


'''simple docstring'''






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 MobileNetVaImageProcessor




class  a     (     unittest.TestCase       ):
 def __init__( self      ,				__magic_name__      ,				__magic_name__=7      ,				__magic_name__=3      ,				__magic_name__=18      ,				__magic_name__=30      ,				__magic_name__=4_00      ,				__magic_name__=True      ,				__magic_name__=None      ,				__magic_name__=True      ,				__magic_name__=None      ,				)					->							Union[str, Any]:
      _a			      =							size if size is not None else {'shortest_edge': 20}
      _a			      =							crop_size if crop_size is not None else {'height': 18, 'width': 18}
      _a			      =							parent
      _a			      =							batch_size
      _a			      =							num_channels
      _a			      =							image_size
      _a			      =							min_resolution
      _a			      =							max_resolution
      _a			      =							do_resize
      _a			      =							size
      _a			      =							do_center_crop
      _a			      =							crop_size




 def 							__UpperCAmelCase		( self						)					->							Dict:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }





@require_torch
@require_vision
class  a     (     _SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	MobileNetVaImageProcessor if is_vision_available() else None
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							MobileNetVaImageProcessingTester(self						)
 @property
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      return self.image_processor_tester.prepare_image_processor_dict()
 def 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(__magic_name__      ,				'do_resize'						)						)
      self.assertTrue(hasattr(__magic_name__      ,				'size'						)						)
      self.assertTrue(hasattr(__magic_name__      ,				'do_center_crop'						)						)
      self.assertTrue(hasattr(__magic_name__      ,				'crop_size'						)						)
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							self.image_processing_class.from_dict(self.image_processor_dict						)
      self.assertEqual(image_processor.size      ,				{'shortest_edge': 20}						)
      self.assertEqual(image_processor.crop_size      ,				{'height': 18, 'width': 18}						)

      _a			      =							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 							__UpperCAmelCase		( self						)					->							Any:
      pass
 def 							__UpperCAmelCase		( self						)					->							int:
      # Initialize image_processing
      _a			      =							self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      _a			      =							prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=__magic_name__						)
      for image in image_inputs:
           self.assertIsInstance(__magic_name__      ,				Image.Image						)

      # Test not batched input
      _a			      =							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
      _a			      =							image_processing(__magic_name__      ,				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 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      # Initialize image_processing
      _a			      =							self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      _a			      =							prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=__magic_name__      ,				numpify=__magic_name__						)
      for image in image_inputs:
           self.assertIsInstance(__magic_name__      ,				np.ndarray						)

      # Test not batched input
      _a			      =							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
      _a			      =							image_processing(__magic_name__      ,				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 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      # Initialize image_processing
      _a			      =							self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      _a			      =							prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=__magic_name__      ,				torchify=__magic_name__						)
      for image in image_inputs:
           self.assertIsInstance(__magic_name__      ,				torch.Tensor						)

      # Test not batched input
      _a			      =							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
      _a			      =							image_processing(__magic_name__      ,				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'],
          )      ,				)





'''simple docstring'''






class  a     :
 def __init__( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							n
      _a			      =							[None] * self.n
      _a			      =							0  # index of the first element
      _a			      =							0
      _a			      =							0
 def __len__( self						)					->							int:
      return self.size
 def 							__UpperCAmelCase		( self						)					->							bool:
      return self.size == 0
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return False if self.is_empty() else self.array[self.front]
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[Any]:
      if self.size >= self.n:
           raise Exception('QUEUE IS FULL'						)

      _a			      =							data
      _a			      =							(self.rear + 1) % self.n
      self.size += 1
      return self




 def 							__UpperCAmelCase		( self						)					->							Any:
      if self.size == 0:
           raise Exception('UNDERFLOW'						)

      _a			      =							self.array[self.front]
      _a			      =							None
      _a			      =							(self.front + 1) % self.n
      self.size -= 1
      return temp

