






import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a__       =     """true"""

def _UpperCAmelCase   (	a						:	Union[str, Any]  ,					a						:	List[Any]=82  ,					a						:	str=16 ):
							set_seed(42 )
							snake_case__	  =       RegressionModel()
							snake_case__	  =       deepcopy(a )
							snake_case__	  =       RegressionDataset(length=a )
							snake_case__	  =       DataLoader(a  ,					batch_size=a )
							model.to(accelerator.device )
							snake_case__  , snake_case__	  =       accelerator.prepare(a  ,					a )
							return model, ddp_model, dataloader

def _UpperCAmelCase   (	a						:	Accelerator  ,					a						:	int=False ):
							snake_case__	  =       AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
							snake_case__	  =       load_dataset("""glue"""  ,					"""mrpc"""  ,					split="""validation""" )

							def tokenize_function(a						:	List[Any] ):
														snake_case__	  =       tokenizer(examples["""sentence1"""]  ,					examples["""sentence2"""]  ,					truncation=a  ,					max_length=a )
														return outputs

							with accelerator.main_process_first():
														snake_case__	  =       dataset.map(
														    a  ,					batched=a  ,					remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,					)

							snake_case__	  =       tokenized_datasets.rename_column("""label"""  ,					"""labels""" )

							def collate_fn(a						:	str ):
														if use_longest:
																					return tokenizer.pad(a  ,					padding="""longest"""  ,					return_tensors="""pt""" )
														return tokenizer.pad(a  ,					padding="""max_length"""  ,					max_length=128  ,					return_tensors="""pt""" )

							return DataLoader(a  ,					shuffle=a  ,					collate_fn=a  ,					batch_size=16 )

def _UpperCAmelCase   (	a						:	List[str]  ,					a						:	Tuple ):
							snake_case__	  =       Accelerator(dispatch_batches=a  ,					split_batches=a )
							snake_case__	  =       get_dataloader(a  ,					not dispatch_batches )
							snake_case__	  =       AutoModelForSequenceClassification.from_pretrained(
							    """hf-internal-testing/mrpc-bert-base-cased"""  ,					return_dict=a )
							snake_case__  , snake_case__	  =       accelerator.prepare(a  ,					a )
							return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Optional[int]  ,					a						:	List[Any] ):
							snake_case__	  =       []
							for batch in dataloader:
														snake_case__  , snake_case__	  =       batch.values()
														with torch.no_grad():
																					snake_case__	  =       model(a )
																					snake_case__  , snake_case__	  =       accelerator.gather_for_metrics((logit, target) )
																					logits_and_targets.append((logit, target) )
							snake_case__  , snake_case__	  =       [], []
							for logit, targ in logits_and_targets:
														logits.append(a )
														targs.append(a )
							snake_case__  , snake_case__	  =       torch.cat(a ), torch.cat(a )
							return logits, targs

def _UpperCAmelCase   (	a						:	Accelerator  ,					a						:	List[str]=82  ,					a						:	List[str]=False  ,					a						:	int=False  ,					a						:	Tuple=16 ):
							snake_case__  , snake_case__  , snake_case__	  =       get_basic_setup(a  ,					a  ,					a )
							snake_case__  , snake_case__	  =       generate_predictions(a  ,					a  ,					a )
							assert (
							    len(a ) == num_samples
							), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(a )}'''

def _UpperCAmelCase   (	a						:	bool = False  ,					a						:	bool = False ):
							snake_case__	  =       evaluate.load("""glue"""  ,					"""mrpc""" )
							snake_case__  , snake_case__	  =       get_mrpc_setup(a  ,					a )
							# First do baseline
							snake_case__  , snake_case__  , snake_case__	  =       setup["""no"""]
							model.to(a )
							model.eval()
							for batch in dataloader:
														batch.to(a )
														with torch.inference_mode():
																					snake_case__	  =       model(**a )
														snake_case__	  =       outputs.logits.argmax(dim=-1 )
														metric.add_batch(predictions=a  ,					references=batch["""labels"""] )
							snake_case__	  =       metric.compute()

							# Then do distributed
							snake_case__  , snake_case__  , snake_case__	  =       setup["""ddp"""]
							model.eval()
							for batch in dataloader:
														with torch.inference_mode():
																					snake_case__	  =       model(**a )
														snake_case__	  =       outputs.logits.argmax(dim=-1 )
														snake_case__	  =       batch["""labels"""]
														snake_case__  , snake_case__	  =       accelerator.gather_for_metrics((preds, references) )
														metric.add_batch(predictions=a  ,					references=a )
							snake_case__	  =       metric.compute()

							for key in "accuracy f1".split():
														assert math.isclose(
														    baseline[key]  ,					distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''

