
"""simple docstring"""

import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_A						=			1_6
_A						=			3_2
def 		UpperCAmelCase (		a_,						a_ = 16,						a_ = "bert-base-cased"      ) ->    Tuple:





		'''simple docstring'''


		lowerCamelCase					:      Optional[int]          =   AutoTokenizer.from_pretrained(_a      )
		lowerCamelCase					:      List[Any]          =   load_dataset('glue',						'mrpc'      )

		def tokenize_function(a_      ):
				# max_length=None => use the model max length (it's actually the default)
				lowerCamelCase					:      int          =   tokenizer(examples['sentence1'],						examples['sentence2'],						truncation=_a,						max_length=_a      )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		lowerCamelCase					:      Optional[Any]          =   datasets.map(
		    _a,						batched=_a,						remove_columns=['idx', 'sentence1', 'sentence2'],						load_from_cache_file=_a      )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowerCamelCase					:      List[str]          =   tokenized_datasets.rename_column('label',						'labels'      )

		def collate_fn(a_      ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(_a,						padding='max_length',						max_length=128,						return_tensors='pt'      )
				return tokenizer.pad(_a,						padding='longest',						return_tensors='pt'      )

		# Instantiate dataloaders.
		lowerCamelCase					:      Optional[Any]          =   DataLoader(
		    tokenized_datasets['train'],						shuffle=_a,						collate_fn=_a,						batch_size=_a      )
		lowerCamelCase					:      List[str]          =   DataLoader(
		    tokenized_datasets['validation'],						shuffle=_a,						collate_fn=_a,						batch_size=_a      )

		return train_dataloader, eval_dataloader
def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ) ->    Optional[int]:





		'''simple docstring'''


		model.eval()
		lowerCamelCase					:      List[Any]          =   0
		for step, batch in enumerate(_a      ):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device      )
				with torch.no_grad():
						lowerCamelCase					:      Optional[int]          =   model(**_a      )
				lowerCamelCase					:      str          =   outputs.logits.argmax(dim=-1      )
				# It is slightly faster to call this once, than multiple times
				lowerCamelCase     ,   lowerCamelCase					:      int          =   accelerator.gather(
				    (predictions, batch['labels'])      )  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(_a      ) - 1:
								lowerCamelCase					:      str          =   predictions[: len(eval_dataloader.dataset      ) - samples_seen]
								lowerCamelCase					:      Tuple          =   references[: len(eval_dataloader.dataset      ) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=_a,						references=_a,						)

		lowerCamelCase					:      Optional[Any]          =   metric.compute()
		return eval_metric["accuracy"]
def 		UpperCAmelCase (		a_,						a_      ) ->    Any:





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCamelCase					:      Any          =   config['lr']
		lowerCamelCase					:      Union[str, Any]          =   int(config['num_epochs']      )
		lowerCamelCase					:      str          =   int(config['seed']      )
		lowerCamelCase					:      int          =   int(config['batch_size']      )
		lowerCamelCase					:      Tuple          =   args.model_name_or_path

		set_seed(_a      )
		lowerCamelCase     ,   lowerCamelCase					:      Dict          =   get_dataloaders(_a,						_a,						_a      )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCamelCase					:      Tuple          =   AutoModelForSequenceClassification.from_pretrained(_a,						return_dict=_a      )

		# Instantiate optimizer
		lowerCamelCase					:      Optional[Any]          =   (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowerCamelCase					:      Union[str, Any]          =   optimizer_cls(params=model.parameters(),						lr=_a      )

		if accelerator.state.deepspeed_plugin is not None:
				lowerCamelCase					:      List[Any]          =   accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				lowerCamelCase					:      Union[str, Any]          =   1
		lowerCamelCase					:      Optional[int]          =   (len(_a      ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				lowerCamelCase					:      List[str]          =   get_linear_schedule_with_warmup(
				    optimizer=_a,						num_warmup_steps=0,						num_training_steps=_a,						)
		else:
				lowerCamelCase					:      Union[str, Any]          =   DummyScheduler(_a,						total_num_steps=_a,						warmup_num_steps=0      )

