


'''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 	UpperCamelCase__					:



            """simple docstring"""






            @staticmethod
            def a	( *snake_case__      ,	**snake_case__   ):

                   '''simple docstring'''

                   pass






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							List[str]   			=       hashlib.mda(image.tobytes()					)
       return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							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 a	( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							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

            _lowerCAmelCase    :							List[str]   			=       datasets.load_dataset('hf-internal-testing/fixtures_image_utils'      ,	'image'      ,	split='test'   )
            _lowerCAmelCase    :							List[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 a	( self   ):

            '''simple docstring'''

            pass







     @slow
     @require_torch
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       'Intel/dpt-large'
            _lowerCAmelCase    :							Any   			=       pipeline('depth-estimation'      ,	model=snake_case__   )
            _lowerCAmelCase    :							Optional[int]   			=       depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'   )
            _lowerCAmelCase    :							List[str]   			=       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 a	( self   ):

            '''simple docstring'''

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





'''simple docstring'''







from collections import Counter
from timeit import timeit






def 			lowercase				(_A = ""   ,						):







       """simple docstring"""







       return sum(c % 2 for c in Counter(input_str.replace(' '   ,						''					).lower()					).values()					) < 2






def 			lowercase				(_A = ""					):







       """simple docstring"""







       if len(_A					) == 0:
              return True
       _lowerCAmelCase    :							Union[str, Any]   			=       input_str.replace(' '   ,						''					).lower()
       # character_freq_dict: Stores the frequency of every character in the input string
       _lowerCAmelCase    :							dict[str, int]   			=       {}

       for character in lower_case_input_str:
              _lowerCAmelCase    :							Union[str, Any]   			=       character_freq_dict.get(_A   ,						0					) + 1
       _lowerCAmelCase    :							List[Any]   			=       0

       for character_count in character_freq_dict.values():
              if character_count % 2:
                     odd_char += 1
       if odd_char > 1:
              return False
       return True






def 			lowercase				(_A = ""					):







       """simple docstring"""







       print('\nFor string = '   ,						_A   ,						':'					)
       print(
           '> can_string_be_rearranged_as_palindrome_counter()'   ,						'\tans ='   ,						can_string_be_rearranged_as_palindrome_counter(_A					)   ,						'\ttime ='   ,						timeit(
               'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'   ,						setup='import __main__ as z'   ,						)   ,						'seconds'   ,						)
       print(
           '> can_string_be_rearranged_as_palindrome()'   ,						'\tans ='   ,						can_string_be_rearranged_as_palindrome(_A					)   ,						'\ttime ='   ,						timeit(
               'z.can_string_be_rearranged_as_palindrome(z.check_str)'   ,						setup='import __main__ as z'   ,						)   ,						'seconds'   ,						)


if __name__ == "__main__":
       lowerCAmelCase    :     Tuple					=						input(
           """Enter string to determine if it can be rearranged as a palindrome or not: """
       ).strip()
       benchmark(check_str)
       lowerCAmelCase    :     Optional[Any]					=						can_string_be_rearranged_as_palindrome_counter(check_str)
       print(F'''{check_str} can {"" if status else "not "}be rearranged as a palindrome''')

