




import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()





class 		A__    (       unittest.TestCase			):






 @property
 def 			lowercase				(     self						)				->       str:




      """simple docstring"""






      torch.manual_seed(0						)
      __magic_name__ :							List[Any]		       =							UNetaDModel(
          block_out_channels=(32, 64)    ,					layers_per_block=2    ,					sample_size=32    ,					in_channels=3    ,					out_channels=3    ,					down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,					up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,					)
      return model






 def 			lowercase				(     self						)				->       Dict:




      """simple docstring"""






      __magic_name__ :							Optional[int]		       =							"cpu"
      __magic_name__ :							Dict		       =							self.dummy_uncond_unet
      __magic_name__ :							Optional[int]		       =							DDPMScheduler()

      __magic_name__ :							Any		       =							DDPMPipeline(unet=__lowerCamelCase    ,					scheduler=__lowerCamelCase						)
      ddpm.to(__lowerCamelCase						)
      ddpm.set_progress_bar_config(disable=__lowerCamelCase						)

      __magic_name__ :							List[Any]		       =							torch.Generator(device=__lowerCamelCase						).manual_seed(0						)
      __magic_name__ :							Any		       =							ddpm(generator=__lowerCamelCase    ,					num_inference_steps=2    ,					output_type='''numpy'''						).images

      __magic_name__ :							Optional[int]		       =							torch.Generator(device=__lowerCamelCase						).manual_seed(0						)
      __magic_name__ :							Optional[Any]		       =							ddpm(generator=__lowerCamelCase    ,					num_inference_steps=2    ,					output_type='''numpy'''    ,					return_dict=__lowerCamelCase						)[0]

      __magic_name__ :							Union[str, Any]		       =							image[0, -3:, -3:, -1]
      __magic_name__ :							Tuple		       =							image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __magic_name__ :							Union[str, Any]		       =							np.array(
          [9.9_56e-01, 5.7_85e-01, 4.6_75e-01, 9.9_30e-01, 0.0, 1.0_0_0, 1.1_99e-03, 2.6_48e-04, 5.1_01e-04]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2






 def 			lowercase				(     self						)				->       Optional[int]:




      """simple docstring"""






      __magic_name__ :							Tuple		       =							self.dummy_uncond_unet
      __magic_name__ :							Optional[int]		       =							DDPMScheduler(prediction_type='''sample'''						)

      __magic_name__ :							Any		       =							DDPMPipeline(unet=__lowerCamelCase    ,					scheduler=__lowerCamelCase						)
      ddpm.to(__lowerCamelCase						)
      ddpm.set_progress_bar_config(disable=__lowerCamelCase						)

      __magic_name__ :							Dict		       =							torch.manual_seed(0						)
      __magic_name__ :							Dict		       =							ddpm(generator=__lowerCamelCase    ,					num_inference_steps=2    ,					output_type='''numpy'''						).images

      __magic_name__ :							Optional[Any]		       =							torch.manual_seed(0						)
      __magic_name__ :							Dict		       =							ddpm(generator=__lowerCamelCase    ,					num_inference_steps=2    ,					output_type='''numpy'''						)[0]

      __magic_name__ :							Tuple		       =							image[0, -3:, -3:, -1]
      __magic_name__ :							List[Any]		       =							image_eps[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __magic_name__ :							Union[str, Any]		       =							1e-2 if torch_device != "mps" else 3e-2
      assert np.abs(image_slice.flatten() - image_eps_slice.flatten()						).max() < tolerance





@slow
@require_torch_gpu
class 		A__    (       unittest.TestCase			):






 def 			lowercase				(     self						)				->       List[str]:




      """simple docstring"""






      __magic_name__ :							str		       =							"google/ddpm-cifar10-32"

      __magic_name__ :							Optional[Any]		       =							UNetaDModel.from_pretrained(__lowerCamelCase						)
      __magic_name__ :							str		       =							DDPMScheduler.from_pretrained(__lowerCamelCase						)

      __magic_name__ :							str		       =							DDPMPipeline(unet=__lowerCamelCase    ,					scheduler=__lowerCamelCase						)
      ddpm.to(__lowerCamelCase						)
      ddpm.set_progress_bar_config(disable=__lowerCamelCase						)

      __magic_name__ :							str		       =							torch.manual_seed(0						)
      __magic_name__ :							List[str]		       =							ddpm(generator=__lowerCamelCase    ,					output_type='''numpy'''						).images

      __magic_name__ :							List[str]		       =							image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __magic_name__ :							Optional[Any]		       =							np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]						)
      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2









import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
				from PIL import Image





class 		A__    (       unittest.TestCase		,					__SCREAMING_SNAKE_CASE			):






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							load_tool('''image-question-answering'''						)
						self.tool.setup()
						__magic_name__ :							Any		       =							load_tool('''image-question-answering'''    ,					remote=lowerCamelCase						)






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.remote_tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							int		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.remote_tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)



