





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


_lowercase		:  Any			       ="""true"""


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=82							,lowerCAmelCase__=16						):
		set_seed(42						)
		lowerCamelCase_	:     Optional[Any] 						=      RegressionModel()
		lowerCamelCase_	:     Tuple 						=      deepcopy(lowerCAmelCase__						)
		lowerCamelCase_	:     Union[str, Any] 						=      RegressionDataset(length=lowerCAmelCase__						)
		lowerCamelCase_	:     str 						=      DataLoader(lowerCAmelCase__							,batch_size=lowerCAmelCase__						)
		model.to(accelerator.device						)
		lowerCamelCase_						,      lowerCamelCase_	:     Optional[Any] 						=      accelerator.prepare(lowerCAmelCase__							,lowerCAmelCase__						)
		return model, ddp_model, dataloader


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=False						):
		lowerCamelCase_	:     Optional[int] 						=      AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'						)
		lowerCamelCase_	:     Tuple 						=      load_dataset('glue'							,'mrpc'							,split='validation'						)

		def tokenize_function(lowerCAmelCase__						):
				lowerCamelCase_	:     Dict 						=      tokenizer(examples['sentence1']							,examples['sentence2']							,truncation=lowerCAmelCase__							,max_length=lowerCAmelCase__						)
				return outputs

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

		lowerCamelCase_	:     Optional[int] 						=      tokenized_datasets.rename_column('label'							,'labels'						)

		def collate_fn(lowerCAmelCase__						):
				if use_longest:
						return tokenizer.pad(lowerCAmelCase__							,padding='longest'							,return_tensors='pt'						)
				return tokenizer.pad(lowerCAmelCase__							,padding='max_length'							,max_length=1_28							,return_tensors='pt'						)

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


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


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     Dict 						=      []
		for batch in dataloader:
				lowerCamelCase_						,      lowerCamelCase_	:     Dict 						=      batch.values()
				with torch.no_grad():
						lowerCamelCase_	:     str 						=      model(lowerCAmelCase__						)
						lowerCamelCase_						,      lowerCamelCase_	:     Optional[int] 						=      accelerator.gather_for_metrics((logit, target)						)
						logits_and_targets.append((logit, target)						)
		lowerCamelCase_						,      lowerCamelCase_	:     Optional[int] 						=      [], []
		for logit, targ in logits_and_targets:
				logits.append(lowerCAmelCase__						)
				targs.append(lowerCAmelCase__						)
		lowerCamelCase_						,      lowerCamelCase_	:     Union[str, Any] 						=      torch.cat(lowerCAmelCase__						), torch.cat(lowerCAmelCase__						)
		return logits, targs


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=82							,lowerCAmelCase__=False							,lowerCAmelCase__=False							,lowerCAmelCase__=16						):
		lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Dict 						=      get_basic_setup(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
		lowerCamelCase_						,      lowerCamelCase_	:     Optional[Any] 						=      generate_predictions(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
		assert (
		    len(lowerCAmelCase__						) == num_samples
		), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase__						)}"


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__ = False							,lowerCAmelCase__ = False						):
		lowerCamelCase_	:     List[Any] 						=      evaluate.load('glue'							,'mrpc'						)
		lowerCamelCase_						,      lowerCamelCase_	:     Tuple 						=      get_mrpc_setup(lowerCAmelCase__							,lowerCAmelCase__						)
		# First do baseline
		lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Union[str, Any] 						=      setup['no']
		model.to(lowerCAmelCase__						)
		model.eval()
		for batch in dataloader:
				batch.to(lowerCAmelCase__						)
				with torch.inference_mode():
						lowerCamelCase_	:     Union[str, Any] 						=      model(**lowerCAmelCase__						)
				lowerCamelCase_	:     str 						=      outputs.logits.argmax(dim=-1						)
				metric.add_batch(predictions=lowerCAmelCase__							,references=batch['labels']						)
		lowerCamelCase_	:     Union[str, Any] 						=      metric.compute()

		# Then do distributed
		lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Union[str, Any] 						=      setup['ddp']
		model.eval()
		for batch in dataloader:
				with torch.inference_mode():
						lowerCamelCase_	:     Union[str, Any] 						=      model(**lowerCAmelCase__						)
				lowerCamelCase_	:     Optional[Any] 						=      outputs.logits.argmax(dim=-1						)
				lowerCamelCase_	:     int 						=      batch['labels']
				lowerCamelCase_						,      lowerCamelCase_	:     Dict 						=      accelerator.gather_for_metrics((preds, references)						)
				metric.add_batch(predictions=lowerCAmelCase__							,references=lowerCAmelCase__						)
		lowerCamelCase_	:     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 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Optional[int] 						=      Accelerator(split_batches=lowerCAmelCase__							,dispatch_batches=lowerCAmelCase__						)
		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(lowerCAmelCase__							,lowerCAmelCase__						)
								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_	:     Dict 						=      Accelerator(split_batches=lowerCAmelCase__							,dispatch_batches=lowerCAmelCase__						)
						if accelerator.is_local_main_process:
								print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"						)
						test_torch_metrics(lowerCAmelCase__							,99						)
						accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print('**Test last batch is not dropped when perfectly divisible**'						)
		lowerCamelCase_	:     int 						=      Accelerator()
		test_torch_metrics(lowerCAmelCase__							,5_12						)
		accelerator.state._reset_state()


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
		main()







_lowercase		:  Any			       ={"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
_lowercase		:  Union[str, Any]			       =["""a""", """b""", """c""", """d""", """e"""]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      start
		# add current to visited
		visited.append(lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      edges[current]
		for neighbor in neighbors:
				# if neighbor not in visited, visit
				if neighbor not in visited:
						lowerCamelCase_	:     Dict 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # if all neighbors visited add current to sort
		sort.append(lowerCAmelCase__						)
		# if all vertices haven't been visited select a new one to visit
		if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
				for vertice in vertices:
						if vertice not in visited:
								lowerCamelCase_	:     Any 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # return sort
		return sort


if __name__ == "__main__":
		_lowercase		:  str			       =topological_sort("""a""", [], [])
		print(sort)
