


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 lowerCAmelCase_		(					unittest.TestCase			):

 '''simple docstring'''

 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= 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 		UpperCamelCase__	(      self   ):
       snake_case_			= '''cpu'''
       snake_case_			= self.dummy_uncond_unet
       snake_case_			= DDPMScheduler()

       snake_case_			= DDPMPipeline(unet=_UpperCAmelCase						, scheduler=_UpperCAmelCase   )
       ddpm.to(_UpperCAmelCase   )
       ddpm.set_progress_bar_config(disable=_UpperCAmelCase   )

       snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(0   )
       snake_case_			= ddpm(generator=_UpperCAmelCase						, num_inference_steps=2						, output_type='''numpy'''   ).images

       snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(0   )
       snake_case_			= ddpm(generator=_UpperCAmelCase						, num_inference_steps=2						, output_type='''numpy'''						, return_dict=_UpperCAmelCase   )[0]

       snake_case_			= image[0, -3:, -3:, -1]
       snake_case_			= image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case_			= np.array(
           [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-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 		UpperCamelCase__	(      self   ):
       snake_case_			= self.dummy_uncond_unet
       snake_case_			= DDPMScheduler(prediction_type='''sample'''   )

       snake_case_			= DDPMPipeline(unet=_UpperCAmelCase						, scheduler=_UpperCAmelCase   )
       ddpm.to(_UpperCAmelCase   )
       ddpm.set_progress_bar_config(disable=_UpperCAmelCase   )

       snake_case_			= torch.manual_seed(0   )
       snake_case_			= ddpm(generator=_UpperCAmelCase						, num_inference_steps=2						, output_type='''numpy'''   ).images

       snake_case_			= torch.manual_seed(0   )
       snake_case_			= ddpm(generator=_UpperCAmelCase						, num_inference_steps=2						, output_type='''numpy'''   )[0]

       snake_case_			= image[0, -3:, -3:, -1]
       snake_case_			= image_eps[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case_			= 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 lowerCAmelCase_		(					unittest.TestCase			):

 '''simple docstring'''

 def 		UpperCamelCase__	(      self   ):
       snake_case_			= '''google/ddpm-cifar10-32'''

       snake_case_			= UNetaDModel.from_pretrained(_UpperCAmelCase   )
       snake_case_			= DDPMScheduler.from_pretrained(_UpperCAmelCase   )

       snake_case_			= DDPMPipeline(unet=_UpperCAmelCase						, scheduler=_UpperCAmelCase   )
       ddpm.to(_UpperCAmelCase   )
       ddpm.set_progress_bar_config(disable=_UpperCAmelCase   )

       snake_case_			= torch.manual_seed(0   )
       snake_case_			= ddpm(generator=_UpperCAmelCase						, output_type='''numpy'''   ).images

       snake_case_			= image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case_			= np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]   )
       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 lowerCAmelCase_		(					unittest.TestCase   ,		lowerCamelCase__			):

 '''simple docstring'''

 def 		UpperCamelCase__	(      self   ):
       snake_case_			= load_tool('''image-question-answering'''   )
       self.tool.setup()
       snake_case_			= load_tool('''image-question-answering'''						, remote=_UpperCAmelCase   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
       snake_case_			= self.tool(_UpperCAmelCase						, '''How many cats are sleeping on the couch?'''   )
       self.assertEqual(_UpperCAmelCase						, '''2'''   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
       snake_case_			= self.remote_tool(_UpperCAmelCase						, '''How many cats are sleeping on the couch?'''   )
       self.assertEqual(_UpperCAmelCase						, '''2'''   )



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





 def 		UpperCamelCase__	(      self   ):
       snake_case_			= Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
       snake_case_			= self.remote_tool(image=_UpperCAmelCase						, question='''How many cats are sleeping on the couch?'''   )
       self.assertEqual(_UpperCAmelCase						, '''2'''   )