


import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor







@require_tokenizers
@require_vision
class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Tuple      )					->    Optional[Any]:
												a__             =     tempfile.mkdtemp()

												# fmt: off
												a__             =     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
												# fmt: on
												a__             =     os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['vocab_file']      )
												with open(self.vocab_file			,'w'			,encoding='utf-8'      ) as vocab_writer:
																			vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )

												a__             =     {
												    'do_resize': True,
												    'size': {'height': 18, 'width': 18},
												    'do_normalize': True,
												    'image_mean': [0.5, 0.5, 0.5],
												    'image_std': [0.5, 0.5, 0.5],
												}
												a__             =     os.path.join(self.tmpdirname			,__snake_case      )
												with open(self.image_processor_file			,'w'			,encoding='utf-8'      ) as fp:
																			json.dump(__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :Any			,**__snake_case   :int      )					->    str:
												return BertTokenizer.from_pretrained(self.tmpdirname			,**__snake_case      )
					def  lowerCamelCase__(      self   :str			,**__snake_case   :Dict      )					->    Optional[int]:
												return ViTImageProcessor.from_pretrained(self.tmpdirname			,**__snake_case      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Optional[Any]:
												shutil.rmtree(self.tmpdirname      )
					def  lowerCamelCase__(      self   :int      )					->    int:
												a__             =     [np.random.randint(2_55			,size=(3, 30, 4_00)			,dtype=np.uinta      )]

												a__             =     [Image.fromarray(np.moveaxis(__snake_case			,0			,-1      )      ) for x in image_inputs]

												return image_inputs
					def  lowerCamelCase__(      self   :List[str]      )					->    Dict:
												a__             =     self.get_tokenizer()
												a__             =     self.get_image_processor()

												a__             =     VisionTextDualEncoderProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )

												processor.save_pretrained(self.tmpdirname      )
												a__             =     VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname      )

												self.assertEqual(processor.tokenizer.get_vocab()			,tokenizer.get_vocab()      )
												self.assertIsInstance(processor.tokenizer			,(BertTokenizer, BertTokenizerFast)      )

												self.assertEqual(processor.image_processor.to_json_string()			,image_processor.to_json_string()      )
												self.assertIsInstance(processor.image_processor			,__snake_case      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Union[str, Any]:
												a__             =     VisionTextDualEncoderProcessor(
												    tokenizer=self.get_tokenizer()			,image_processor=self.get_image_processor()      )
												processor.save_pretrained(self.tmpdirname      )

												a__             =     self.get_tokenizer(bos_token='(BOS)'			,eos_token='(EOS)'      )
												a__             =     self.get_image_processor(do_normalize=__snake_case			,padding_value=1.0      )

												a__             =     VisionTextDualEncoderProcessor.from_pretrained(
												    self.tmpdirname			,bos_token='(BOS)'			,eos_token='(EOS)'			,do_normalize=__snake_case			,padding_value=1.0      )

												self.assertEqual(processor.tokenizer.get_vocab()			,tokenizer_add_kwargs.get_vocab()      )
												self.assertIsInstance(processor.tokenizer			,(BertTokenizer, BertTokenizerFast)      )

												self.assertEqual(processor.image_processor.to_json_string()			,image_processor_add_kwargs.to_json_string()      )
												self.assertIsInstance(processor.image_processor			,__snake_case      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												a__             =     self.get_image_processor()
												a__             =     self.get_tokenizer()

												a__             =     VisionTextDualEncoderProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )

												a__             =     self.prepare_image_inputs()

												a__             =     image_processor(__snake_case			,return_tensors='np'      )
												a__             =     processor(images=__snake_case			,return_tensors='np'      )

												for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()			,input_processor[key].sum()			,delta=1E-2      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[Any]:
												a__             =     self.get_image_processor()
												a__             =     self.get_tokenizer()

												a__             =     VisionTextDualEncoderProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )

												a__             =     'lower newer'

												a__             =     processor(text=__snake_case      )

												a__             =     tokenizer(__snake_case      )

												for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]			,encoded_processor[key]      )
					def  lowerCamelCase__(      self   :int      )					->    str:
												a__             =     self.get_image_processor()
												a__             =     self.get_tokenizer()

												a__             =     VisionTextDualEncoderProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )

												a__             =     'lower newer'
												a__             =     self.prepare_image_inputs()

												a__             =     processor(text=__snake_case			,images=__snake_case      )

												self.assertListEqual(list(inputs.keys()      )			,['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']      )

												# test if it raises when no input is passed
												with self.assertRaises(__snake_case      ):
																			processor()
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Any:
												a__             =     self.get_image_processor()
												a__             =     self.get_tokenizer()

												a__             =     VisionTextDualEncoderProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )

												a__             =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												a__             =     processor.batch_decode(__snake_case      )
												a__             =     tokenizer.batch_decode(__snake_case      )

												self.assertListEqual(__snake_case			,__snake_case      )



					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Optional[Any]:
												a__             =     self.get_image_processor()
												a__             =     self.get_tokenizer()

												a__             =     VisionTextDualEncoderProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )

												a__             =     'lower newer'
												a__             =     self.prepare_image_inputs()

												a__             =     processor(text=__snake_case			,images=__snake_case      )

												self.assertListEqual(list(inputs.keys()      )			,processor.model_input_names      )








