"""simple docstring"""




import os
import sys
import unittest


__lowerCamelCase											=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


__lowerCamelCase											=  os.path.join("tests", "models", "bert", "test_modeling_bert.py")
__lowerCamelCase											=  os.path.join("tests", "models", "blip", "test_modeling_blip.py")
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							get_test_to_tester_mapping(__UpperCAmelCase     )
													A__	     =							get_test_to_tester_mapping(__UpperCAmelCase     )

													A__	     =							{'BertModelTest': 'BertModelTester'}

													A__	     =							{
													    'BlipModelTest': 'BlipModelTester',
													    'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
													    'BlipTextModelTest': 'BlipTextModelTester',
													    'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
													    'BlipVQAModelTest': 'BlipVQAModelTester',
													    'BlipVisionModelTest': 'BlipVisionModelTester',
													}

													self.assertEqual(get_test_info.to_json(__UpperCAmelCase     )		,__UpperCAmelCase     )
													self.assertEqual(get_test_info.to_json(__UpperCAmelCase     )		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				str:
													A__	     =							get_model_to_test_mapping(__UpperCAmelCase     )
													A__	     =							get_model_to_test_mapping(__UpperCAmelCase     )

													A__	     =							{
													    'BertForMaskedLM': ['BertModelTest'],
													    'BertForMultipleChoice': ['BertModelTest'],
													    'BertForNextSentencePrediction': ['BertModelTest'],
													    'BertForPreTraining': ['BertModelTest'],
													    'BertForQuestionAnswering': ['BertModelTest'],
													    'BertForSequenceClassification': ['BertModelTest'],
													    'BertForTokenClassification': ['BertModelTest'],
													    'BertLMHeadModel': ['BertModelTest'],
													    'BertModel': ['BertModelTest'],
													}

													A__	     =							{
													    'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
													    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
													    'BlipForQuestionAnswering': ['BlipVQAModelTest'],
													    'BlipModel': ['BlipModelTest'],
													    'BlipTextModel': ['BlipTextModelTest'],
													    'BlipVisionModel': ['BlipVisionModelTest'],
													}

													self.assertEqual(get_test_info.to_json(__UpperCAmelCase     )		,__UpperCAmelCase     )
													self.assertEqual(get_test_info.to_json(__UpperCAmelCase     )		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							get_model_to_tester_mapping(__UpperCAmelCase     )
													A__	     =							get_model_to_tester_mapping(__UpperCAmelCase     )

													A__	     =							{
													    'BertForMaskedLM': ['BertModelTester'],
													    'BertForMultipleChoice': ['BertModelTester'],
													    'BertForNextSentencePrediction': ['BertModelTester'],
													    'BertForPreTraining': ['BertModelTester'],
													    'BertForQuestionAnswering': ['BertModelTester'],
													    'BertForSequenceClassification': ['BertModelTester'],
													    'BertForTokenClassification': ['BertModelTester'],
													    'BertLMHeadModel': ['BertModelTester'],
													    'BertModel': ['BertModelTester'],
													}

													A__	     =							{
													    'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
													    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
													    'BlipForQuestionAnswering': ['BlipVQAModelTester'],
													    'BlipModel': ['BlipModelTester'],
													    'BlipTextModel': ['BlipTextModelTester'],
													    'BlipVisionModel': ['BlipVisionModelTester'],
													}

													self.assertEqual(get_test_info.to_json(__UpperCAmelCase     )		,__UpperCAmelCase     )
													self.assertEqual(get_test_info.to_json(__UpperCAmelCase     )		,__UpperCAmelCase     )






"""simple docstring"""




import numpy as np
from PIL import Image






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							np.array(UpperCamelCase__				)
							if arr.shape[0] != arr.shape[1]:
														raise ValueError('The input array is not a square matrix'				)
							A__	     =							0
							A__	     =							0
							A__	     =							0
							A__	     =							0

							# compute the shape of the output matrix
							A__	     =							(arr.shape[0] - size) // stride + 1
							# initialize the output matrix with zeros of shape maxpool_shape
							A__	     =							np.zeros((maxpool_shape, maxpool_shape)				)

							while i < arr.shape[0]:
														if i + size > arr.shape[0]:
																					# if the end of the matrix is reached, break
																					break
														while j < arr.shape[1]:
																					# if the end of the matrix is reached, break
																					if j + size > arr.shape[1]:
																												break
																					# compute the maximum of the pooling matrix
																					A__	     =							np.max(arr[i : i + size, j : j + size]				)
																					# shift the pooling matrix by stride of column pixels
																					j += stride
																					mat_j += 1

														# shift the pooling matrix by stride of row pixels
														i += stride
														mat_i += 1

														# reset the column index to 0
														A__	     =							0
														A__	     =							0

							return updated_arr






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							np.array(UpperCamelCase__				)
							if arr.shape[0] != arr.shape[1]:
														raise ValueError('The input array is not a square matrix'				)
							A__	     =							0
							A__	     =							0
							A__	     =							0
							A__	     =							0

							# compute the shape of the output matrix
							A__	     =							(arr.shape[0] - size) // stride + 1
							# initialize the output matrix with zeros of shape avgpool_shape
							A__	     =							np.zeros((avgpool_shape, avgpool_shape)				)

							while i < arr.shape[0]:
														# if the end of the matrix is reached, break
														if i + size > arr.shape[0]:
																					break
														while j < arr.shape[1]:
																					# if the end of the matrix is reached, break
																					if j + size > arr.shape[1]:
																												break
																					# compute the average of the pooling matrix
																					A__	     =							int(np.average(arr[i : i + size, j : j + size]				)				)
																					# shift the pooling matrix by stride of column pixels
																					j += stride
																					mat_j += 1

														# shift the pooling matrix by stride of row pixels
														i += stride
														mat_i += 1
														# reset the column index to 0
														A__	     =							0
														A__	     =							0

							return updated_arr


# Main Function
if __name__ == "__main__":
				from doctest import testmod

				testmod(name="avgpooling", verbose=True)

				# Loading the image
				__lowerCamelCase											=  Image.open("path_to_image")

				# Converting the image to numpy array and maxpooling, displaying the result
				# Ensure that the image is a square matrix

				Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

				# Converting the image to numpy array and averagepooling, displaying the result
				# Ensure that the image is a square matrix

				Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()





