





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


__UpperCamelCase:	Optional[int] = """true"""


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Any   ,    _lowercase     : Optional[Any]=82   ,    _lowercase     : List[Any]=16			)   ->							str:




		'''simple docstring'''
		set_seed(42			)
		lowercase__					:      Optional[Any]      =		RegressionModel()
		lowercase__					:      List[Any]      =		deepcopy(_lowercase			)
		lowercase__					:      Dict      =		RegressionDataset(length=_lowercase			)
		lowercase__					:      str      =		DataLoader(_lowercase   ,    batch_size=_lowercase			)
		model.to(accelerator.device			)
		lowercase__   ,				lowercase__					:      int      =		accelerator.prepare(_lowercase   ,    _lowercase			)
		return model, ddp_model, dataloader


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Accelerator   ,    _lowercase     : int=False			)   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      Dict      =		AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'			)
		lowercase__					:      int      =		load_dataset('glue'   ,    'mrpc'   ,    split='validation'			)

		def tokenize_function(_lowercase     : Tuple			):
				lowercase__					:      List[Any]      =		tokenizer(examples['sentence1']   ,    examples['sentence2']   ,    truncation=_lowercase   ,    max_length=_lowercase			)
				return outputs

		with accelerator.main_process_first():
				lowercase__					:      Any      =		dataset.map(
				    _lowercase   ,    batched=_lowercase   ,    remove_columns=['idx', 'sentence1', 'sentence2']   ,    )

		lowercase__					:      int      =		tokenized_datasets.rename_column('label'   ,    'labels'			)

		def collate_fn(_lowercase     : Optional[int]			):
				if use_longest:
						return tokenizer.pad(_lowercase   ,    padding='longest'   ,    return_tensors='pt'			)
				return tokenizer.pad(_lowercase   ,    padding='max_length'   ,    max_length=128   ,    return_tensors='pt'			)

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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Dict   ,    _lowercase     : Tuple			)   ->							Optional[Any]:




		'''simple docstring'''
		lowercase__					:      int      =		Accelerator(dispatch_batches=_lowercase   ,    split_batches=_lowercase			)
		lowercase__					:      Any      =		get_dataloader(_lowercase   ,    not dispatch_batches			)
		lowercase__					:      Any      =		AutoModelForSequenceClassification.from_pretrained(
		    'hf-internal-testing/mrpc-bert-base-cased'   ,    return_dict=_lowercase			)
		lowercase__   ,				lowercase__					:      str      =		accelerator.prepare(_lowercase   ,    _lowercase			)
		return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Tuple   ,    _lowercase     : Tuple   ,    _lowercase     : Optional[Any]			)   ->							Dict:




		'''simple docstring'''
		lowercase__					:      Tuple      =		[]
		for batch in dataloader:
				lowercase__   ,				lowercase__					:      Optional[Any]      =		batch.values()
				with torch.no_grad():
						lowercase__					:      List[Any]      =		model(_lowercase			)
						lowercase__   ,				lowercase__					:      int      =		accelerator.gather_for_metrics((logit, target)			)
						logits_and_targets.append((logit, target)			)
		lowercase__   ,				lowercase__					:      Optional[int]      =		[], []
		for logit, targ in logits_and_targets:
				logits.append(_lowercase			)
				targs.append(_lowercase			)
		lowercase__   ,				lowercase__					:      List[str]      =		torch.cat(_lowercase			), torch.cat(_lowercase			)
		return logits, targs


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Accelerator   ,    _lowercase     : List[Any]=82   ,    _lowercase     : Dict=False   ,    _lowercase     : Dict=False   ,    _lowercase     : Optional[int]=16			)   ->							Tuple:




		'''simple docstring'''
		lowercase__   ,				lowercase__   ,				lowercase__					:      Any      =		get_basic_setup(_lowercase   ,    _lowercase   ,    _lowercase			)
		lowercase__   ,				lowercase__					:      Optional[int]      =		generate_predictions(_lowercase   ,    _lowercase   ,    _lowercase			)
		assert (
		    len(_lowercase			) == num_samples
		), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_lowercase			)}"""


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : bool = False   ,    _lowercase     : bool = False			)   ->							str:




