

"""simple docstring"""


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     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=8_2					, __SCREAMING_SNAKE_CASE=1_6 )	->	str:
		set_seed(4_2 )
		__lowerCAmelCase:       str          =			RegressionModel()
		__lowerCAmelCase:       Optional[Any]          =			deepcopy(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       List[str]          =			RegressionDataset(length=__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Union[str, Any]          =			DataLoader(__SCREAMING_SNAKE_CASE					, batch_size=__SCREAMING_SNAKE_CASE )
		model.to(accelerator.device )
		__lowerCAmelCase      , __lowerCAmelCase:       Dict          =			accelerator.prepare(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		return model, ddp_model, dataloader






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=False )	->	Optional[int]:
		__lowerCAmelCase:       List[Any]          =			AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
		__lowerCAmelCase:       List[str]          =			load_dataset("glue"					, "mrpc"					, split="validation" )

		def tokenize_function(__SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase:       List[str]          =			tokenizer(examples["sentence1"]					, examples["sentence2"]					, truncation=__SCREAMING_SNAKE_CASE					, max_length=__SCREAMING_SNAKE_CASE )
				return outputs

		with accelerator.main_process_first():
				__lowerCAmelCase:       List[str]          =			dataset.map(
				    __SCREAMING_SNAKE_CASE					, batched=__SCREAMING_SNAKE_CASE					, remove_columns=["idx", "sentence1", "sentence2"]					, )

		__lowerCAmelCase:       Optional[Any]          =			tokenized_datasets.rename_column("label"					, "labels" )

		def collate_fn(__SCREAMING_SNAKE_CASE ):
				if use_longest:
						return tokenizer.pad(__SCREAMING_SNAKE_CASE					, padding="longest"					, return_tensors="pt" )
				return tokenizer.pad(__SCREAMING_SNAKE_CASE					, padding="max_length"					, max_length=1_2_8					, return_tensors="pt" )

		return DataLoader(__SCREAMING_SNAKE_CASE					, shuffle=__SCREAMING_SNAKE_CASE					, collate_fn=__SCREAMING_SNAKE_CASE					, batch_size=1_6 )






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






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Any:
		__lowerCAmelCase:       Optional[int]          =			[]
		for batch in dataloader:
				__lowerCAmelCase      , __lowerCAmelCase:       Union[str, Any]          =			batch.values()
				with torch.no_grad():
						__lowerCAmelCase:       List[str]          =			model(__SCREAMING_SNAKE_CASE )
						__lowerCAmelCase      , __lowerCAmelCase:       Union[str, Any]          =			accelerator.gather_for_metrics((logit, target) )
						logits_and_targets.append((logit, target) )
		__lowerCAmelCase      , __lowerCAmelCase:       Dict          =			[], []
		for logit, targ in logits_and_targets:
				logits.append(__SCREAMING_SNAKE_CASE )
				targs.append(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase      , __lowerCAmelCase:       Any          =			torch.cat(__SCREAMING_SNAKE_CASE ), torch.cat(__SCREAMING_SNAKE_CASE )
		return logits, targs






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=8_2					, __SCREAMING_SNAKE_CASE=False					, __SCREAMING_SNAKE_CASE=False					, __SCREAMING_SNAKE_CASE=1_6 )	->	Union[str, Any]:
		__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       List[str]          =			get_basic_setup(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		__lowerCAmelCase      , __lowerCAmelCase:       List[str]          =			generate_predictions(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		assert (
		    len(__SCREAMING_SNAKE_CASE ) == num_samples
		), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__SCREAMING_SNAKE_CASE )}"






def     a__    (    __SCREAMING_SNAKE_CASE = False					, __SCREAMING_SNAKE_CASE = False )	->	List[str]:
		__lowerCAmelCase:       Any          =			evaluate.load("glue"					, "mrpc" )
		__lowerCAmelCase      , __lowerCAmelCase:       Optional[int]          =			get_mrpc_setup(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		# First do baseline
		__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       Dict          =			setup["no"]
		model.to(__SCREAMING_SNAKE_CASE )
		model.eval()
		for batch in dataloader:
				batch.to(__SCREAMING_SNAKE_CASE )
				with torch.inference_mode():
						__lowerCAmelCase:       Optional[int]          =			model(**__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       Optional[int]          =			outputs.logits.argmax(dim=-1 )
				metric.add_batch(predictions=__SCREAMING_SNAKE_CASE					, references=batch["labels"] )
		__lowerCAmelCase:       Dict          =			metric.compute()

		# Then do distributed
		__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       List[str]          =			setup["ddp"]
		model.eval()
		for batch in dataloader:
				with torch.inference_mode():
						__lowerCAmelCase:       Any          =			model(**__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       Optional[Any]          =			outputs.logits.argmax(dim=-1 )
				__lowerCAmelCase:       str          =			batch["labels"]
				__lowerCAmelCase      , __lowerCAmelCase:       Union[str, Any]          =			accelerator.gather_for_metrics((preds, references) )
				metric.add_batch(predictions=__SCREAMING_SNAKE_CASE					, references=__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Union[str, Any]          =			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     a__    (    )	->	List[Any]:
		__lowerCAmelCase:       Tuple          =			Accelerator(split_batches=__SCREAMING_SNAKE_CASE					, dispatch_batches=__SCREAMING_SNAKE_CASE )
		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(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
								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]:
						__lowerCAmelCase:       str          =			Accelerator(split_batches=__SCREAMING_SNAKE_CASE					, dispatch_batches=__SCREAMING_SNAKE_CASE )
						if accelerator.is_local_main_process:
								print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99" )
						test_torch_metrics(__SCREAMING_SNAKE_CASE					, 9_9 )
						accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print("**Test last batch is not dropped when perfectly divisible**" )
		__lowerCAmelCase:       Any          =			Accelerator()
		test_torch_metrics(__SCREAMING_SNAKE_CASE					, 5_1_2 )
		accelerator.state._reset_state()






def     a__    (    __SCREAMING_SNAKE_CASE )	->	Tuple:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
			main()





"""simple docstring"""


__A						   =			{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	list[str]:
		__lowerCAmelCase:       Tuple          =			set()
		# keep track of all the paths to be checked
		__lowerCAmelCase:       Optional[Any]          =			[[start]]

		# return path if start is goal
		if start == goal:
				return [start]

		# keeps looping until all possible paths have been checked
		while queue:
				# pop the first path from the queue
				__lowerCAmelCase:       str          =			queue.pop(0 )
				# get the last node from the path
				__lowerCAmelCase:       List[Any]          =			path[-1]
				if node not in explored:
						__lowerCAmelCase:       str          =			graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								__lowerCAmelCase:       Optional[int]          =			list(__SCREAMING_SNAKE_CASE )
								new_path.append(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(__SCREAMING_SNAKE_CASE )

    # in case there's no path between the 2 nodes
		return []






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		__lowerCAmelCase:       Dict          =			[start]
		__lowerCAmelCase:       Any          =			set(__SCREAMING_SNAKE_CASE )
		# Keep tab on distances from `start` node.
		__lowerCAmelCase:       int          =			{start: 0, target: -1}
		while queue:
				__lowerCAmelCase:       Optional[Any]          =			queue.pop(0 )
				if node == target:
						__lowerCAmelCase:       Dict          =			(
						    dist[node] if dist[target] == -1 else min(dist[target]					, dist[node] )
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								__lowerCAmelCase:       List[str]          =			dist[node] + 1
		return dist[target]


if __name__ == "__main__":
			print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
			print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4


