


"""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 					__magic_name__		:






								@staticmethod
								def  __snake_case						(   *snake_case__  :    List[str]  ,     **snake_case__  :    Dict				):

															'''simple docstring'''






															pass






def       lowerCamelCase					(a_						:Image)     ->							str:
							lowercase		:Any       							=				hashlib.mda(image.tobytes())
							return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__magic_name__		(			unittest.TestCase     ):
							__A			:      Optional[int]        =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






							def  __snake_case						(   self  :    int  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    int  ,     snake_case__  :    Dict				):

														'''simple docstring'''






														lowercase		:Optional[int]       							=				DepthEstimationPipeline(model=snake_case__  ,     image_processor=snake_case__				)
														return depth_estimator, [
														    "./tests/fixtures/tests_samples/COCO/000000039769.png",
														    "./tests/fixtures/tests_samples/COCO/000000039769.png",
														]






							def  __snake_case						(   self  :    str  ,     snake_case__  :    Tuple  ,     snake_case__  :    Union[str, Any]				):

														'''simple docstring'''






														lowercase		:Optional[int]       							=				depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
														self.assertEqual({'''predicted_depth''': ANY(torch.Tensor				), '''depth''': ANY(Image.Image				)}  ,     snake_case__				)
														import datasets

														lowercase		:Optional[Any]       							=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''  ,     '''image'''  ,     split='''test'''				)
														lowercase		:str       							=				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				)},
														    ]  ,     snake_case__  ,     )






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

														'''simple docstring'''






														pass






							@slow
							@require_torch
							def  __snake_case						(   self  :    Optional[int]				):

														'''simple docstring'''






														lowercase		:Dict       							=				'''Intel/dpt-large'''
														lowercase		:int       							=				pipeline('''depth-estimation'''  ,     model=snake_case__				)
														lowercase		:int       							=				depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''				)
														lowercase		:List[str]       							=				hashimage(outputs['''depth''']				)

														# This seems flaky.
														# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
														self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()				)  ,     29.3_04				)
														self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()				)  ,     2.6_62				)







							@require_torch
							def  __snake_case						(   self  :    int				):

														'''simple docstring'''






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





"""simple docstring"""



import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
 import jax
@require_flax
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      str        =   FlaxAutoencoderKL






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:Dict       							=				4
              lowercase		:Optional[int]       							=				3
              lowercase		:Any       							=				(3_2, 3_2)

              lowercase		:Optional[int]       							=				jax.random.PRNGKey(0				)
              lowercase		:Any       							=				jax.random.uniform(snake_case__  ,     ((batch_size, num_channels) + sizes)				)

              return {"sample": image, "prng_key": prng_key}







       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				{
                  '''block_out_channels''': [3_2, 6_4],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 4,
              }
              lowercase		:Optional[int]       							=				self.dummy_input
              return init_dict, inputs_dict

