


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 						lowercase_(    SCREAMING_SNAKE_CASE_=2 ,		SCREAMING_SNAKE_CASE_=3 ,		SCREAMING_SNAKE_CASE_=16 ,		SCREAMING_SNAKE_CASE_ = 10 ,		SCREAMING_SNAKE_CASE_ = 2      ):


  '''simple docstring'''

  def get_dataset(SCREAMING_SNAKE_CASE_      ):
    lowerCamelCase					:   Optional[Any]       =							torch.randn(batch_size * n_batches ,		1      )
    return TensorDataset(_lowercase ,		a * x + b + 0.1 * torch.randn(batch_size * n_batches ,		1      )      )

  lowerCamelCase					:   Any       =							get_dataset(_lowercase      )
  lowerCamelCase					:   Dict       =							get_dataset(_lowercase      )
  lowerCamelCase					:   Optional[int]       =							DataLoader(_lowercase ,		shuffle=_lowercase ,		batch_size=_lowercase ,		num_workers=4      )
  lowerCamelCase					:   Optional[int]       =							DataLoader(_lowercase ,		shuffle=_lowercase ,		batch_size=_lowercase ,		num_workers=4      )
  return (train_dataloader, valid_dataloader)






def 						lowercase_(    SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_=None      ):


  '''simple docstring'''

  lowerCamelCase					:   Tuple       =							[]
  for epoch in range(_lowercase      ):
    # Train quickly
    model.train()
    for batch in dataloader:
      lowerCamelCase       ,      lowerCamelCase					:   Tuple       =							batch
      lowerCamelCase					:   Any       =							model(_lowercase      )
      lowerCamelCase					:   Tuple       =							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       UpperCAmelCase_   (   nn.Module       ):

   '''simple docstring'''




   def __init__(						self					):






     """simple docstring"""





     super().__init__()
     lowerCamelCase					:   Optional[Any]       =							nn.Parameter(torch.randn(1					)					)
     lowerCamelCase					:   List[Any]       =							nn.Parameter(torch.randn(1					)					)






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     return x * self.a + self.b



class       UpperCAmelCase_   (   unittest.TestCase       ):

   '''simple docstring'''




   def _snake_case       (						self					):






     """simple docstring"""





     with tempfile.TemporaryDirectory() as tmpdir:
       set_seed(42					)
       lowerCamelCase					:   List[str]       =							DummyModel()
       lowerCamelCase					:   int       =							torch.optim.Adam(params=model.parameters()		,							lr=1e-3					)
       lowerCamelCase       ,      lowerCamelCase					:   Optional[int]       =							dummy_dataloaders()
       lowerCamelCase					:   List[str]       =							ProjectConfiguration(total_limit=1		,							project_dir=A_		,							automatic_checkpoint_naming=A_					)
       # Train baseline
       lowerCamelCase					:   List[Any]       =							Accelerator(project_config=A_					)
       lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase					:   Dict       =							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					):






     """simple docstring"""





     with tempfile.TemporaryDirectory() as tmpdir:
       set_seed(42					)
       lowerCamelCase					:   List[Any]       =							DummyModel()
       lowerCamelCase					:   Optional[Any]       =							torch.optim.Adam(params=model.parameters()		,							lr=1e-3					)
       lowerCamelCase       ,      lowerCamelCase					:   int       =							dummy_dataloaders()
       # Train baseline
       lowerCamelCase					:   Tuple       =							Accelerator()
       lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase					:   Optional[Any]       =							accelerator.prepare(
           A_		,							A_		,							A_		,							A_					)
       # Save initial
       lowerCamelCase					:   List[Any]       =							os.path.join(A_		,							"initial"					)
       accelerator.save_state(A_					)
       ((lowerCamelCase)       ,      (lowerCamelCase))					:   Tuple       =							model.a.item(), model.b.item()
       lowerCamelCase					:   Tuple       =							optimizer.state_dict()
       lowerCamelCase					:   int       =							train(3		,							A_		,							A_		,							A_		,							A_					)
       ((lowerCamelCase)       ,      (lowerCamelCase))					:   List[Any]       =							model.a.item(), model.b.item()
       lowerCamelCase					:   List[str]       =							optimizer.state_dict()

       # Train partially
       set_seed(42					)
       lowerCamelCase					:   List[str]       =							DummyModel()
       lowerCamelCase					:   Union[str, Any]       =							torch.optim.Adam(params=model.parameters()		,							lr=1e-3					)
       lowerCamelCase       ,      lowerCamelCase					:   Dict       =							dummy_dataloaders()
       lowerCamelCase					:   Optional[Any]       =							Accelerator()
       lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase					:   int       =							accelerator.prepare(
           A_		,							A_		,							A_		,							A_					)
       accelerator.load_state(A_					)
       ((lowerCamelCase)       ,      (lowerCamelCase))					:   List[Any]       =							model.a.item(), model.b.item()
       lowerCamelCase					:   Tuple       =							optimizer.state_dict()
       self.assertEqual(A_		,							A_					)
       self.assertEqual(A_		,							A_					)
       self.assertEqual(A_		,							A_					)

       lowerCamelCase					:   str       =							train(2		,							A_		,							A_		,							A_		,							A_					)
       # Save everything
       lowerCamelCase					:   Dict       =							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_					)
       ((lowerCamelCase)       ,      (lowerCamelCase))					:   Union[str, Any]       =							model.a.item(), model.b.item()
       lowerCamelCase					:   str       =							optimizer.state_dict()
       self.assertEqual(A_		,							A_					)
       self.assertEqual(A_		,							A_					)
       self.assertEqual(A_		,							A_					)
       self.assertEqual(A_		,							A_					)






   def _snake_case       (						self					):






