
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_lowerCamelCase	:    Tuple          =      logging.getLogger(__name__)






def 	a_		(	__lowercase       :     Any=2	, __lowercase       :     Union[str, Any]=3	, __lowercase       :     str=16	, __lowercase       :     List[Any] = 10	, __lowercase       :     Tuple = 2			)			->  Optional[Any]:

			def get_dataset(__lowercase       :     Union[str, Any]			):
						_snake_case          =      torch.randn(batch_size * n_batches	, 1			)
						return TensorDataset(lowercase__	, a * x + b + 0.1 * torch.randn(batch_size * n_batches	, 1			)			)

			_snake_case          =      get_dataset(lowercase__			)
			_snake_case          =      get_dataset(lowercase__			)
			_snake_case          =      DataLoader(lowercase__	, shuffle=lowercase__	, batch_size=lowercase__	, num_workers=4			)
			_snake_case          =      DataLoader(lowercase__	, shuffle=lowercase__	, batch_size=lowercase__	, num_workers=4			)
			return (train_dataloader, valid_dataloader)






def 	a_		(	__lowercase       :     Optional[Any]	, __lowercase       :     int	, __lowercase       :     List[str]	, __lowercase       :     str	, __lowercase       :     Tuple	, __lowercase       :     Tuple=None			)			->  int:
			_snake_case          =      []
			for epoch in range(lowercase__			):
						# Train quickly
						model.train()
						for batch in dataloader:
									_snake_case          =      batch
									_snake_case          =      model(lowercase__			)
									_snake_case          =      torch.nn.functional.mse_loss(lowercase__	, lowercase__			)
									accelerator.backward(lowercase__			)
									optimizer.step()
									optimizer.zero_grad()
						rands.append(random.random()			)  # Introduce some randomness
						if scheduler is not None:
									scheduler.step()
			return rands




class        SCREAMING_SNAKE_CASE__					(     nn.Module ):





	'''simple docstring'''



	def __init__(  self     :   List[Any]     ):


				'''simple docstring'''


				super().__init__()
				_snake_case          =      nn.Parameter(torch.randn(1     )     )
				_snake_case          =      nn.Parameter(torch.randn(1     )     )





	def 			A							(  self     :   Any      ,  lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				return x * self.a + self.b




class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42     )
							_snake_case          =      DummyModel()
							_snake_case          =      torch.optim.Adam(params=model.parameters()      ,  lr=1E-3     )
							_snake_case          =      dummy_dataloaders()
							_snake_case          =      ProjectConfiguration(total_limit=1      ,  project_dir=lowercase      ,  automatic_checkpoint_naming=lowercase     )
							# Train baseline
							_snake_case          =      Accelerator(project_config=lowercase     )
							_snake_case          =      accelerator.prepare(
							    lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							# Save initial
							accelerator.save_state()

							# Save second state
							accelerator.save_state()
							self.assertEqual(len(os.listdir(accelerator.project_dir     )     )      ,  1     )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42     )
							_snake_case          =      DummyModel()
							_snake_case          =      torch.optim.Adam(params=model.parameters()      ,  lr=1E-3     )
							_snake_case          =      dummy_dataloaders()
							# Train baseline
							_snake_case          =      Accelerator()
							_snake_case          =      accelerator.prepare(
							    lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							# Save initial
							_snake_case          =      os.path.join(lowercase      ,  'initial'     )
							accelerator.save_state(lowercase     )
							(_snake_case)          =      model.a.item(), model.b.item()
							_snake_case          =      optimizer.state_dict()
							_snake_case          =      train(3      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							(_snake_case)          =      model.a.item(), model.b.item()
							_snake_case          =      optimizer.state_dict()

							# Train partially
							set_seed(42     )
							_snake_case          =      DummyModel()
							_snake_case          =      torch.optim.Adam(params=model.parameters()      ,  lr=1E-3     )
							_snake_case          =      dummy_dataloaders()
							_snake_case          =      Accelerator()
							_snake_case          =      accelerator.prepare(
							    lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							accelerator.load_state(lowercase     )
							(_snake_case)          =      model.a.item(), model.b.item()
							_snake_case          =      optimizer.state_dict()
							self.assertEqual(lowercase      ,  lowercase     )
							self.assertEqual(lowercase      ,  lowercase     )
							self.assertEqual(lowercase      ,  lowercase     )

							_snake_case          =      train(2      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							# Save everything
							_snake_case          =      os.path.join(lowercase      ,  'checkpoint'     )
							accelerator.save_state(lowercase     )

							# Load everything back in and make sure all states work
							accelerator.load_state(lowercase     )
							test_rands += train(1      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							(_snake_case)          =      model.a.item(), model.b.item()
							_snake_case          =      optimizer.state_dict()
							self.assertEqual(lowercase      ,  lowercase     )
							self.assertEqual(lowercase      ,  lowercase     )
							self.assertEqual(lowercase      ,  lowercase     )
							self.assertEqual(lowercase      ,  lowercase     )



