


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 						UpperCamelCase__					(     *_UpperCAmelCase		,				**_UpperCAmelCase ):
										pass






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




						"""simple docstring"""

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







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class lowerCAmelCase_						(			unittest.TestCase	):

	'''simple docstring'''

	__snake_case												=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
							snake_case_	=				DepthEstimationPipeline(model=_UpperCAmelCase		,				image_processor=_UpperCAmelCase )
							return depth_estimator, [
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							]



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

							snake_case_	=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''		,				'''image'''		,				split='''test''' )
							snake_case_	=				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 )},
							    ]		,				_UpperCAmelCase		,				)



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



	@slow
	@require_torch
	def 						UpperCamelCase__					(     self ):
							snake_case_	=				'''Intel/dpt-large'''
							snake_case_	=				pipeline('''depth-estimation'''		,				model=_UpperCAmelCase )
							snake_case_	=				depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
							snake_case_	=				hashimage(outputs['''depth'''] )

							# This seems flaky.
							# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
							self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() )		,				29.304 )
							self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() )		,				2.662 )





	@require_torch
	def 						UpperCamelCase__					(     self ):
							# This is highly irregular to have no small tests.
							self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' )



# Function to print upper half of diamond (pyramid)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      for i in range(0 , SCREAMING_SNAKE_CASE ):
            for _ in range(0 , n - i - 1 ):  # printing spaces
                  print(''' ''' , end='''''' )
            for _ in range(0 , i + 1 ):  # printing stars
                  print('''* ''' , end='''''' )
            print()






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      for i in range(SCREAMING_SNAKE_CASE , 0 , -1 ):
            for _ in range(SCREAMING_SNAKE_CASE , 0 , -1 ):  # printing stars
                  print('''* ''' , end='''''' )
            print()
            for _ in range(n - i + 1 , 0 , -1 ):  # printing spaces
                  print(''' ''' , end='''''' )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      if n <= 0:
            print('''       ...       ....        nothing printing :(''' )
            return
      floyd(SCREAMING_SNAKE_CASE )  # upper half
      reverse_floyd(SCREAMING_SNAKE_CASE )  # lower half


if __name__ == "__main__":
   print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
   print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
   UpperCAmelCase      =   1
   while K:
      UpperCAmelCase      =   int(input("""enter the number and , and see the magic : """))
      print()
      pretty_print(user_number)
      UpperCAmelCase      =   int(input("""press 0 to exit... and 1 to continue..."""))

   print("""Good Bye...""")