


"""simple docstring"""
import pickle

import numpy as np
from matplotlib import pyplot as plt




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=0.2      ,   SCREAMING_SNAKE_CASE=0.2)  ->  Tuple:
      _lowerCamelCase	:					Tuple   =		bp_numa
      _lowerCamelCase	:					Any   =		bp_numa
      _lowerCamelCase	:					Optional[int]   =		bp_numa
      _lowerCamelCase	:					Tuple   =		conva_get[:2]
      _lowerCamelCase	:					Tuple   =		conva_get[2]
      _lowerCamelCase	:					List[Any]   =		size_pa
      _lowerCamelCase	:					Union[str, Any]   =		rate_w
      _lowerCamelCase	:					Any   =		rate_t
      _lowerCamelCase	:					Optional[Any]   =		[
          np.mat(-1 * np.random.rand(self.conva[0]      ,   self.conva[0]) + 0.5)
          for i in range(self.conva[1])
      ]
      _lowerCamelCase	:					int   =		np.mat(-1 * np.random.rand(self.num_bpa      ,   self.num_bpa) + 0.5)
      _lowerCamelCase	:					List[str]   =		np.mat(-1 * np.random.rand(self.num_bpa      ,   self.num_bpa) + 0.5)
      _lowerCamelCase	:					Dict   =		-2 * np.random.rand(self.conva[1]) + 1
      _lowerCamelCase	:					Tuple   =		-2 * np.random.rand(self.num_bpa) + 1
      _lowerCamelCase	:					Dict   =		-2 * np.random.rand(self.num_bpa) + 1





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      _lowerCamelCase	:					str   =		{
          """num_bp1""": self.num_bpa,
          """num_bp2""": self.num_bpa,
          """num_bp3""": self.num_bpa,
          """conv1""": self.conva,
          """step_conv1""": self.step_conva,
          """size_pooling1""": self.size_poolinga,
          """rate_weight""": self.rate_weight,
          """rate_thre""": self.rate_thre,
          """w_conv1""": self.w_conva,
          """wkj""": self.wkj,
          """vji""": self.vji,
          """thre_conv1""": self.thre_conva,
          """thre_bp2""": self.thre_bpa,
          """thre_bp3""": self.thre_bpa,
      }
      with open(__UpperCamelCase      ,   """wb""") as f:
         pickle.dump(__UpperCamelCase      ,   __UpperCamelCase)

      print(F'Model saved： {save_path}')





   @classmethod
   def 		UpperCamelCase_  (							cls      ,   SCREAMING_SNAKE_CASE)  ->  Optional[Any]:


      with open(__UpperCamelCase      ,   """rb""") as f:
         _lowerCamelCase	:					List[Any]   =		pickle.load(__UpperCamelCase)  # noqa: S301

      _lowerCamelCase	:					List[str]   =		model_dic.get("""conv1""")
      conv_get.append(model_dic.get("""step_conv1"""))
      _lowerCamelCase	:					str   =		model_dic.get("""size_pooling1""")
      _lowerCamelCase	:					Any   =		model_dic.get("""num_bp1""")
      _lowerCamelCase	:					str   =		model_dic.get("""num_bp2""")
      _lowerCamelCase	:					Optional[int]   =		model_dic.get("""num_bp3""")
      _lowerCamelCase	:					List[Any]   =		model_dic.get("""rate_weight""")
      _lowerCamelCase	:					List[str]   =		model_dic.get("""rate_thre""")
      # create model instance
      _lowerCamelCase	:					Dict   =		CNN(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase)
      # modify model parameter
      _lowerCamelCase	:					Dict   =		model_dic.get("""w_conv1""")
      _lowerCamelCase	:					Any   =		model_dic.get("""wkj""")
      _lowerCamelCase	:					str   =		model_dic.get("""vji""")
      _lowerCamelCase	:					Union[str, Any]   =		model_dic.get("""thre_conv1""")
      _lowerCamelCase	:					Tuple   =		model_dic.get("""thre_bp2""")
      _lowerCamelCase	:					Tuple   =		model_dic.get("""thre_bp3""")
      return conv_ins