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42     )
							_snake_case          =      DummyModel()
							_snake_case          =      torch.optim.Adam(params=model.parameters()      ,  lr=1E-3     )
							_snake_case          =      dummy_dataloaders()
							_snake_case          =      ProjectConfiguration(automatic_checkpoint_naming=lowercase     )

							# Train baseline
							_snake_case          =      Accelerator(project_dir=lowercase      ,  project_config=lowercase     )
							_snake_case          =      accelerator.prepare(
							    lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							# Save initial
							accelerator.save_state()
							(_snake_case)          =      model.a.item(), model.b.item()
							_snake_case          =      optimizer.state_dict()
							_snake_case          =      train(3      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							(_snake_case)          =      model.a.item(), model.b.item()
							_snake_case          =      optimizer.state_dict()

							# Train partially
							set_seed(42     )
							_snake_case          =      DummyModel()
							_snake_case          =      torch.optim.Adam(params=model.parameters()      ,  lr=1E-3     )
							_snake_case          =      dummy_dataloaders()
							_snake_case          =      ProjectConfiguration(iteration=1      ,  automatic_checkpoint_naming=lowercase     )
							_snake_case          =      Accelerator(project_dir=lowercase      ,  project_config=lowercase     )
							_snake_case          =      accelerator.prepare(
							    lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							accelerator.load_state(os.path.join(lowercase      ,  'checkpoints'      ,  'checkpoint_0'     )     )
							(_snake_case)          =      model.a.item(), model.b.item()
							_snake_case          =      optimizer.state_dict()
							self.assertEqual(lowercase      ,  lowercase     )
							self.assertEqual(lowercase      ,  lowercase     )
							self.assertEqual(lowercase      ,  lowercase     )

							_snake_case          =      train(2      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							# Save everything
							accelerator.save_state()

							# Load everything back in and make sure all states work
							accelerator.load_state(os.path.join(lowercase      ,  'checkpoints'      ,  'checkpoint_1'     )     )
							test_rands += train(1      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							(_snake_case)          =      model.a.item(), model.b.item()
							_snake_case          =      optimizer.state_dict()
							self.assertEqual(lowercase      ,  lowercase     )
							self.assertEqual(lowercase      ,  lowercase     )
							self.assertEqual(lowercase      ,  lowercase     )
							self.assertEqual(lowercase      ,  lowercase     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      torch.tensor([1, 2, 3]     )
				_snake_case          =      torch.tensor([2, 3, 4]     )
				_snake_case          =      DummyModel()
				_snake_case          =      torch.optim.Adam(net.parameters()     )
				_snake_case          =      Accelerator()
				with self.assertRaises(lowercase     ) as ve:
							accelerator.register_for_checkpointing(lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      str(ve.exception     )
				self.assertTrue('Item at index 0' in message     )
				self.assertTrue('Item at index 1' in message     )
				self.assertFalse('Item at index 2' in message     )
				self.assertFalse('Item at index 3' in message     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42     )
							_snake_case          =      DummyModel()
							_snake_case          =      torch.optim.Adam(params=model.parameters()      ,  lr=1E-3     )
							_snake_case          =      torch.optim.lr_scheduler.StepLR(lowercase      ,  step_size=1      ,  gamma=0.99     )
							_snake_case          =      dummy_dataloaders()
							_snake_case          =      ProjectConfiguration(automatic_checkpoint_naming=lowercase     )
							# Train baseline
							_snake_case          =      Accelerator(project_dir=lowercase      ,  project_config=lowercase     )
							_snake_case          =      accelerator.prepare(
							    lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							# Save initial
							accelerator.save_state()
							_snake_case          =      scheduler.state_dict()
							train(3      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
							self.assertNotEqual(lowercase      ,  scheduler.state_dict()     )

							# Load everything back in and make sure all states work
							accelerator.load_state(os.path.join(lowercase      ,  'checkpoints'      ,  'checkpoint_0'     )     )
							self.assertEqual(lowercase      ,  scheduler.state_dict()     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42     )
							_snake_case          =      DummyModel()
							_snake_case          =      ProjectConfiguration(automatic_checkpoint_naming=lowercase      ,  total_limit=2     )
							# Train baseline
							_snake_case          =      Accelerator(project_dir=lowercase      ,  project_config=lowercase     )
							_snake_case          =      accelerator.prepare(lowercase     )
							# Save 3 states:
							for _ in range(11     ):
										accelerator.save_state()
							self.assertTrue(not os.path.exists(os.path.join(lowercase      ,  'checkpoints'      ,  'checkpoint_0'     )     )     )
							self.assertTrue(os.path.exists(os.path.join(lowercase      ,  'checkpoints'      ,  'checkpoint_9'     )     )     )
							self.assertTrue(os.path.exists(os.path.join(lowercase      ,  'checkpoints'      ,  'checkpoint_10'     )     )     )