     """simple docstring"""





     with tempfile.TemporaryDirectory() as tmpdir:
       set_seed(42					)
       lowerCamelCase					:   Optional[int]       =							DummyModel()
       lowerCamelCase					:   List[str]       =							torch.optim.Adam(params=model.parameters()		,							lr=1e-3					)
       lowerCamelCase       ,      lowerCamelCase					:   str       =							dummy_dataloaders()
       lowerCamelCase					:   Optional[Any]       =							ProjectConfiguration(automatic_checkpoint_naming=A_					)

       # Train baseline
       lowerCamelCase					:   int       =							Accelerator(project_dir=A_		,							project_config=A_					)
       lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase					:   Dict       =							accelerator.prepare(
           A_		,							A_		,							A_		,							A_					)
       # Save initial
       accelerator.save_state()
       ((lowerCamelCase)       ,      (lowerCamelCase))					:   Optional[Any]       =							model.a.item(), model.b.item()
       lowerCamelCase					:   Dict       =							optimizer.state_dict()
       lowerCamelCase					:   Any       =							train(3		,							A_		,							A_		,							A_		,							A_					)
       ((lowerCamelCase)       ,      (lowerCamelCase))					:   Tuple       =							model.a.item(), model.b.item()
       lowerCamelCase					:   Tuple       =							optimizer.state_dict()

       # Train partially
       set_seed(42					)
       lowerCamelCase					:   Union[str, Any]       =							DummyModel()
       lowerCamelCase					:   List[str]       =							torch.optim.Adam(params=model.parameters()		,							lr=1e-3					)
       lowerCamelCase       ,      lowerCamelCase					:   List[Any]       =							dummy_dataloaders()
       lowerCamelCase					:   Union[str, Any]       =							ProjectConfiguration(iteration=1		,							automatic_checkpoint_naming=A_					)
       lowerCamelCase					:   Union[str, Any]       =							Accelerator(project_dir=A_		,							project_config=A_					)
       lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase					:   Union[str, Any]       =							accelerator.prepare(
           A_		,							A_		,							A_		,							A_					)
       accelerator.load_state(os.path.join(A_		,							"checkpoints"		,							"checkpoint_0"					)					)
       ((lowerCamelCase)       ,      (lowerCamelCase))					:   Union[str, Any]       =							model.a.item(), model.b.item()
       lowerCamelCase					:   List[Any]       =							optimizer.state_dict()
       self.assertEqual(A_		,							A_					)
       self.assertEqual(A_		,							A_					)
       self.assertEqual(A_		,							A_					)

       lowerCamelCase					:   Optional[Any]       =							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_					)
       ((lowerCamelCase)       ,      (lowerCamelCase))					:   Any       =							model.a.item(), model.b.item()
       lowerCamelCase					:   Optional[Any]       =							optimizer.state_dict()
       self.assertEqual(A_		,							A_					)
       self.assertEqual(A_		,							A_					)
       self.assertEqual(A_		,							A_					)
       self.assertEqual(A_		,							A_					)






   def _snake_case       (						self					):






     """simple docstring"""





     lowerCamelCase					:   Tuple       =							torch.tensor([1, 2, 3]					)
     lowerCamelCase					:   int       =							torch.tensor([2, 3, 4]					)
     lowerCamelCase					:   str       =							DummyModel()
     lowerCamelCase					:   Tuple       =							torch.optim.Adam(net.parameters()					)
     lowerCamelCase					:   Union[str, Any]       =							Accelerator()
     with self.assertRaises(A_					) as ve:
       accelerator.register_for_checkpointing(A_		,							A_		,							A_		,							A_					)
     lowerCamelCase					:   int       =							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					):






     """simple docstring"""





     with tempfile.TemporaryDirectory() as tmpdir:
       set_seed(42					)
       lowerCamelCase					:   List[str]       =							DummyModel()
       lowerCamelCase					:   List[Any]       =							torch.optim.Adam(params=model.parameters()		,							lr=1e-3					)
       lowerCamelCase					:   Optional[int]       =							torch.optim.lr_scheduler.StepLR(A_		,							step_size=1		,							gamma=0.99					)
       lowerCamelCase       ,      lowerCamelCase					:   Optional[int]       =							dummy_dataloaders()
       lowerCamelCase					:   Dict       =							ProjectConfiguration(automatic_checkpoint_naming=A_					)
       # Train baseline
       lowerCamelCase					:   Optional[Any]       =							Accelerator(project_dir=A_		,							project_config=A_					)
       lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase					:   Optional[int]       =							accelerator.prepare(
           A_		,							A_		,							A_		,							A_		,							A_					)
       # Save initial
       accelerator.save_state()
       lowerCamelCase					:   Tuple       =							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					):






     """simple docstring"""





     with tempfile.TemporaryDirectory() as tmpdir:
       set_seed(42					)
       lowerCamelCase					:   Dict       =							DummyModel()
       lowerCamelCase					:   Tuple       =							ProjectConfiguration(automatic_checkpoint_naming=A_		,							total_limit=2					)
       # Train baseline
       lowerCamelCase					:   List[Any]       =							Accelerator(project_dir=A_		,							project_config=A_					)
       lowerCamelCase					:   Optional[Any]       =							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					):






     """simple docstring"""





     lowerCamelCase					:   Any       =							["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)