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      return 1 / (1 + np.exp(-1 * x))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      return round(__UpperCamelCase      ,   3)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      _lowerCamelCase	:					Union[str, Any]   =		convs[0]
      _lowerCamelCase	:					Dict   =		convs[1]
      _lowerCamelCase	:					str   =		np.shape(__UpperCamelCase)[0]
      # get the data slice of original image data, data_focus
      _lowerCamelCase	:					Dict   =		[]
      for i_focus in range(0      ,   size_data - size_conv + 1      ,   __UpperCamelCase):
         for j_focus in range(0      ,   size_data - size_conv + 1      ,   __UpperCamelCase):
            _lowerCamelCase	:					Any   =		data[
                i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
            ]
            data_focus.append(__UpperCamelCase)
        # calculate the feature map of every single kernel, and saved as list of matrix
      _lowerCamelCase	:					Dict   =		[]
      _lowerCamelCase	:					Any   =		int((size_data - size_conv) / conv_step + 1)
      for i_map in range(__UpperCamelCase):
         _lowerCamelCase	:					Dict   =		[]
         for i_focus in range(len(__UpperCamelCase)):
            _lowerCamelCase	:					List[Any]   =		(
                np.sum(np.multiply(data_focus[i_focus]      ,   w_convs[i_map]))
                - thre_convs[i_map]
            )
            featuremap.append(self.sig(__UpperCamelCase))
         _lowerCamelCase	:					Tuple   =		np.asmatrix(__UpperCamelCase).reshape(
             __UpperCamelCase      ,   __UpperCamelCase)
         data_featuremap.append(__UpperCamelCase)

      # expanding the data slice to One dimenssion
      _lowerCamelCase	:					Union[str, Any]   =		[]
      for each_focus in data_focus:
         focusa_list.extend(self.Expand_Mat(__UpperCamelCase))
      _lowerCamelCase	:					List[str]   =		np.asarray(__UpperCamelCase)
      return focus_list, data_featuremap





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE="average_pool")  ->  Tuple:
      _lowerCamelCase	:					Optional[Any]   =		len(featuremaps[0])
      _lowerCamelCase	:					int   =		int(size_map / size_pooling)
      _lowerCamelCase	:					Union[str, Any]   =		[]
      for i_map in range(len(__UpperCamelCase)):
         _lowerCamelCase	:					Any   =		featuremaps[i_map]
         _lowerCamelCase	:					Any   =		[]
         for i_focus in range(0      ,   __UpperCamelCase      ,   __UpperCamelCase):
            for j_focus in range(0      ,   __UpperCamelCase      ,   __UpperCamelCase):
               _lowerCamelCase	:					str   =		feature_map[
                   i_focus : i_focus + size_pooling,
                   j_focus : j_focus + size_pooling,
               ]
               if pooling_type == "average_pool":
                  # average pooling
                  map_pooled.append(np.average(__UpperCamelCase))
               elif pooling_type == "max_pooling":
                  # max pooling
                  map_pooled.append(np.max(__UpperCamelCase))
         _lowerCamelCase	:					Optional[Any]   =		np.asmatrix(__UpperCamelCase).reshape(__UpperCamelCase      ,   __UpperCamelCase)
         featuremap_pooled.append(__UpperCamelCase)
      return featuremap_pooled





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					Union[str, Any]   =		[]
      for i in range(len(__UpperCamelCase)):
         _lowerCamelCase	:					List[Any]   =		np.shape(data[i])
         _lowerCamelCase	:					Tuple   =		data[i].reshape(1      ,   shapes[0] * shapes[1])
         _lowerCamelCase	:					List[Any]   =		data_listed.getA().tolist()[0]
         data_expanded.extend(__UpperCamelCase)
      _lowerCamelCase	:					List[Any]   =		np.asarray(__UpperCamelCase)
      return data_expanded





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Dict:
      _lowerCamelCase	:					int   =		np.asarray(__UpperCamelCase)
      _lowerCamelCase	:					Tuple   =		np.shape(__UpperCamelCase)
      _lowerCamelCase	:					Optional[Any]   =		data_mat.reshape(1      ,   shapes[0] * shapes[1])
      return data_expanded