		'''simple docstring'''
		lowercase__					:      Dict      =		evaluate.load('glue'   ,    'mrpc'			)
		lowercase__   ,				lowercase__					:      Optional[int]      =		get_mrpc_setup(_lowercase   ,    _lowercase			)
		# First do baseline
		lowercase__   ,				lowercase__   ,				lowercase__					:      Union[str, Any]      =		setup['no']
		model.to(_lowercase			)
		model.eval()
		for batch in dataloader:
				batch.to(_lowercase			)
				with torch.inference_mode():
						lowercase__					:      Dict      =		model(**_lowercase			)
				lowercase__					:      str      =		outputs.logits.argmax(dim=-1			)
				metric.add_batch(predictions=_lowercase   ,    references=batch['labels']			)
		lowercase__					:      Optional[Any]      =		metric.compute()

		# Then do distributed
		lowercase__   ,				lowercase__   ,				lowercase__					:      Union[str, Any]      =		setup['ddp']
		model.eval()
		for batch in dataloader:
				with torch.inference_mode():
						lowercase__					:      Union[str, Any]      =		model(**_lowercase			)
				lowercase__					:      Dict      =		outputs.logits.argmax(dim=-1			)
				lowercase__					:      Optional[int]      =		batch['labels']
				lowercase__   ,				lowercase__					:      Union[str, Any]      =		accelerator.gather_for_metrics((preds, references)			)
				metric.add_batch(predictions=_lowercase   ,    references=_lowercase			)
		lowercase__					:      List[str]      =		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       SCREAMING_SNAKE_CASE__  (       )   ->							Dict:




		'''simple docstring'''
		lowercase__					:      Any      =		Accelerator(split_batches=_lowercase   ,    dispatch_batches=_lowercase			)
		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(_lowercase   ,    _lowercase			)
								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]:
						lowercase__					:      Optional[Any]      =		Accelerator(split_batches=_lowercase   ,    dispatch_batches=_lowercase			)
						if accelerator.is_local_main_process:
								print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""			)
						test_torch_metrics(_lowercase   ,    99			)
						accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print('**Test last batch is not dropped when perfectly divisible**'			)
		lowercase__					:      str      =		Accelerator()
		test_torch_metrics(_lowercase   ,    512			)
		accelerator.state._reset_state()


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]			)   ->							Optional[Any]:




		'''simple docstring'''
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
			main()









from __future__ import annotations


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[float]   ,    _lowercase     : Tuple			)   ->							int:




		'''simple docstring'''
		print(f"""Vertex\tShortest Distance from vertex {src}"""			)
		for i, d in enumerate(_lowercase			):
				print(f"""{i}\t\t{d}"""			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : list[float]   ,    _lowercase     : int			)   ->							Any:




		'''simple docstring'''
		for j in range(_lowercase			):
				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		(graph[j][k] for k in ['src', 'dst', 'weight'])
				if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
						return True
		return False


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : int   ,    _lowercase     : int   ,    _lowercase     : int			)   ->							list[float]:




		'''simple docstring'''
		lowercase__					:      Dict      =		[float('inf'			)] * vertex_count
		lowercase__					:      Dict      =		0.0

		for _ in range(vertex_count - 1			):
				for j in range(_lowercase			):
						lowercase__   ,				lowercase__   ,				lowercase__					:      int      =		(graph[j][k] for k in ['src', 'dst', 'weight'])

						if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
								lowercase__					:      str      =		distance[u] + w

		lowercase__					:      str      =		check_negative_cycle(_lowercase   ,    _lowercase   ,    _lowercase			)
		if negative_cycle_exists:
				raise Exception('Negative cycle found'			)

		return distance


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	Optional[int] = int(input("""Enter number of vertices: """).strip())
			__UpperCamelCase:	Union[str, Any] = int(input("""Enter number of edges: """).strip())

			__UpperCamelCase:	list[dict[str, int]] = [{} for _ in range(E)]

			for i in range(E):
						print("""Edge """, i + 1)
						__UpperCamelCase,	__UpperCamelCase,	__UpperCamelCase:	List[str] = (
						    int(x)
						    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
						)
						__UpperCamelCase:	List[Any] = {"""src""": src, """dst""": dest, """weight""": weight}

			__UpperCamelCase:	Optional[int] = int(input("""\nEnter shortest path source:""").strip())
			__UpperCamelCase:	Dict = bellman_ford(graph, V, E, source)
			print_distance(shortest_distance, 0)