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


snake_case							:       List[Any]     			=      logging.getLogger(__name__)






def 							__lowercase   ( __lowerCAmelCase							:				str=2					,    __lowerCAmelCase							:				Optional[int]=3					,    __lowerCAmelCase							:				List[str]=1_6					,    __lowerCAmelCase							:				int = 1_0					,    __lowerCAmelCase							:				int = 2   ):

							def get_dataset(__lowerCAmelCase							:				Dict   ):
														a__             =     torch.randn(batch_size * n_batches					,    1   )
														return TensorDataset(__lowerCAmelCase					,    a * x + b + 0.1 * torch.randn(batch_size * n_batches					,    1   )   )

							a__             =     get_dataset(__lowerCAmelCase   )
							a__             =     get_dataset(__lowerCAmelCase   )
							a__             =     DataLoader(__lowerCAmelCase					,    shuffle=__lowerCAmelCase					,    batch_size=__lowerCAmelCase					,    num_workers=4   )
							a__             =     DataLoader(__lowerCAmelCase					,    shuffle=__lowerCAmelCase					,    batch_size=__lowerCAmelCase					,    num_workers=4   )
							return (train_dataloader, valid_dataloader)






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







class        snake_case_   (nn.Module      ):
					def __init__(      self   :Any      )					->    Union[str, Any]:
												super().__init__()
												a__             =     nn.Parameter(torch.randn(1      )      )
												a__             =     nn.Parameter(torch.randn(1      )      )



					def  lowerCamelCase__(      self   :List[str]			,__snake_case   :Union[str, Any]      )					->    str:
												return x * self.a + self.b







class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(total_limit=1			,project_dir=__snake_case			,automatic_checkpoint_naming=__snake_case      )
																			# Train baseline
																			a__             =     Accelerator(project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			accelerator.save_state()

																			# Save second state
																			accelerator.save_state()
																			self.assertEqual(len(os.listdir(accelerator.project_dir      )      )			,1      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    str:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			# Train baseline
																			a__             =     Accelerator()
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			a__             =     os.path.join(__snake_case			,'initial'      )
																			accelerator.save_state(__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			a__             =     train(3			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()

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

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

																			# Load everything back in and make sure all states work
																			accelerator.load_state(__snake_case      )
																			test_rands += train(1			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :str      )					->    List[str]:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(automatic_checkpoint_naming=__snake_case      )

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

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

																			a__             =     train(2			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save everything
																			accelerator.save_state()

																			# Load everything back in and make sure all states work
																			accelerator.load_state(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_1'      )      )
																			test_rands += train(1			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    List[str]:
												a__             =     torch.tensor([1, 2, 3]      )
												a__             =     torch.tensor([2, 3, 4]      )
												a__             =     DummyModel()
												a__             =     torch.optim.Adam(net.parameters()      )
												a__             =     Accelerator()
												with self.assertRaises(__snake_case      ) as ve:
																			accelerator.register_for_checkpointing(__snake_case			,__snake_case			,__snake_case			,__snake_case      )
												a__             =     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  lowerCamelCase__(      self   :List[Any]      )					->    Dict:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__             =     torch.optim.lr_scheduler.StepLR(__snake_case			,step_size=1			,gamma=0.99      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(automatic_checkpoint_naming=__snake_case      )
																			# Train baseline
																			a__             =     Accelerator(project_dir=__snake_case			,project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			accelerator.save_state()
																			a__             =     scheduler.state_dict()
																			train(3			,__snake_case			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			self.assertNotEqual(__snake_case			,scheduler.state_dict()      )

																			# Load everything back in and make sure all states work
																			accelerator.load_state(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_0'      )      )
																			self.assertEqual(__snake_case			,scheduler.state_dict()      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     ProjectConfiguration(automatic_checkpoint_naming=__snake_case			,total_limit=2      )
																			# Train baseline
																			a__             =     Accelerator(project_dir=__snake_case			,project_config=__snake_case      )
																			a__             =     accelerator.prepare(__snake_case      )
																			# Save 3 states:
																			for _ in range(11      ):
																										accelerator.save_state()
																			self.assertTrue(not os.path.exists(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_0'      )      )      )
																			self.assertTrue(os.path.exists(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_9'      )      )      )
																			self.assertTrue(os.path.exists(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_10'      )      )      )



					@require_cuda
					def  lowerCamelCase__(      self   :Dict      )					->    str:
												a__             =     ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
												execute_subprocess_async(__snake_case			,env=os.environ.copy()      )


if __name__ == "__main__":
						snake_case							:       Tuple     			=      '''/tmp/accelerate/state_checkpointing'''
						snake_case							:       str     			=      DummyModel()
						snake_case							:       List[Any]     			=      torch.optim.Adam(params=model.parameters(), lr=1e-3)
						snake_case							:       Union[str, Any]     			=      torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
						snake_case			,     snake_case							:       str     			=      dummy_dataloaders()
						snake_case							:       Union[str, Any]     			=      ProjectConfiguration(automatic_checkpoint_naming=True)
						# Train baseline
						snake_case							:       Dict     			=      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)
						snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case							:       List[str]     			=      accelerator.prepare(
						    model, optimizer, train_dataloader, valid_dataloader, scheduler
						)
						snake_case			,     snake_case							:       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:
												snake_case							:       Any     			=      group['''params'''][0].device
												break
						assert param_device.type == accelerator.device.type
						snake_case							:       Union[str, Any]     			=      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:
												snake_case							:       int     			=      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:
												snake_case							:       Optional[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()