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      _lowerCamelCase	:					Any   =		[]
      _lowerCamelCase	:					List[Any]   =		0
      for i_map in range(__UpperCamelCase):
         _lowerCamelCase	:					Any   =		np.ones((size_map, size_map))
         for i in range(0      ,   __UpperCamelCase      ,   __UpperCamelCase):
            for j in range(0      ,   __UpperCamelCase      ,   __UpperCamelCase):
               _lowerCamelCase	:					int   =		pd_pool[
                   i_pool
               ]
               _lowerCamelCase	:					Any   =		i_pool + 1
         _lowerCamelCase	:					Union[str, Any]   =		np.multiply(
             __UpperCamelCase      ,   np.multiply(out_map[i_map]      ,   (1 - out_map[i_map])))
         pd_all.append(__UpperCamelCase)
      return pd_all





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=bool)  ->  Optional[int]:
      print("""----------------------Start Training-------------------------""")
      print((""" - - Shape: Train_Data  """, np.shape(__UpperCamelCase)))
      print((""" - - Shape: Teach_Data  """, np.shape(__UpperCamelCase)))
      _lowerCamelCase	:					List[str]   =		0
      _lowerCamelCase	:					Optional[int]   =		[]
      _lowerCamelCase	:					Optional[Any]   =		1_0000
      while rp < n_repeat and mse >= error_accuracy:
         _lowerCamelCase	:					Union[str, Any]   =		0
         print(F'-------------Learning Time {rp}--------------')
         for p in range(len(__UpperCamelCase)):
            # print('------------Learning Image: %d--------------'%p)
            _lowerCamelCase	:					Dict   =		np.asmatrix(datas_train[p])
            _lowerCamelCase	:					Optional[Any]   =		np.asarray(datas_teach[p])
            _lowerCamelCase   ,    _lowerCamelCase	:					Any   =		self.convolute(
                __UpperCamelCase      ,   self.conva      ,   self.w_conva      ,   self.thre_conva      ,   conv_step=self.step_conva      ,   )
            _lowerCamelCase	:					Union[str, Any]   =		self.pooling(__UpperCamelCase      ,   self.size_poolinga)
            _lowerCamelCase	:					List[str]   =		np.shape(__UpperCamelCase)
            _lowerCamelCase	:					Any   =		self._expand(__UpperCamelCase)
            _lowerCamelCase	:					Any   =		data_bp_input

            _lowerCamelCase	:					Any   =		np.dot(__UpperCamelCase      ,   self.vji.T) - self.thre_bpa
            _lowerCamelCase	:					List[Any]   =		self.sig(__UpperCamelCase)
            _lowerCamelCase	:					Optional[Any]   =		np.dot(__UpperCamelCase      ,   self.wkj.T) - self.thre_bpa
            _lowerCamelCase	:					int   =		self.sig(__UpperCamelCase)

            # --------------Model Leaning ------------------------
            # calculate error and gradient---------------
            _lowerCamelCase	:					Union[str, Any]   =		np.multiply(
                (data_teach - bp_outa)      ,   np.multiply(__UpperCamelCase      ,   (1 - bp_outa)))
            _lowerCamelCase	:					Any   =		np.multiply(
                np.dot(__UpperCamelCase      ,   self.wkj)      ,   np.multiply(__UpperCamelCase      ,   (1 - bp_outa)))
            _lowerCamelCase	:					Tuple   =		np.dot(__UpperCamelCase      ,   self.vji)

            _lowerCamelCase	:					str   =		pd_i_all / (self.size_poolinga * self.size_poolinga)
            _lowerCamelCase	:					int   =		pd_conva_pooled.T.getA().tolist()
            _lowerCamelCase	:					Union[str, Any]   =		self._calculate_gradient_from_pool(
                __UpperCamelCase      ,   __UpperCamelCase      ,   shape_featuremapa[0]      ,   shape_featuremapa[1]      ,   self.size_poolinga      ,   )
            # weight and threshold learning process---------
            # convolution layer
            for k_conv in range(self.conva[1]):
               _lowerCamelCase	:					List[str]   =		self._expand_mat(pd_conva_all[k_conv])
               _lowerCamelCase	:					Union[str, Any]   =		self.rate_weight * np.dot(__UpperCamelCase      ,   __UpperCamelCase)

