






'''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 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def __init__(					self      : Optional[Any]	,    A__      : Union[str, Any]	,    A__      : str=7	,    A__      : List[str]=3	,    A__      : Tuple=18	,    A__      : Union[str, Any]=30	,    A__      : Optional[Any]=400	,    A__      : List[str]=True	,    A__      : Dict=None	,    A__      : List[str]=True	,    A__      : Dict=[0.5, 0.5, 0.5]	,    A__      : int=[0.5, 0.5, 0.5]	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				size if size is not None else {"""height""": 18, """width""": 18}
            __lowerCamelCase						:       Tuple				   =				parent
            __lowerCamelCase						:       int				   =				batch_size
            __lowerCamelCase						:       List[str]				   =				num_channels
            __lowerCamelCase						:       int				   =				image_size
            __lowerCamelCase						:       Dict				   =				min_resolution
            __lowerCamelCase						:       int				   =				max_resolution
            __lowerCamelCase						:       Optional[Any]				   =				do_resize
            __lowerCamelCase						:       Dict				   =				size
            __lowerCamelCase						:       str				   =				do_normalize
            __lowerCamelCase						:       int				   =				image_mean
            __lowerCamelCase						:       int				   =				image_std




       def 					a_					(					self      : Any					):


            """simple docstring"""







            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 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     List[Any]   		=			DPTImageProcessor if is_vision_available() else None




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				DPTImageProcessingTester(self					)




       @property
       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				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 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size	,    {"""height""": 18, """width""": 18}					)

            __lowerCamelCase						:       str				   =				self.image_processing_class.from_dict(self.image_processor_dict	,    size=42					)
            self.assertEqual(image_processor.size	,    {"""height""": 42, """width""": 42}					)




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            __lowerCamelCase						:       Tuple				   =				prepare_image_inputs(self.image_processor_tester	,    equal_resolution=A__					)
            for image in image_inputs:
                 self.assertIsInstance(A__	,    Image.Image					)

            # Test not batched input
            __lowerCamelCase						:       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
            __lowerCamelCase						:       Optional[int]				   =				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 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            __lowerCamelCase						:       Union[str, Any]				   =				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
            __lowerCamelCase						:       Union[str, 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
            __lowerCamelCase						:       int				   =				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 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            __lowerCamelCase						:       Any				   =				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
            __lowerCamelCase						:       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
            __lowerCamelCase						:       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"""],
                )	,    )











'''simple docstring'''

def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # bit count represents no. of bits in the gray code
     if bit_count < 0:
          raise ValueError("""The given input must be positive"""     )

     # get the generated string sequence
     __lowerCamelCase						:       Optional[Any]				   =				gray_code_sequence_string(_lowercase     )
     #
     # convert them to integers
     for i in range(len(_lowercase     )     ):
          __lowerCamelCase						:       List[str]				   =				int(sequence[i],						2     )

     return sequence
def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # The approach is a recursive one
     # Base case achieved when either n = 0 or n=1
     if bit_count == 0:
          return ["0"]

     if bit_count == 1:
          return ["0", "1"]

     __lowerCamelCase						:       str				   =				1 << bit_count  # defines the length of the sequence
     # 1<< n is equivalent to 2^n

     # recursive answer will generate answer for n-1 bits
     __lowerCamelCase						:       Union[str, Any]				   =				gray_code_sequence_string(bit_count - 1     )

     __lowerCamelCase						:       Optional[int]				   =				[]

     # append 0 to first half of the smaller sequence generated
     for i in range(seq_len // 2     ):
          __lowerCamelCase						:       Union[str, Any]				   =				"""0""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     # append 1 to second half ... start from the end of the list
     for i in reversed(range(seq_len // 2     )     ):
          __lowerCamelCase						:       Optional[Any]				   =				"""1""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()