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      Union[str, Any]          =   accelerator.prepare(
		    _a,						_a,						_a,						_a,						_a      )

		# We need to keep track of how many total steps we have iterated over
		lowerCamelCase					:      Optional[int]          =   0
		# We also need to keep track of the stating epoch so files are named properly
		lowerCamelCase					:      Dict          =   0
		lowerCamelCase					:      Optional[Any]          =   evaluate.load('glue',						'mrpc'      )
		lowerCamelCase					:      int          =   num_epochs

		if args.partial_train_epoch is not None:
				lowerCamelCase					:      int          =   args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint      )
				lowerCamelCase					:      Union[str, Any]          =   args.resume_from_checkpoint.split('epoch_'      )[1]
				lowerCamelCase					:      Optional[int]          =   ''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				lowerCamelCase					:      Optional[Any]          =   int(_a      ) + 1
				lowerCamelCase					:      Any          =   evaluation_loop(_a,						_a,						_a,						_a      )
				accelerator.print('resumed checkpoint performance:',						_a      )
				accelerator.print('resumed checkpoint\'s scheduler\'s lr:',						lr_scheduler.get_lr()[0]      )
				accelerator.print('resumed optimizers\'s lr:',						optimizer.param_groups[0]['lr']      )
				with open(os.path.join(args.output_dir,						F"""state_{starting_epoch-1}.json"""      ),						'r'      ) as f:
						lowerCamelCase					:      List[Any]          =   json.load(_a      )
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		lowerCamelCase					:      Optional[int]          =   {}
		for epoch in range(_a,						_a      ):
				model.train()
				for step, batch in enumerate(_a      ):
						lowerCamelCase					:      Optional[Any]          =   model(**_a      )
						lowerCamelCase					:      str          =   outputs.loss
						lowerCamelCase					:      Optional[Any]          =   loss / gradient_accumulation_steps
						accelerator.backward(_a      )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				lowerCamelCase					:      List[Any]          =   F"""epoch_{epoch}"""
				lowerCamelCase					:      Tuple          =   os.path.join(args.output_dir,						_a      )
				accelerator.save_state(_a      )
				lowerCamelCase					:      Union[str, Any]          =   evaluation_loop(_a,						_a,						_a,						_a      )
				lowerCamelCase					:      Tuple          =   accuracy
				lowerCamelCase					:      List[Any]          =   lr_scheduler.get_lr()[0]
				lowerCamelCase					:      Tuple          =   optimizer.param_groups[0]['lr']
				lowerCamelCase					:      str          =   epoch
				lowerCamelCase					:      Union[str, Any]          =   overall_step
				accelerator.print(F"""epoch {epoch}:""",						_a      )

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir,						F"""state_{epoch}.json"""      ),						'w'      ) as f:
								json.dump(_a,						_a      )
def 		UpperCAmelCase (		) ->    Optional[Any]:





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'      )
		parser.add_argument(
		    '--model_name_or_path',						type=_a,						default='bert-base-cased',						help='Path to pretrained model or model identifier from huggingface.co/models.',						required=_a,						)
		parser.add_argument(
		    '--output_dir',						type=_a,						default='.',						help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',						)
		parser.add_argument(
		    '--resume_from_checkpoint',						type=_a,						default=_a,						help='If the training should continue from a checkpoint folder.',						)
		parser.add_argument(
		    '--partial_train_epoch',						type=_a,						default=_a,						help='If passed, the training will stop after this number of epochs.',						)
		parser.add_argument(
		    '--num_epochs',						type=_a,						default=2,						help='Number of train epochs.',						)
		lowerCamelCase					:      Union[str, Any]          =   parser.parse_args()
		lowerCamelCase					:      str          =   {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

		training_function(_a,						_a      )


if __name__ == "__main__":
					main()