               _lowerCamelCase	:					Any   =		self.w_conva[k_conv] + delta_w.reshape(
                   (self.conva[0], self.conva[0]))

               _lowerCamelCase	:					Tuple   =		(
                   self.thre_conva[k_conv]
                   - np.sum(pd_conva_all[k_conv]) * self.rate_thre
               )
            # all connected layer
            _lowerCamelCase	:					Tuple   =		self.wkj + pd_k_all.T * bp_outa * self.rate_weight
            _lowerCamelCase	:					List[str]   =		self.vji + pd_j_all.T * bp_outa * self.rate_weight
            _lowerCamelCase	:					Union[str, Any]   =		self.thre_bpa - pd_k_all * self.rate_thre
            _lowerCamelCase	:					Dict   =		self.thre_bpa - pd_j_all * self.rate_thre
            # calculate the sum error of all single image
            _lowerCamelCase	:					Union[str, Any]   =		np.sum(abs(data_teach - bp_outa))
            error_count += errors
            # print('   ----Teach      ',data_teach)
            # print('   ----BP_output  ',bp_out3)
         _lowerCamelCase	:					Optional[Any]   =		rp + 1
         _lowerCamelCase	:					str   =		error_count / patterns
         all_mse.append(__UpperCamelCase)

      def draw_error():
         _lowerCamelCase	:					List[Any]   =		[error_accuracy for i in range(int(n_repeat * 1.2))]
         plt.plot(__UpperCamelCase      ,   """+-""")
         plt.plot(__UpperCamelCase      ,   """r--""")
         plt.xlabel("""Learning Times""")
         plt.ylabel("""All_mse""")
         plt.grid(__UpperCamelCase      ,   alpha=0.5)
         plt.show()

      print("""------------------Training Complished---------------------""")
      print((""" - - Training epoch: """, rp, F'     - - Mse: {mse:.6f}'))
      if draw_e:
         draw_error()
      return mse





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      _lowerCamelCase	:					List[str]   =		[]
      print("""-------------------Start Testing-------------------------""")
      print((""" - - Shape: Test_Data  """, np.shape(__UpperCamelCase)))
      for p in range(len(__UpperCamelCase)):
         _lowerCamelCase	:					Optional[Any]   =		np.asmatrix(datas_test[p])
         _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		self.convolute(
             __UpperCamelCase      ,   self.conva      ,   self.w_conva      ,   self.thre_conva      ,   conv_step=self.step_conva      ,   )
         _lowerCamelCase	:					int   =		self.pooling(__UpperCamelCase      ,   self.size_poolinga)
         _lowerCamelCase	:					List[str]   =		self._expand(__UpperCamelCase)

         _lowerCamelCase	:					Optional[Any]   =		data_bp_input
         _lowerCamelCase	:					Dict   =		bp_outa * self.vji.T - self.thre_bpa
         _lowerCamelCase	:					Any   =		self.sig(__UpperCamelCase)
         _lowerCamelCase	:					Optional[Any]   =		bp_outa * self.wkj.T - self.thre_bpa
         _lowerCamelCase	:					Union[str, Any]   =		self.sig(__UpperCamelCase)
         produce_out.extend(bp_outa.getA().tolist())
      _lowerCamelCase	:					Optional[int]   =		[list(map(self.do_round      ,   __UpperCamelCase)) for each in produce_out]
      return np.asarray(__UpperCamelCase)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      _lowerCamelCase	:					int   =		np.asmatrix(__UpperCamelCase)
      _lowerCamelCase   ,    _lowerCamelCase	:					int   =		self.convolute(
          __UpperCamelCase      ,   self.conva      ,   self.w_conva      ,   self.thre_conva      ,   conv_step=self.step_conva      ,   )
      _lowerCamelCase	:					List[Any]   =		self.pooling(__UpperCamelCase      ,   self.size_poolinga)

