


"""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 EfficientNetImageProcessor






class 		_A    (    unittest.TestCase    ):







    """simple docstring"""




    def __init__(   self							:       Tuple							,					__UpperCAmelCase							:       Dict							,					__UpperCAmelCase							:       List[str]=13							,					__UpperCAmelCase							:       Dict=3							,					__UpperCAmelCase							:       Dict=18							,					__UpperCAmelCase							:       Tuple=30							,					__UpperCAmelCase							:       str=400							,					__UpperCAmelCase							:       str=True							,					__UpperCAmelCase							:       List[str]=None							,					__UpperCAmelCase							:       Union[str, Any]=True							,					__UpperCAmelCase							:       Optional[int]=[0.5, 0.5, 0.5]							,					__UpperCAmelCase							:       List[str]=[0.5, 0.5, 0.5]							,					):
      a		:						Any 				=     size if size is not None else {"height": 18, "width": 18}
      a		:						str 				=     parent
      a		:						Optional[Any] 				=     batch_size
      a		:						Optional[int] 				=     num_channels
      a		:						int 				=     image_size
      a		:						List[Any] 				=     min_resolution
      a		:						Union[str, Any] 				=     max_resolution
      a		:						Any 				=     do_resize
      a		:						Union[str, Any] 				=     size
      a		:						List[Any] 				=     do_normalize
      a		:						Optional[int] 				=     image_mean
      a		:						int 				=     image_std






    def       __snake_case				(   self							:       Optional[int]):
      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 		_A    (    lowercase_       ,unittest.TestCase    ):







    """simple docstring"""




    UpperCAmelCase			:				str      =			EfficientNetImageProcessor if is_vision_available() else None






    def       __snake_case				(   self							:       int):
      a		:						Union[str, Any] 				=     EfficientNetImageProcessorTester(self)






    @property
    def       __snake_case				(   self							:       Dict):
      return self.image_processor_tester.prepare_image_processor_dict()






    def       __snake_case				(   self							:       Union[str, Any]):
      a		:						Tuple 				=     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							:       Union[str, Any]):
      a		:						Dict 				=     self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size							,					{"height": 18, "width": 18})

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






    def       __snake_case				(   self							:       Tuple):
      # Initialize image_processing
      a		:						Any 				=     self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      a		:						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
      a		:						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
      a		:						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"],
          )							,					)






    def       __snake_case				(   self							:       Optional[int]):
      # Initialize image_processing
      a		:						Optional[int] 				=     self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a		:						int 				=     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
      a		:						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
      a		:						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"],
          )							,					)






    def       __snake_case				(   self							:       Tuple):
      # Initialize image_processing
      a		:						str 				=     self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a		:						List[str] 				=     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
      a		:						Dict 				=     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
      a		:						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"],
          )							,					)








import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


_UpperCAmelCase    			=					logging.get_logger(__name__)

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  def __init__(					self	,		*a__	,		**a__		):
    warnings.warn(
        '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
        ''' Please use ConditionalDetrImageProcessor instead.'''	,		a__	,		)
    super().__init__(*a__	,		**a__		)




