"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase											=  logging.get_logger(__name__)

__lowerCamelCase											=  {
    "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
    "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						str													=		'falcon'
						lowerCAmelCase__  :						Union[str, Any]													=		['past_key_values']





						def __init__(       self		,__UpperCAmelCase=6_50_24		,__UpperCAmelCase=45_44		,__UpperCAmelCase=32		,__UpperCAmelCase=71		,__UpperCAmelCase=1e-5		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=True		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=None		,__UpperCAmelCase=False		,__UpperCAmelCase=False		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=False		,__UpperCAmelCase=11		,__UpperCAmelCase=11		,**__UpperCAmelCase		,)					->				int:
													A__	     =							vocab_size
													# Backward compatibility with n_embed kwarg
													A__	     =							kwargs.pop('n_embed'		,__UpperCAmelCase     )
													A__	     =							hidden_size if n_embed is None else n_embed
													A__	     =							num_hidden_layers
													A__	     =							num_attention_heads
													A__	     =							layer_norm_epsilon
													A__	     =							initializer_range
													A__	     =							use_cache
													A__	     =							hidden_dropout
													A__	     =							attention_dropout

													A__	     =							bos_token_id
													A__	     =							eos_token_id
													A__	     =							num_attention_heads if num_kv_heads is None else num_kv_heads
													A__	     =							alibi
													A__	     =							new_decoder_architecture
													A__	     =							multi_query  # Ignored when new_decoder_architecture is True
													A__	     =							parallel_attn
													A__	     =							bias

													super().__init__(bos_token_id=__UpperCAmelCase		,eos_token_id=__UpperCAmelCase		,**__UpperCAmelCase     )





						@property
						def 				snake_case__	(       self     )					->				Tuple:
													return self.hidden_size // self.num_attention_heads





						@property
						def 				snake_case__	(       self     )					->				int:
													return not self.alibi






"""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()