      return data_conveda, data_pooleda


if __name__ == "__main__":
 pass




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor


class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase=7			,					__UpperCamelCase=3			,					__UpperCamelCase=30			,					__UpperCamelCase=4_00			,					__UpperCamelCase=True			,					__UpperCamelCase=None			,					__UpperCamelCase=True			,					__UpperCamelCase=[0.5, 0.5, 0.5]			,					__UpperCamelCase=[0.5, 0.5, 0.5]			,					__UpperCamelCase=True			,					__UpperCamelCase=1 / 2_55			,					__UpperCamelCase=True			,					):

				"""simple docstring"""


				snake_case_   		=			size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
				snake_case_   		=			parent
				snake_case_   		=			batch_size
				snake_case_   		=			num_channels
				snake_case_   		=			min_resolution
				snake_case_   		=			max_resolution
				snake_case_   		=			do_resize
				snake_case_   		=			size
				snake_case_   		=			do_normalize
				snake_case_   		=			image_mean
				snake_case_   		=			image_std
				snake_case_   		=			do_rescale
				snake_case_   		=			rescale_factor
				snake_case_   		=			do_pad


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_pad": self.do_pad,
				}







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=False				):

				"""simple docstring"""


				if not batched:
					snake_case_   		=			image_inputs[0]
					if isinstance(__UpperCamelCase			,					Image.Image				):
						snake_case_		,							snake_case_   		=			image.size
					else:
						snake_case_		,							snake_case_   		=			image.shape[1], image.shape[2]
					if w < h:
						snake_case_   		=			int(self.size['shortest_edge'] * h / w				)
						snake_case_   		=			self.size['shortest_edge']
					elif w > h:
						snake_case_   		=			self.size['shortest_edge']
						snake_case_   		=			int(self.size['shortest_edge'] * w / h				)
					else:
						snake_case_   		=			self.size['shortest_edge']
						snake_case_   		=			self.size['shortest_edge']

				else:
					snake_case_   		=			[]
					for image in image_inputs:
						snake_case_		,							snake_case_   		=			self.get_expected_values([image]				)
						expected_values.append((expected_height, expected_width)				)
					snake_case_   		=			max(__UpperCamelCase			,					key=lambda __UpperCamelCase				: item[0]				)[0]
					snake_case_   		=			max(__UpperCamelCase			,					key=lambda __UpperCamelCase				: item[1]				)[1]

				return expected_height, expected_width







@require_torch
@require_vision
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	DetaImageProcessor if is_vision_available() else None


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			DetaImageProcessingTester(self				)


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.image_processor_tester.prepare_image_processor_dict()


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'image_mean'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'image_std'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_normalize'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_resize'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_rescale'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_pad'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'size'				)				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class.from_dict(self.image_processor_dict				)
				self.assertEqual(image_processor.size			,					{'shortest_edge': 18, 'longest_edge': 13_33}				)
				self.assertEqual(image_processor.do_pad			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				pass


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

				# Test not batched input
				snake_case_   		=			image_processing(image_inputs[0]			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase				)

				self.assertEqual(
				    encoded_images.shape			,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,					)

				# Test batched
				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase			,					batched=__UpperCamelCase				)

				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values
				self.assertEqual(
				    encoded_images.shape			,					(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )			,					)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				# create random numpy tensors
				snake_case_   		=			prepare_image_inputs(self.image_processor_tester			,					equal_resolution=__UpperCamelCase			,					numpify=__UpperCamelCase				)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase			,					np.ndarray				)

				# Test not batched input
				snake_case_   		=			image_processing(image_inputs[0]			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase				)

				self.assertEqual(
				    encoded_images.shape			,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,					)

				# Test batched
				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase			,					batched=__UpperCamelCase				)

				self.assertEqual(
				    encoded_images.shape			,					(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )			,					)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				# create random PyTorch tensors
				snake_case_   		=			prepare_image_inputs(self.image_processor_tester			,					equal_resolution=__UpperCamelCase			,					torchify=__UpperCamelCase				)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase			,					torch.Tensor				)