	@require_cuda
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__     )]
				execute_subprocess_async(lowercase      ,  env=os.environ.copy()     )


if __name__ == "__main__":
					_lowerCamelCase	:    Tuple          =      '''/tmp/accelerate/state_checkpointing'''
					_lowerCamelCase	:    Tuple          =      DummyModel()
					_lowerCamelCase	:    Dict          =      torch.optim.Adam(params=model.parameters(), lr=1E-3)
					_lowerCamelCase	:    Optional[Any]          =      torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
					_lowerCamelCase   ,					_lowerCamelCase	:    Dict          =      dummy_dataloaders()
					_lowerCamelCase	:    Optional[int]          =      ProjectConfiguration(automatic_checkpoint_naming=True)
					# Train baseline
					_lowerCamelCase	:    Optional[Any]          =      Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
					if accelerator.process_index == 0:
										if os.path.exists(savedir):
															shutil.rmtree(savedir)
										os.makedirs(savedir)
					_lowerCamelCase   ,					_lowerCamelCase   ,					_lowerCamelCase   ,					_lowerCamelCase   ,					_lowerCamelCase	:    int          =      accelerator.prepare(
					    model, optimizer, train_dataloader, valid_dataloader, scheduler
					)
					_lowerCamelCase   ,					_lowerCamelCase	:    Optional[Any]          =      accelerator.prepare(model, optimizer)
					train(3, model, train_dataloader, optimizer, accelerator, scheduler)
					# Check that the intial optimizer is loaded on the GPU
					for group in optimizer.param_groups:
										_lowerCamelCase	:    Any          =      group['''params'''][0].device
										break
					assert param_device.type == accelerator.device.type
					_lowerCamelCase	:    str          =      model.cpu()
					accelerator.wait_for_everyone()
					accelerator.save_state()
					accelerator.wait_for_everyone()

					# Check CPU state
					accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
					for group in optimizer.param_groups:
										_lowerCamelCase	:    Union[str, Any]          =      group['''params'''][0].device
										break
					assert (
					    param_device.type == torch.device('''cpu''').type
					), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

					# Check device state
					model.to(accelerator.device)
					accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
					for group in optimizer.param_groups:
										_lowerCamelCase	:    int          =      group['''params'''][0].device
										break
					assert (
					    param_device.type == accelerator.device.type
					), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

					# Check error
					with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
										accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
					accelerator.wait_for_everyone()
					if accelerator.process_index == 0:
										shutil.rmtree(savedir)
					accelerator.wait_for_everyone()

import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_lowerCamelCase	:    Union[str, Any]          =      logging.getLogger()






def 	a_		(	__lowercase       :     Path	, __lowercase       :     list			)			->  Tuple:
			_snake_case          =      '\n'.join(__lowercase			)
			Path(__lowercase			).open('w'			).writelines(__lowercase			)


_lowerCamelCase	:    Any          =      '''patrickvonplaten/t5-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/bart-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/tiny-mbart'''

_lowerCamelCase	:    Union[str, Any]          =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()
				_snake_case          =      [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
				_dump_articles(lowercase      ,  lowercase     )

				_snake_case          =      str(Path(self.get_auto_remove_tmp_dir()     ) / 'scores.json'     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

				with patch.object(lowercase      ,  'argv'      ,  lowercase     ):
							run_generate()
							assert Path(lowercase     ).exists()
							# os.remove(Path(output_file_name))



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )



	@parameterized.expand([BART_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   int     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )





	@parameterized.expand([T5_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()

				_snake_case          =      {
				    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
				    'de': [
				        'Maschinelles Lernen ist großartig, oder?',
				        'Ich esse gerne Bananen',
				        'Morgen ist wieder ein toller Tag!',
				    ],
				}

				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     )
				_snake_case          =      str(tmp_dir / 'scores.json'     )
				_snake_case          =      str(tmp_dir / 'val.target'     )
				_dump_articles(lowercase      ,  text['en']     )
				_dump_articles(lowercase      ,  text['de']     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {str(lowercase     )}
            {str(lowercase     )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
				testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']     )

				with patch.object(lowercase      ,  'argv'      ,  lowercase     ):
							with CaptureStdout() as cs:
										run_search()
							_snake_case          =      [' num_beams | length_penalty', model, 'Best score args']
							_snake_case          =      ['Info']
							if "translation" in task:
										expected_strings.append('bleu'     )
							else:
										expected_strings.extend(lowercase     )
							for w in expected_strings:
										assert w in cs.out
							for w in un_expected_strings:
										assert w not in cs.out
							assert Path(lowercase     ).exists()
							os.remove(Path(lowercase     )     )