





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 	A_       :
					@staticmethod
					def      _lowercase				(    *_A							,					**_A		):






								'''simple docstring'''







								pass





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				hashlib.mda(image.tobytes() )
			return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	A_       (unittest.TestCase			):
	UpperCAmelCase__					=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING
	def      _lowercase				(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				DepthEstimationPipeline(model=__SCREAMING_SNAKE_CASE							,					image_processor=__SCREAMING_SNAKE_CASE		)
				return depth_estimator, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]
	def      _lowercase				(    self							,					_A							,					_A		):






				'''simple docstring'''







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

				UpperCAmelCase   		=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''							,					'''image'''							,					split='''test'''		)
				UpperCAmelCase   		=				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		)},
				    ]							,					__SCREAMING_SNAKE_CASE							,					)
	@require_tf
	@unittest.skip('''Depth estimation is not implemented in TF'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	@slow
	@require_torch
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''Intel/dpt-large'''
				UpperCAmelCase   		=				pipeline('''depth-estimation'''							,					model=__SCREAMING_SNAKE_CASE		)
				UpperCAmelCase   		=				depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''		)
				UpperCAmelCase   		=				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      _lowercase				(    self		):






				'''simple docstring'''







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






'''simple docstring'''



from itertools import count







def    a__       (  a__ = 50						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      [1] * min_block_length

     for n in count(a__						):
          fill_count_functions.append(1						)

          for block_length in range(a__      ,    n + 1						):
               for block_start in range(n - block_length						):
                    fill_count_functions[n] += fill_count_functions[
                        n - block_start - block_length - 1
                    ]

               fill_count_functions[n] += 1

          if fill_count_functions[n] > 1_00_00_00:
               break

     return n


if __name__ == "__main__":
  print(f"""{solution() = }""")






