

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class     a__    (				unittest.TestCase    ):







  """simple docstring"""




  @property
  def 	_lowercase (     self      :	List[str]						)   ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: int					       =				1
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				3
        SCREAMING_SNAKE_CASE	: str					       =				8
        SCREAMING_SNAKE_CASE	: Tuple					       =				8

        SCREAMING_SNAKE_CASE	: List[Any]					       =				torch.rand((batch_size, num_channels, height, width)						)

        return sample


  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	int						)   ->Dict:
        """simple docstring"""







        if isinstance(image[0]			,			PIL.Image.Image						):
              return np.stack([np.array(__UpperCamelCase						) for i in image]			,			axis=0						)
        elif isinstance(__UpperCamelCase			,			torch.Tensor						):
              return image.cpu().numpy().transpose(0			,			2			,			3			,			1						)
        return image


  def 	_lowercase (     self      :	Any						)   ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				VaeImageProcessor(do_resize=__UpperCamelCase			,			do_normalize=__UpperCamelCase						)

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.dummy_sample
        SCREAMING_SNAKE_CASE	: Any					       =				self.to_np(__UpperCamelCase						)

        for output_type in ["pt", "np", "pil"]:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				image_processor.postprocess(
                  image_processor.preprocess(__UpperCamelCase						)			,			output_type=__UpperCamelCase			,			)
              SCREAMING_SNAKE_CASE	: List[str]					       =				self.to_np(__UpperCamelCase						)
              SCREAMING_SNAKE_CASE	: List[Any]					       =				(input_np * 2_5_5).round() if output_type == """pil""" else input_np
              assert (
                  np.abs(in_np - out_np						).max() < 1e-6
              ), f"decoded output does not match input for output_type {output_type}"


  def 	_lowercase (     self      :	Optional[Any]						)   ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[int]					       =				VaeImageProcessor(do_resize=__UpperCamelCase			,			do_normalize=__UpperCamelCase						)
        SCREAMING_SNAKE_CASE	: Dict					       =				self.dummy_sample.cpu().numpy().transpose(0			,			2			,			3			,			1						)

        for output_type in ["pt", "np", "pil"]:
              SCREAMING_SNAKE_CASE	: Dict					       =				image_processor.postprocess(image_processor.preprocess(__UpperCamelCase						)			,			output_type=__UpperCamelCase						)

              SCREAMING_SNAKE_CASE	: str					       =				self.to_np(__UpperCamelCase						)
              SCREAMING_SNAKE_CASE	: Tuple					       =				(input_np * 2_5_5).round() if output_type == """pil""" else input_np
              assert (
                  np.abs(in_np - out_np						).max() < 1e-6
              ), f"decoded output does not match input for output_type {output_type}"


  def 	_lowercase (     self      :	Union[str, Any]						)   ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				VaeImageProcessor(do_resize=__UpperCamelCase			,			do_normalize=__UpperCamelCase						)

        SCREAMING_SNAKE_CASE	: Any					       =				self.dummy_sample.cpu().numpy().transpose(0			,			2			,			3			,			1						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				image_processor.numpy_to_pil(__UpperCamelCase						)

        for output_type in ["pt", "np", "pil"]:
              SCREAMING_SNAKE_CASE	: Tuple					       =				image_processor.postprocess(image_processor.preprocess(__UpperCamelCase						)			,			output_type=__UpperCamelCase						)
              for i, o in zip(__UpperCamelCase			,			__UpperCamelCase						):
                    SCREAMING_SNAKE_CASE	: str					       =				np.array(__UpperCamelCase						)
                    SCREAMING_SNAKE_CASE	: List[Any]					       =				self.to_np(__UpperCamelCase						) if output_type == """pil""" else (self.to_np(__UpperCamelCase						) * 2_5_5).round()
                    assert (
                        np.abs(in_np - out_np						).max() < 1e-6
                    ), f"decoded output does not match input for output_type {output_type}"


  def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				VaeImageProcessor(do_resize=__UpperCamelCase			,			do_normalize=__UpperCamelCase						)

        SCREAMING_SNAKE_CASE	: Tuple					       =				self.dummy_sample
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				input_pt_ad.squeeze(0						)

        SCREAMING_SNAKE_CASE	: Dict					       =				image_processor.postprocess(
            image_processor.preprocess(__UpperCamelCase						)			,			output_type="""np"""			,			)
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				image_processor.postprocess(
            image_processor.preprocess(__UpperCamelCase						)			,			output_type="""np"""			,			)

        SCREAMING_SNAKE_CASE	: List[str]					       =				self.to_np(self.dummy_sample						)
        SCREAMING_SNAKE_CASE	: str					       =				input_np_ad.squeeze(0						)

        SCREAMING_SNAKE_CASE	: Dict					       =				image_processor.postprocess(
            image_processor.preprocess(__UpperCamelCase						)			,			output_type="""np"""			,			)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				image_processor.postprocess(
            image_processor.preprocess(__UpperCamelCase						)			,			output_type="""np"""			,			)

        assert np.abs(out_pt_ad - out_pt_ad						).max() < 1e-6
        assert np.abs(out_np_ad - out_np_ad						).max() < 1e-6




  def 	_lowercase (     self      :	Union[str, Any]						)   ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[int]					       =				VaeImageProcessor(do_resize=__UpperCamelCase			,			do_normalize=__UpperCamelCase						)

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.dummy_sample
        SCREAMING_SNAKE_CASE	: Tuple					       =				list(__UpperCamelCase						)

        SCREAMING_SNAKE_CASE	: List[Any]					       =				image_processor.postprocess(
            image_processor.preprocess(__UpperCamelCase						)			,			output_type="""np"""			,			)

        SCREAMING_SNAKE_CASE	: List[str]					       =				image_processor.postprocess(
            image_processor.preprocess(__UpperCamelCase						)			,			output_type="""np"""			,			)

        SCREAMING_SNAKE_CASE	: Any					       =				self.to_np(self.dummy_sample						)
        list(__UpperCamelCase						)

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				image_processor.postprocess(
            image_processor.preprocess(__UpperCamelCase						)			,			output_type="""np"""			,			)

        SCREAMING_SNAKE_CASE	: Tuple					       =				image_processor.postprocess(
            image_processor.preprocess(__UpperCamelCase						)			,			output_type="""np"""			,			)

        assert np.abs(out_pt_ad - out_pt_list						).max() < 1e-6
        assert np.abs(out_np_ad - out_np_list						).max() < 1e-6







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment







class     a__    (				unittest.TestCase    ):







  """simple docstring"""




  def 	_lowercase (     self      :	Optional[int]						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				inspect.getfile(accelerate.test_utils						)
        SCREAMING_SNAKE_CASE	: int					       =				os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_sync.py"""]						)


  @require_cpu
  def 	_lowercase (     self      :	Optional[int]						)   ->str:
        """simple docstring"""







        debug_launcher(test_sync.main			,			num_processes=1						)


  @require_cpu
  def 	_lowercase (     self      :	Optional[int]						)   ->int:
        """simple docstring"""







        debug_launcher(test_sync.main						)


  @require_single_gpu
  def 	_lowercase (     self      :	str						)   ->Any:
        """simple docstring"""







        test_sync.main()




  @require_multi_gpu
  def 	_lowercase (     self      :	Union[str, Any]						)   ->List[Any]:
        """simple docstring"""







        print(f"Found {torch.cuda.device_count()} devices."						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1						):
              execute_subprocess_async(UpperCAmelCase__			,			env=os.environ.copy()						)




