowerCamelCase				, weights_summary=_lowerCamelCase				, callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]				, logger=_lowerCamelCase				, val_check_interval=1				, num_sanity_val_steps=2				, **_lowerCamelCase				, )

							if args.do_train:
														trainer.fit(_lowerCamelCase  )

							else:
														print("RAG modeling tests with new set functions successfuly executed!"  )
							return trainer





import argparse
import copy






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    {}

							with open(_lowerCamelCase  ) as f:
														for line in f:
																					if line.split()[0] not in dict_of_neighbours:
																												_lowerCAmelCase     :							Tuple    =    []
																												_list.append([line.split()[1], line.split()[2]]  )
																												_lowerCAmelCase     :							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:
																												_lowerCAmelCase     :							str    =    []
																												_list.append([line.split()[0], line.split()[2]]  )
																												_lowerCAmelCase     :							Any    =    _list
																					else:
																												dict_of_neighbours[line.split()[1]].append(
																												    [line.split()[0], line.split()[2]]  )

							return dict_of_neighbours






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							with open(_lowerCamelCase  ) as f:
														_lowerCAmelCase     :							str    =    f.read(1  )
							_lowerCAmelCase     :							str    =    start_node

							_lowerCAmelCase     :							List[str]    =    []

							_lowerCAmelCase     :							Any    =    start_node

							_lowerCAmelCase     :							str    =    0
							while visiting not in first_solution:
														_lowerCAmelCase     :							Dict    =    10_000
														for k in dict_of_neighbours[visiting]:
																					if int(k[1]  ) < int(_lowerCamelCase  ) and k[0] not in first_solution:
																												_lowerCAmelCase     :							List[str]    =    k[1]
																												_lowerCAmelCase     :							List[Any]    =    k[0]

														first_solution.append(_lowerCamelCase  )
														_lowerCAmelCase     :							Optional[int]    =    distance_of_first_solution + int(_lowerCamelCase  )
														_lowerCAmelCase     :							str    =    best_node

							first_solution.append(_lowerCamelCase  )

							_lowerCAmelCase     :							Union[str, Any]    =    0
							for k in dict_of_neighbours[first_solution[-2]]:
														if k[0] == start_node:
																					break
														position += 1

							_lowerCAmelCase     :							Tuple    =    (
							    distance_of_first_solution
							    + int(dict_of_neighbours[first_solution[-2]][position][1]  )
							    - 10_000
							)
							return first_solution, distance_of_first_solution






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    []

							for n in solution[1:-1]:
														_lowerCAmelCase     :							Dict    =    solution.index(_lowerCamelCase  )
														for kn in solution[1:-1]:
																					_lowerCAmelCase     :							Dict    =    solution.index(_lowerCamelCase  )
																					if n == kn:
																												continue

																					_lowerCAmelCase     :							Optional[int]    =    copy.deepcopy(_lowerCamelCase  )
																					_lowerCAmelCase     :							int    =    kn
																					_lowerCAmelCase     :							Dict    =    n

																					_lowerCAmelCase     :							Optional[int]    =    0

																					for k in _tmp[:-1]:
																												_lowerCAmelCase     :							str    =    _tmp[_tmp.index(_lowerCamelCase  ) + 1]
																												for i in dict_of_neighbours[k]:
																																			if i[0] == next_node:
																																										_lowerCAmelCase     :							Optional[Any]    =    distance + int(i[1]  )
																					_tmp.append(_lowerCamelCase  )

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

							_lowerCAmelCase     :							List[Any]    =    len(neighborhood_of_solution[0]  ) - 1

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






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    1
							_lowerCAmelCase     :							int    =    first_solution
							_lowerCAmelCase     :							Tuple    =    []
							_lowerCAmelCase     :							Tuple    =    distance_of_first_solution
							_lowerCAmelCase     :							Optional[int]    =    solution

							while count <= iters:
														_lowerCAmelCase     :							int    =    find_neighborhood(_lowerCamelCase				, _lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    0
														_lowerCAmelCase     :							Dict    =    neighborhood[index_of_best_solution]
														_lowerCAmelCase     :							int    =    len(_lowerCamelCase  ) - 1

														_lowerCAmelCase     :							Union[str, Any]    =    False
														while not found:
																					_lowerCAmelCase     :							Tuple    =    0
																					while i < len(_lowerCamelCase  ):
																												if best_solution[i] != solution[i]:
																																			_lowerCAmelCase     :							str    =    best_solution[i]
																																			_lowerCAmelCase     :							Tuple    =    solution[i]
																																			break
																												_lowerCAmelCase     :							int    =    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]  )
																												_lowerCAmelCase     :							Optional[int]    =    True
																												_lowerCAmelCase     :							Optional[Any]    =    best_solution[:-1]
																												_lowerCAmelCase     :							Tuple    =    neighborhood[index_of_best_solution][best_cost_index]
																												if cost < best_cost:
																																			_lowerCAmelCase     :							Union[str, Any]    =    cost
																																			_lowerCAmelCase     :							List[Any]    =    solution
																					else:
																												_lowerCAmelCase     :							Optional[Any]    =    index_of_best_solution + 1
																												_lowerCAmelCase     :							Optional[Any]    =    neighborhood[index_of_best_solution]

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

														_lowerCAmelCase     :							int    =    count + 1

							return best_solution_ever, best_cost






def 				A				(					_lowerCamelCase=None  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    generate_neighbours(args.File  )

							_lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    generate_first_solution(
							    args.File				, _lowerCamelCase  )

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    tabu_search(
							    _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, args.Iterations				, args.Size				, )

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


if __name__ == "__main__":
						_snake_case		=  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())
