



"""simple docstring"""




import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DPTImageProcessor






class       UpperCAmelCase_    (							unittest.TestCase		):



      def __init__(	self	:      Union[str, Any]	,      A	:      Union[str, Any]	,      A	:      Optional[int]=7	,      A	:      Dict=3	,      A	:      List[str]=1_8	,      A	:      Tuple=3_0	,      A	:      int=4_0_0	,      A	:      List[Any]=True	,      A	:      Any=None	,      A	:      Dict=True	,      A	:      int=[0.5, 0.5, 0.5]	,      A	:      Dict=[0.5, 0.5, 0.5]	,      ):
           _UpperCAmelCase     :      int														=       size if size is not None else {"height": 1_8, "width": 1_8}
           _UpperCAmelCase     :      Any														=       parent
           _UpperCAmelCase     :      Any														=       batch_size
           _UpperCAmelCase     :      str														=       num_channels
           _UpperCAmelCase     :      Union[str, Any]														=       image_size
           _UpperCAmelCase     :      List[Any]														=       min_resolution
           _UpperCAmelCase     :      Any														=       max_resolution
           _UpperCAmelCase     :      Union[str, Any]														=       do_resize
           _UpperCAmelCase     :      str														=       size
           _UpperCAmelCase     :      Optional[int]														=       do_normalize
           _UpperCAmelCase     :      List[Any]														=       image_mean
           _UpperCAmelCase     :      int														=       image_std



      def 		snake_case_   (	self	:      Optional[Any]		):
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
           }







@require_torch
@require_vision
class       UpperCAmelCase_    (							_UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Any						=					DPTImageProcessor if is_vision_available() else None



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      List[Any]														=       DPTImageProcessingTester(self		)



      @property
      def 		snake_case_   (	self	:      List[str]		):
           return self.image_processor_tester.prepare_image_processor_dict()



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      List[Any]														=       self.image_processing_class(**self.image_processor_dict		)
           self.assertTrue(hasattr(A	,      "image_mean"		)		)
           self.assertTrue(hasattr(A	,      "image_std"		)		)
           self.assertTrue(hasattr(A	,      "do_normalize"		)		)
           self.assertTrue(hasattr(A	,      "do_resize"		)		)
           self.assertTrue(hasattr(A	,      "size"		)		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Optional[int]														=       self.image_processing_class.from_dict(self.image_processor_dict		)
           self.assertEqual(image_processor.size	,      {"height": 1_8, "width": 1_8}		)

           _UpperCAmelCase     :      int														=       self.image_processing_class.from_dict(self.image_processor_dict	,      size=4_2		)
           self.assertEqual(image_processor.size	,      {"height": 4_2, "width": 4_2}		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           # Initialize image_processing
           _UpperCAmelCase     :      str														=       self.image_processing_class(**self.image_processor_dict		)
           # create random PIL images
           _UpperCAmelCase     :      Optional[int]														=       prepare_image_inputs(self.image_processor_tester	,      equal_resolution=A		)
           for image in image_inputs:
                self.assertIsInstance(A	,      Image.Image		)

           # Test not batched input
           _UpperCAmelCase     :      List[Any]														=       image_processing(image_inputs[0]	,      return_tensors="pt"		).pixel_values
           self.assertEqual(
               encoded_images.shape	,      (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )	,      )

           # Test batched
           _UpperCAmelCase     :      str														=       image_processing(A	,      return_tensors="pt"		).pixel_values
           self.assertEqual(
               encoded_images.shape	,      (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )	,      )



      def 		snake_case_   (	self	:      List[str]		):
           # Initialize image_processing
           _UpperCAmelCase     :      Optional[Any]														=       self.image_processing_class(**self.image_processor_dict		)
           # create random numpy tensors
           _UpperCAmelCase     :      str														=       prepare_image_inputs(self.image_processor_tester	,      equal_resolution=A	,      numpify=A		)
           for image in image_inputs:
                self.assertIsInstance(A	,      np.ndarray		)

           # Test not batched input
           _UpperCAmelCase     :      Optional[int]														=       image_processing(image_inputs[0]	,      return_tensors="pt"		).pixel_values
           self.assertEqual(
               encoded_images.shape	,      (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )	,      )

           # Test batched
           _UpperCAmelCase     :      Dict														=       image_processing(A	,      return_tensors="pt"		).pixel_values
           self.assertEqual(
               encoded_images.shape	,      (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )	,      )



      def 		snake_case_   (	self	:      Optional[Any]		):
           # Initialize image_processing
           _UpperCAmelCase     :      List[str]														=       self.image_processing_class(**self.image_processor_dict		)
           # create random PyTorch tensors
           _UpperCAmelCase     :      int														=       prepare_image_inputs(self.image_processor_tester	,      equal_resolution=A	,      torchify=A		)
           for image in image_inputs:
                self.assertIsInstance(A	,      torch.Tensor		)

           # Test not batched input
           _UpperCAmelCase     :      Tuple														=       image_processing(image_inputs[0]	,      return_tensors="pt"		).pixel_values
           self.assertEqual(
               encoded_images.shape	,      (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )	,      )

           # Test batched
           _UpperCAmelCase     :      Optional[Any]														=       image_processing(A	,      return_tensors="pt"		).pixel_values
           self.assertEqual(
               encoded_images.shape	,      (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )	,      )





"""simple docstring"""




def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  bool:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       0
     for ch in input_str:
          _UpperCAmelCase     :      Optional[int]														=       ord(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      int														=       pow(2		,    SCREAMING_SNAKE_CASE__							)

          # If we already turned on bit for current character's unicode
          if bitmap >> ch_unicode & 1 == 1:
               return False
          bitmap |= ch_bit_index_on
     return True


if __name__ == "__main__":
 import doctest

 doctest.testmod()