				# Test not batched input
				snake_case_   		=			image_processing(image_inputs[0]			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase				)

				self.assertEqual(
				    encoded_images.shape			,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,					)

				# Test batched
				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase			,					batched=__UpperCamelCase				)

				self.assertEqual(
				    encoded_images.shape			,					(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )			,					)


			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
				with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'			,					'r'				) as f:
					snake_case_   		=			json.loads(f.read()				)

				snake_case_   		=			{'image_id': 3_97_69, 'annotations': target}

				# encode them
				snake_case_   		=			DetaImageProcessor()
				snake_case_   		=			image_processing(images=__UpperCamelCase			,					annotations=__UpperCamelCase			,					return_tensors='pt'				)

				# verify pixel values
				snake_case_   		=			torch.Size([1, 3, 8_00, 10_66]				)
				self.assertEqual(encoding['pixel_values'].shape			,					__UpperCamelCase				)

				snake_case_   		=			torch.tensor([0.2796, 0.3138, 0.3481]				)
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,					__UpperCamelCase			,					atol=1E-4				)				)

				# verify area
				snake_case_   		=			torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,					__UpperCamelCase				)				)
				# verify boxes
				snake_case_   		=			torch.Size([6, 4]				)
				self.assertEqual(encoding['labels'][0]['boxes'].shape			,					__UpperCamelCase				)
				snake_case_   		=			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,					__UpperCamelCase			,					atol=1E-3				)				)
				# verify image_id
				snake_case_   		=			torch.tensor([3_97_69]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,					__UpperCamelCase				)				)
				# verify is_crowd
				snake_case_   		=			torch.tensor([0, 0, 0, 0, 0, 0]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,					__UpperCamelCase				)				)
				# verify class_labels
				snake_case_   		=			torch.tensor([75, 75, 63, 65, 17, 17]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,					__UpperCamelCase				)				)
				# verify orig_size
				snake_case_   		=			torch.tensor([4_80, 6_40]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,					__UpperCamelCase				)				)
				# verify size
				snake_case_   		=			torch.tensor([8_00, 10_66]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,					__UpperCamelCase				)				)







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
				with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'			,					'r'				) as f:
					snake_case_   		=			json.loads(f.read()				)

				snake_case_   		=			{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

				snake_case_   		=			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'				)

				# encode them
				snake_case_   		=			DetaImageProcessor(format='coco_panoptic'				)
				snake_case_   		=			image_processing(images=__UpperCamelCase			,					annotations=__UpperCamelCase			,					masks_path=__UpperCamelCase			,					return_tensors='pt'				)

				# verify pixel values
				snake_case_   		=			torch.Size([1, 3, 8_00, 10_66]				)
				self.assertEqual(encoding['pixel_values'].shape			,					__UpperCamelCase				)

				snake_case_   		=			torch.tensor([0.2796, 0.3138, 0.3481]				)
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,					__UpperCamelCase			,					atol=1E-4				)				)

				# verify area
				snake_case_   		=			torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,					__UpperCamelCase				)				)
				# verify boxes
				snake_case_   		=			torch.Size([6, 4]				)
				self.assertEqual(encoding['labels'][0]['boxes'].shape			,					__UpperCamelCase				)
				snake_case_   		=			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,					__UpperCamelCase			,					atol=1E-3				)				)
				# verify image_id
				snake_case_   		=			torch.tensor([3_97_69]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,					__UpperCamelCase				)				)
				# verify is_crowd
				snake_case_   		=			torch.tensor([0, 0, 0, 0, 0, 0]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,					__UpperCamelCase				)				)
				# verify class_labels
				snake_case_   		=			torch.tensor([17, 17, 63, 75, 75, 93]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,					__UpperCamelCase				)				)
				# verify masks
				snake_case_   		=			82_28_73
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,					__UpperCamelCase				)
				# verify orig_size
				snake_case_   		=			torch.tensor([4_80, 6_40]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,					__UpperCamelCase				)				)
				# verify size
				snake_case_   		=			torch.tensor([8_00, 10_66]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,					__UpperCamelCase				)				)
