






'''simple docstring'''





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


snake_case__      :	List[str]       	=      logging.get_logger(__name__)  # pylint: disable=invalid-name






def     _snake_case    (__lowercase):
   warnings.warn(
       'The preprocess method is deprecated and will be removed in a future version. Please'
       ' use VaeImageProcessor.preprocess instead'    ,      __lowercase    ,      )
   if isinstance(__lowercase    ,      torch.Tensor):
      return image
   elif isinstance(__lowercase    ,      PIL.Image.Image):
      UpperCamelCase_									=     [image]

   if isinstance(image[0]    ,      PIL.Image.Image):
      UpperCamelCase_									=     image[0].size
      UpperCamelCase_									=     (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

      UpperCamelCase_									=     [np.array(i.resize((w, h)    ,      resample=PIL_INTERPOLATION['lanczos']))[None, :] for i in image]
      UpperCamelCase_									=     np.concatenate(__lowercase    ,      axis=0)
      UpperCamelCase_									=     np.array(__lowercase).astype(np.floataa) / 255.0
      UpperCamelCase_									=     image.transpose(0    ,      3    ,      1    ,      2)
      UpperCamelCase_									=     2.0 * image - 1.0
      UpperCamelCase_									=     torch.from_numpy(__lowercase)
   elif isinstance(image[0]    ,      torch.Tensor):
      UpperCamelCase_									=     torch.cat(__lowercase    ,      dim=0)
   return image






def     _snake_case    (__lowercase):
   if isinstance(__lowercase    ,      torch.Tensor):
      return mask
   elif isinstance(__lowercase    ,      PIL.Image.Image):
      UpperCamelCase_									=     [mask]

   if isinstance(mask[0]    ,      PIL.Image.Image):
      UpperCamelCase_									=     mask[0].size
      UpperCamelCase_									=     (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      UpperCamelCase_									=     [np.array(m.convert('L').resize((w, h)    ,      resample=PIL_INTERPOLATION['nearest']))[None, :] for m in mask]
      UpperCamelCase_									=     np.concatenate(__lowercase    ,      axis=0)
      UpperCamelCase_									=     mask.astype(np.floataa) / 255.0
      UpperCamelCase_									=     0
      UpperCamelCase_									=     1
      UpperCamelCase_									=     torch.from_numpy(__lowercase)
   elif isinstance(mask[0]    ,      torch.Tensor):
      UpperCamelCase_									=     torch.cat(__lowercase    ,      dim=0)
   return mask


class 						_a       (  snake_case__    ):






  """simple docstring"""





  A_						=   42
  A_						=   42




  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> str:
     super().__init__()
     self.register_modules(unet=_A		,		scheduler=_A )




  @torch.no_grad()
  def __call__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = 250		,		_UpperCAmelCase = 0.0		,		_UpperCAmelCase = 10		,		_UpperCAmelCase = 10		,		_UpperCAmelCase = None		,		_UpperCAmelCase = "pil"		,		_UpperCAmelCase = True		,		)			-> int:
     UpperCamelCase_									=     image

     UpperCamelCase_									=     _preprocess_image(_A )
     UpperCamelCase_									=     original_image.to(device=self.device		,		dtype=self.unet.dtype )
     UpperCamelCase_									=     _preprocess_mask(_A )
     UpperCamelCase_									=     mask_image.to(device=self.device		,		dtype=self.unet.dtype )

     UpperCamelCase_									=     original_image.shape[0]

     # sample gaussian noise to begin the loop
     if isinstance(_A		,		_A ) and len(_A ) != batch_size:
        raise ValueError(
            f"""You have passed a list of generators of length {len(_A )}, but requested an effective batch"""
            f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

     UpperCamelCase_									=     original_image.shape
     UpperCamelCase_									=     randn_tensor(_A		,		generator=_A		,		device=self.device		,		dtype=self.unet.dtype )

     # set step values
     self.scheduler.set_timesteps(_A		,		_A		,		_A		,		self.device )
     UpperCamelCase_									=     eta

     UpperCamelCase_									=     self.scheduler.timesteps[0] + 1
     UpperCamelCase_									=     generator[0] if isinstance(_A		,		_A ) else generator
     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
        if t < t_last:
           # predict the noise residual
           UpperCamelCase_									=     self.unet(_A		,		_A ).sample
           # compute previous image: x_t -> x_t-1
           UpperCamelCase_									=     self.scheduler.step(_A		,		_A		,		_A		,		_A		,		_A		,		_A ).prev_sample

        else:
           # compute the reverse: x_t-1 -> x_t
           UpperCamelCase_									=     self.scheduler.undo_step(_A		,		_A		,		_A )
        UpperCamelCase_									=     t

     UpperCamelCase_									=     (image / 2 + 0.5).clamp(0		,		1 )
     UpperCamelCase_									=     image.cpu().permute(0		,		2		,		3		,		1 ).numpy()
     if output_type == "pil":
        UpperCamelCase_									=     self.numpy_to_pil(_A )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_A )













import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command


class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  A_						=   inspect.getfile(accelerate.test_utils    )
  A_						=   os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_cli.py"""]    )

  A_						=   ["""accelerate""", """launch"""]
  A_						=   Path.home() / """.cache/huggingface/accelerate"""
  A_						=   """default_config.yaml"""
  A_						=   config_folder / config_file
  A_						=   config_folder / """_default_config.yaml"""

  A_						=   Path("""tests/test_configs"""    )




  @classmethod
  def    _UpperCAmelCase     (     cls )			-> Union[str, Any]:
     if cls.config_path.is_file():
        cls.config_path.rename(cls.changed_path )




  @classmethod
  def    _UpperCAmelCase     (     cls )			-> List[str]:
     if cls.changed_path.is_file():
        cls.changed_path.rename(cls.config_path )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.base_cmd
     if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
        cmd += ["--multi_gpu"]
     execute_subprocess_async(cmd + [self.test_file_path]		,		env=os.environ.copy() )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     for config in sorted(self.test_config_path.glob('**/*.yaml' ) ):
        with self.subTest(config_file=_UpperCAmelCase ):
           execute_subprocess_async(
               self.base_cmd + ['--config_file', str(_UpperCAmelCase ), self.test_file_path]		,		env=os.environ.copy() )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     execute_subprocess_async(['accelerate', 'test']		,		env=os.environ.copy() )




class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  A_						=   """test-tpu"""
  A_						=   """us-central1-a"""
  A_						=   """ls"""
  A_						=   ["""accelerate""", """tpu-config"""]
  A_						=   """cd /usr/share"""
  A_						=   """tests/test_samples/test_command_file.sh"""
  A_						=   """Running gcloud compute tpus tpu-vm ssh"""




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     run_command(
         self.cmd
         + ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command',
             self.command,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug']		,		return_stdout=_UpperCAmelCase )
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--command',
             self.command,
             '--command',
             'echo "Hello World"',
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     run_command(
         self.cmd
         + ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command_file',
             self.command_file,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--install_accelerate',
             '--accelerate_version',
             '12.0.0',
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)





