





"""simple docstring"""





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image
else:






  class    _lowerCAmelCase  :
         """simple docstring"""




         @staticmethod
         def 	snake_case		(       *__UpperCAmelCase       ,					**__UpperCAmelCase ):



           '''simple docstring'''

           pass
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Any:





  """simple docstring"""


  lowerCAmelCase__     :Optional[Any]											=				hashlib.mda(image.tobytes()     )
  return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Any						    =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				DepthEstimationPipeline(model=lowerCAmelCase_       ,					image_processor=lowerCAmelCase_ )
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png' )
         self.assertEqual({'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )}       ,					lowerCAmelCase_ )
         import datasets

         lowerCAmelCase__     :Union[str, Any]											=				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'       ,					'image'       ,					split='test' )
         lowerCAmelCase__     :Tuple											=				depth_estimator(
             [
                 Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
                 'http://images.cocodataset.org/val2017/000000039769.jpg',
                 # RGBA
                 dataset[0]['file'],
                 # LA
                 dataset[1]['file'],
                 # L
                 dataset[2]['file'],
             ] )
         self.assertEqual(
             [
                 {'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
                 {'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
                 {'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
                 {'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
                 {'predicted_depth': ANY(torch.Tensor ), 'depth': ANY(Image.Image )},
             ]       ,					lowerCAmelCase_       ,					)






       @require_tf
       @unittest.skip('Depth estimation is not implemented in TF' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       @slow
       @require_torch
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				'Intel/dpt-large'
         lowerCAmelCase__     :Optional[Any]											=				pipeline('depth-estimation'       ,					model=lowerCAmelCase_ )
         lowerCAmelCase__     :Union[str, Any]											=				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg' )
         lowerCAmelCase__     :str											=				hashimage(outputs['depth'] )

         # This seems flaky.
         # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
         self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item() )       ,					2_9.3_0_4 )
         self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item() )       ,					2.6_62 )






       @require_torch
       def 	snake_case		(       self ):



         '''simple docstring'''

         self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT' )






'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

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







							def   __lowerCAmelCase     (    self			:    int   )     ->				Any:


														"""simple docstring"""


														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]   )

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







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]   )

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-1







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_dpmpp_2m'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=7.5				,     num_inference_steps=15				,     output_type='''np'''				,     use_karras_sigmas=lowerCAmelCase_				,     )

														_a      	=							output.images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array(
														    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]   )

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

