


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   							=						logging.getLogger(__name__)






def 		_A		(		_lowercase=2					,  _lowercase=3					,  _lowercase=16					,  _lowercase = 10					,  _lowercase = 2 )					-> Any:


     """simple docstring"""


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

     __UpperCamelCase =			get_dataset(_lowercase )
     __UpperCamelCase =			get_dataset(_lowercase )
     __UpperCamelCase =			DataLoader(_lowercase					,  shuffle=_lowercase					,  batch_size=_lowercase					,  num_workers=4 )
     __UpperCamelCase =			DataLoader(_lowercase					,  shuffle=_lowercase					,  batch_size=_lowercase					,  num_workers=4 )
     return (train_dataloader, valid_dataloader)






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase=None )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			[]
     for epoch in range(_lowercase ):
          # Train quickly
          model.train()
          for batch in dataloader:
               __UpperCamelCase,    __UpperCamelCase =			batch
               __UpperCamelCase =			model(_lowercase )
               __UpperCamelCase =			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 	__lowerCamelCase			(nn.Module						):







       def __init__(			self: Optional[Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Tuple,A_: Tuple					):






            '''simple docstring'''




            return x * self.a + self.b



class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




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

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







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




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

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

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

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







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




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

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

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

                 __UpperCamelCase =			train(2,A_,A_,A_,A_					)
                 # Save everything
                 accelerator.save_state()

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







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			torch.tensor([1, 2, 3]					)
            __UpperCamelCase =			torch.tensor([2, 3, 4]					)
            __UpperCamelCase =			DummyModel()
            __UpperCamelCase =			torch.optim.Adam(net.parameters()					)
            __UpperCamelCase =			Accelerator()
            with self.assertRaises(A_					) as ve:
                 accelerator.register_for_checkpointing(A_,A_,A_,A_					)
            __UpperCamelCase =			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 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            with tempfile.TemporaryDirectory() as tmpdir:
                 set_seed(42					)
                 __UpperCamelCase =			DummyModel()
                 __UpperCamelCase =			torch.optim.Adam(params=model.parameters(),lr=1E-3					)
                 __UpperCamelCase =			torch.optim.lr_scheduler.StepLR(A_,step_size=1,gamma=0.9_9					)
                 __UpperCamelCase,    __UpperCamelCase =			dummy_dataloaders()
                 __UpperCamelCase =			ProjectConfiguration(automatic_checkpoint_naming=A_					)
                 # Train baseline
                 __UpperCamelCase =			Accelerator(project_dir=A_,project_config=A_					)
                 __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			accelerator.prepare(
                     A_,A_,A_,A_,A_					)
                 # Save initial
                 accelerator.save_state()
                 __UpperCamelCase =			scheduler.state_dict()
                 train(3,A_,A_,A_,A_,A_					)
                 self.assertNotEqual(A_,scheduler.state_dict()					)

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







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




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







       @require_cuda
       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




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


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

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()

    parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

    parser.add_argument(
        '''--txt2img_unclip''',
        default='''kakaobrain/karlo-v1-alpha''',
        type=str,
        required=False,
        help='''The pretrained txt2img unclip.''',
    )

    __snake_case   							=						parser.parse_args()

    __snake_case   							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    __snake_case   							=						CLIPImageProcessor()
    __snake_case   							=						CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

    __snake_case   							=						UnCLIPImageVariationPipeline(
        decoder=txtaimg.decoder,
        text_encoder=txtaimg.text_encoder,
        tokenizer=txtaimg.tokenizer,
        text_proj=txtaimg.text_proj,
        feature_extractor=feature_extractor,
        image_encoder=image_encoder,
        super_res_first=txtaimg.super_res_first,
        super_res_last=txtaimg.super_res_last,
        decoder_scheduler=txtaimg.decoder_scheduler,
        super_res_scheduler=txtaimg.super_res_scheduler,
    )

    imgaimg.save_pretrained(args.dump_path)