"""simple docstring"""

from __future__ import annotations

_A						=			[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def 		UpperCAmelCase (		a_,						a_,						a_,						a_,						a_,						):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the reference grid
		lowerCamelCase					:      Union[str, Any]          =   1
		lowerCamelCase					:      Any          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the action grid

		lowerCamelCase					:      List[str]          =   init[0]
		lowerCamelCase					:      Optional[Any]          =   init[1]
		lowerCamelCase					:      List[Any]          =   0
		lowerCamelCase					:      List[str]          =   g + heuristic[x][y]  # cost from starting cell to destination cell
		lowerCamelCase					:      Union[str, Any]          =   [[f, g, x, y]]

		lowerCamelCase					:      Union[str, Any]          =   False  # flag that is set when search is complete
		lowerCamelCase					:      str          =   False  # flag set if we can't find expand

		while not found and not resign:
				if len(a_      ) == 0:
						raise ValueError('Algorithm is unable to find solution'      )
				else:  # to choose the least costliest action so as to move closer to the goal
						cell.sort()
						cell.reverse()
						lowerCamelCase					:      int          =   cell.pop()
						lowerCamelCase					:      str          =   next_cell[2]
						lowerCamelCase					:      Union[str, Any]          =   next_cell[3]
						lowerCamelCase					:      List[str]          =   next_cell[1]

						if x == goal[0] and y == goal[1]:
								lowerCamelCase					:      Any          =   True
						else:
								for i in range(len(a_      )      ):  # to try out different valid actions
										lowerCamelCase					:      Tuple          =   x + DIRECTIONS[i][0]
										lowerCamelCase					:      Union[str, Any]          =   y + DIRECTIONS[i][1]
										if xa >= 0 and xa < len(a_      ) and ya >= 0 and ya < len(grid[0]      ):
												if closed[xa][ya] == 0 and grid[xa][ya] == 0:
														lowerCamelCase					:      str          =   g + cost
														lowerCamelCase					:      Tuple          =   ga + heuristic[xa][ya]
														cell.append([fa, ga, xa, ya]      )
														lowerCamelCase					:      Union[str, Any]          =   1
														lowerCamelCase					:      Any          =   i
		lowerCamelCase					:      Any          =   []
		lowerCamelCase					:      Optional[int]          =   goal[0]
		lowerCamelCase					:      Dict          =   goal[1]
		invpath.append([x, y]      )  # we get the reverse path from here
		while x != init[0] or y != init[1]:
				lowerCamelCase					:      Dict          =   x - DIRECTIONS[action[x][y]][0]
				lowerCamelCase					:      Dict          =   y - DIRECTIONS[action[x][y]][1]
				lowerCamelCase					:      Optional[Any]          =   xa
				lowerCamelCase					:      Union[str, Any]          =   ya
				invpath.append([x, y]      )

		lowerCamelCase					:      Optional[int]          =   []
		for i in range(len(a_      )      ):
				path.append(invpath[len(a_      ) - 1 - i]      )
		return path, action


if __name__ == "__main__":
					_A						=			[
					    [0, 1, 0, 0, 0, 0],
					    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
					    [0, 1, 0, 0, 0, 0],
					    [0, 1, 0, 0, 1, 0],
					    [0, 0, 0, 0, 1, 0],
					]

					_A						=			[0, 0]
					# all coordinates are given in format [y,x]
					_A						=			[len(grid) - 1, len(grid[0]) - 1]
					_A						=			1

					# the cost map which pushes the path closer to the goal
					_A						=			[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
					for i in range(len(grid)):
										for j in range(len(grid[0])):
															_A						=			abs(i - goal[0]) + abs(j - goal[1])
															if grid[i][j] == 1:
																				# added extra penalty in the heuristic map
																				_A						=			9_9

					_A					,  _A						=			search(grid, init, goal, cost, heuristic)

					print('ACTION MAP')
					for i in range(len(action)):
										print(action[i])

					for i in range(len(path)):
										print(path[i])






