"""simple docstring"""




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class     UpperCamelCase__(			__A		,						__A		,						__A ):





						@register_to_config
						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = False		,)					->				Union[str, Any]:
													super().__init__()

													A__	     =							nn.Embedding(__UpperCAmelCase		,__UpperCAmelCase     )

													A__	     =							nn.Embedding(__UpperCAmelCase		,__UpperCAmelCase     )
													A__	     =							False

													A__	     =							nn.Dropout(p=__UpperCAmelCase     )

													A__	     =							TaConfig(
													    vocab_size=__UpperCAmelCase		,d_model=__UpperCAmelCase		,num_heads=__UpperCAmelCase		,d_kv=__UpperCAmelCase		,d_ff=__UpperCAmelCase		,dropout_rate=__UpperCAmelCase		,feed_forward_proj=__UpperCAmelCase		,is_decoder=__UpperCAmelCase		,is_encoder_decoder=__UpperCAmelCase		,)

													A__	     =							nn.ModuleList()
													for lyr_num in range(__UpperCAmelCase     ):
																				A__	     =							TaBlock(__UpperCAmelCase     )
																				self.encoders.append(__UpperCAmelCase     )

													A__	     =							TaLayerNorm(__UpperCAmelCase     )
													A__	     =							nn.Dropout(p=__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Any:
													A__	     =							self.token_embedder(__UpperCAmelCase     )

													A__	     =							encoder_input_tokens.shape[1]
													A__	     =							torch.arange(__UpperCAmelCase		,device=encoder_input_tokens.device     )
													x += self.position_encoding(__UpperCAmelCase     )

													A__	     =							self.dropout_pre(__UpperCAmelCase     )

													# inverted the attention mask
													A__	     =							encoder_input_tokens.size()
													A__	     =							self.get_extended_attention_mask(__UpperCAmelCase		,__UpperCAmelCase     )

													for lyr in self.encoders:
																				A__	     =							lyr(__UpperCAmelCase		,__UpperCAmelCase     )[0]
													A__	     =							self.layer_norm(__UpperCAmelCase     )

													return self.dropout_post(__UpperCAmelCase     ), encoder_inputs_mask






"""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()