def _UpperCAmelCase   (	):
							snake_case__	  =       Accelerator(split_batches=a  ,					dispatch_batches=a )
							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_warning()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							# These are a bit slower so they should only be ran on the GPU or TPU
							if torch.cuda.is_available() or is_tpu_available():
														if accelerator.is_local_main_process:
																					print("""**Testing gather_for_metrics**""" )
														for split_batches in [True, False]:
																					for dispatch_batches in [True, False]:
																												if accelerator.is_local_main_process:
																																			print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
																												test_mrpc(a  ,					a )
																												accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print("""**Test torch metrics**""" )
							for split_batches in [True, False]:
														for dispatch_batches in [True, False]:
																					snake_case__	  =       Accelerator(split_batches=a  ,					dispatch_batches=a )
																					if accelerator.is_local_main_process:
																												print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
																					test_torch_metrics(a  ,					99 )
																					accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print("""**Test last batch is not dropped when perfectly divisible**""" )
							snake_case__	  =       Accelerator()
							test_torch_metrics(a  ,					512 )
							accelerator.state._reset_state()

def _UpperCAmelCase   (	a						:	int ):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
	main()








from __future__ import annotations

import time

a__       =     list[tuple[int, int]]

a__       =     [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a__       =     [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    Dict			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    Node | None):






											'''simple docstring'''







											snake_case__	  =       pos_x
											snake_case__	  =       pos_y
											snake_case__	  =       (pos_y, pos_x)
											snake_case__	  =       goal_x
											snake_case__	  =       goal_y
											snake_case__	  =       parent


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    Optional[int]			,		UpperCamelCase__    :    tuple[int, int]			,		UpperCamelCase__    :    tuple[int, int]):






											'''simple docstring'''







											snake_case__	  =       Node(start[1]			,		start[0]			,		goal[1]			,		goal[0]			,		UpperCamelCase__)
											snake_case__	  =       Node(goal[1]			,		goal[0]			,		goal[1]			,		goal[0]			,		UpperCamelCase__)

											snake_case__	  =       [self.start]
											snake_case__	  =       False



				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											while self.node_queue:
																		snake_case__	  =       self.node_queue.pop(0)

																		if current_node.pos == self.target.pos:
																									snake_case__	  =       True
																									return self.retrace_path(UpperCamelCase__)

																		snake_case__	  =       self.get_successors(UpperCamelCase__)

																		for node in successors:
																									self.node_queue.append(UpperCamelCase__)

											if not self.reached:
																		return [self.start.pos]
											return None



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    Node):






											'''simple docstring'''







											snake_case__	  =       []
											for action in delta:
																		snake_case__	  =       parent.pos_x + action[1]
																		snake_case__	  =       parent.pos_y + action[0]
																		if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(UpperCamelCase__) - 1):
																									continue

																		if grid[pos_y][pos_x] != 0:
																									continue

																		successors.append(
																		    Node(UpperCamelCase__			,		UpperCamelCase__			,		self.target.pos_y			,		self.target.pos_x			,		UpperCamelCase__))
											return successors



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Node | None):






											'''simple docstring'''







											snake_case__	  =       node
											snake_case__	  =       []
											while current_node is not None:
																		path.append((current_node.pos_y, current_node.pos_x))
																		snake_case__	  =       current_node.parent
											path.reverse()
											return path


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    str			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Any):






											'''simple docstring'''







											snake_case__	  =       BreadthFirstSearch(UpperCamelCase__			,		UpperCamelCase__)
											snake_case__	  =       BreadthFirstSearch(UpperCamelCase__			,		UpperCamelCase__)
											snake_case__	  =       False



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
																		snake_case__	  =       self.fwd_bfs.node_queue.pop(0)
																		snake_case__	  =       self.bwd_bfs.node_queue.pop(0)

																		if current_bwd_node.pos == current_fwd_node.pos:
																									snake_case__	  =       True
																									return self.retrace_bidirectional_path(
																									    UpperCamelCase__			,		UpperCamelCase__)

																		snake_case__	  =       current_bwd_node
																		snake_case__	  =       current_fwd_node

																		snake_case__	  =       {
																		    self.fwd_bfs: self.fwd_bfs.get_successors(UpperCamelCase__),
																		    self.bwd_bfs: self.bwd_bfs.get_successors(UpperCamelCase__),
																		}

																		for bfs in [self.fwd_bfs, self.bwd_bfs]:
																									for node in successors[bfs]:
																																bfs.node_queue.append(UpperCamelCase__)

											if not self.reached:
																		return [self.fwd_bfs.start.pos]
											return None



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Node			,		UpperCamelCase__    :    Node):






											'''simple docstring'''







											snake_case__	  =       self.fwd_bfs.retrace_path(UpperCamelCase__)
											snake_case__	  =       self.bwd_bfs.retrace_path(UpperCamelCase__)
											bwd_path.pop()
											bwd_path.reverse()
											snake_case__	  =       fwd_path + bwd_path
											return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	import doctest

	doctest.testmod()
	a__       =     (0, 0)
	a__       =     (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	a__       =     time.time()
	a__       =     BreadthFirstSearch(init, goal)
	a__       =     bfs.search()
	a__       =     time.time() - start_bfs_time

	print("""Unidirectional BFS computation time : """, bfs_time)

	a__       =     time.time()
	a__       =     BidirectionalBreadthFirstSearch(init, goal)
	a__       =     bd_bfs.search()
	a__       =     time.time() - start_bd_bfs_time

	print("""Bidirectional BFS computation time : """, bd_bfs_time)
