
import unittest

import numpy as np

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

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 LevitImageProcessor




class        _SCREAMING_SNAKE_CASE	( unittest.TestCase		):



	def __init__(       self		,    lowercase		,    lowercase=7		,    lowercase=3		,    lowercase=18		,    lowercase=30		,    lowercase=400		,    lowercase=True		,    lowercase=None		,    lowercase=True		,    lowercase=None		,    lowercase=True		,    lowercase=[0.5, 0.5, 0.5]		,    lowercase=[0.5, 0.5, 0.5]		,    )							->			str:
					lowerCamelCase_         =     size if size is not None else {"shortest_edge": 18}
					lowerCamelCase_         =     crop_size if crop_size is not None else {"height": 18, "width": 18}
					lowerCamelCase_         =     parent
					lowerCamelCase_         =     batch_size
					lowerCamelCase_         =     num_channels
					lowerCamelCase_         =     image_size
					lowerCamelCase_         =     min_resolution
					lowerCamelCase_         =     max_resolution
					lowerCamelCase_         =     do_resize
					lowerCamelCase_         =     size
					lowerCamelCase_         =     do_center_crop
					lowerCamelCase_         =     crop_size
					lowerCamelCase_         =     do_normalize
					lowerCamelCase_         =     image_mean
					lowerCamelCase_         =     image_std



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "do_center_crop": self.do_center_crop,
					    "size": self.size,
					    "crop_size": self.crop_size,
					}




@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE	( __snake_case     ,			unittest.TestCase		):
	lowerCAmelCase__   =					LevitImageProcessor if is_vision_available() else None



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					lowerCamelCase_         =     LevitImageProcessingTester(self					)



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					return self.image_processor_tester.prepare_image_processor_dict()



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					lowerCamelCase_         =     self.image_processing_class(**self.image_processor_dict					)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,    "image_mean"					)					)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,    "image_std"					)					)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,    "do_normalize"					)					)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,    "do_resize"					)					)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,    "do_center_crop"					)					)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,    "size"					)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					lowerCamelCase_         =     self.image_processing_class.from_dict(self.image_processor_dict					)
					self.assertEqual(image_processor.size		,    {"shortest_edge": 18}					)
					self.assertEqual(image_processor.crop_size		,    {"height": 18, "width": 18}					)

					lowerCamelCase_         =     self.image_processing_class.from_dict(self.image_processor_dict		,    size=42		,    crop_size=84					)
					self.assertEqual(image_processor.size		,    {"shortest_edge": 42}					)
					self.assertEqual(image_processor.crop_size		,    {"height": 84, "width": 84}					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					pass



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					lowerCamelCase_         =     self.image_processing_class(**self.image_processor_dict					)
					# create random PIL images
					lowerCamelCase_         =     prepare_image_inputs(self.image_processor_tester		,    equal_resolution=_SCREAMING_SNAKE_CASE					)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE		,    Image.Image					)

					# Test not batched input
					lowerCamelCase_         =     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.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )		,    )

					# Test batched
					lowerCamelCase_         =     image_processing(_SCREAMING_SNAKE_CASE		,    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.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )		,    )



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					lowerCamelCase_         =     self.image_processing_class(**self.image_processor_dict					)
					# create random numpy tensors
					lowerCamelCase_         =     prepare_image_inputs(self.image_processor_tester		,    equal_resolution=_SCREAMING_SNAKE_CASE		,    numpify=_SCREAMING_SNAKE_CASE					)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE		,    np.ndarray					)

					# Test not batched input
					lowerCamelCase_         =     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.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )		,    )

					# Test batched
					lowerCamelCase_         =     image_processing(_SCREAMING_SNAKE_CASE		,    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.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )		,    )



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					lowerCamelCase_         =     self.image_processing_class(**self.image_processor_dict					)
					# create random PyTorch tensors
					lowerCamelCase_         =     prepare_image_inputs(self.image_processor_tester		,    equal_resolution=_SCREAMING_SNAKE_CASE		,    torchify=_SCREAMING_SNAKE_CASE					)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE		,    torch.Tensor					)

					# Test not batched input
					lowerCamelCase_         =     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.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )		,    )

					# Test batched
					lowerCamelCase_         =     image_processing(_SCREAMING_SNAKE_CASE		,    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.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )		,    )










def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if bit_count < 0:
            raise ValueError('The given input must be positive')

      # get the generated string sequence
      UpperCamelCase					       =  gray_code_sequence_string(_UpperCamelCase)
      #
      # convert them to integers
      for i in range(len(_UpperCamelCase)):
            UpperCamelCase					       =  int(sequence[i]       ,							2)

      return sequence
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

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

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

      UpperCamelCase					       =  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
      UpperCamelCase					       =  gray_code_sequence_string(bit_count - 1)

      UpperCamelCase					       =  []

      # append 0 to first half of the smaller sequence generated
      for i in range(seq_len // 2):
            UpperCamelCase					       =  '0' + smaller_sequence[i]
            sequence.append(_UpperCamelCase)

      # append 1 to second half ... start from the end of the list
      for i in reversed(range(seq_len // 2)):
            UpperCamelCase					       =  '1' + smaller_sequence[i]
            sequence.append(_UpperCamelCase)

      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()



