

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:    List[str] 							=			"""true"""



def       __lowerCAmelCase      (						A       ,     A=82       ,     A=16    ):
	set_seed(42    )
	UpperCAmelCase_       				=			RegressionModel()
	UpperCAmelCase_       				=			deepcopy(A    )
	UpperCAmelCase_       				=			RegressionDataset(length=A    )
	UpperCAmelCase_       				=			DataLoader(A       ,     batch_size=A    )
	model.to(accelerator.device    )
	UpperCAmelCase_				,   UpperCAmelCase_       				=			accelerator.prepare(A       ,     A    )
	return model, ddp_model, dataloader



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"    )
	UpperCAmelCase_       				=			load_dataset("glue"       ,     "mrpc"       ,     split="validation"    )

	def tokenize_function(A    ):
		UpperCAmelCase_       				=			tokenizer(examples["sentence1"]       ,     examples["sentence2"]       ,     truncation=A       ,     max_length=A    )
		return outputs

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

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

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



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[]
	for batch in dataloader:
		UpperCAmelCase_				,   UpperCAmelCase_       				=			batch.values()
		with torch.no_grad():
			UpperCAmelCase_       				=			model(A    )
			UpperCAmelCase_				,   UpperCAmelCase_       				=			accelerator.gather_for_metrics((logit, target)    )
			logits_and_targets.append((logit, target)    )
	UpperCAmelCase_				,   UpperCAmelCase_       				=			[], []
	for logit, targ in logits_and_targets:
		logits.append(A    )
		targs.append(A    )
	UpperCAmelCase_				,   UpperCAmelCase_       				=			torch.cat(A    ), torch.cat(A    )
	return logits, targs



def       __lowerCAmelCase      (						A       ,     A=82       ,     A=False       ,     A=False       ,     A=16    ):
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			get_basic_setup(A       ,     A       ,     A    )
	UpperCAmelCase_				,   UpperCAmelCase_       				=			generate_predictions(A       ,     A       ,     A    )
	assert (
	    len(A    ) == num_samples
	), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A    )}"



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

	# Then do distributed
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			setup["ddp"]
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			UpperCAmelCase_       				=			model(**A    )
		UpperCAmelCase_       				=			outputs.logits.argmax(dim=-1    )
		UpperCAmelCase_       				=			batch["labels"]
		UpperCAmelCase_				,   UpperCAmelCase_       				=			accelerator.gather_for_metrics((preds, references)    )
		metric.add_batch(predictions=A       ,     references=A    )
	UpperCAmelCase_       				=			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       __lowerCAmelCase      (						):
	UpperCAmelCase_       				=			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]:
			UpperCAmelCase_       				=			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**"    )
	UpperCAmelCase_       				=			Accelerator()
	test_torch_metrics(A       ,     512    )
	accelerator.state._reset_state()



def       __lowerCAmelCase      (						A    ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
				main()


from __future__ import annotations

from collections import Counter
from random import random

class 						__UpperCamelCase    :




	def __init__(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						float							):


		'''simple docstring'''







		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		UpperCAmelCase_       				=			probability




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return list(self.connections							)




	def       __A					(		self	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			0
		UpperCAmelCase_       				=			random()

		for dest in self.connections[node]:
			current_probability += self.connections[node][dest]
			if current_probability > random_value:
				return dest
		return ""



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(A       ,     A       ,     A    )

	UpperCAmelCase_       				=			Counter(graph.get_nodes()    )
	UpperCAmelCase_       				=			start

	for _ in range(A    ):
		UpperCAmelCase_       				=			graph.transition(A    )
		visited[node] += 1

	return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()