



import numpy as np
from PIL import Image

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]	,   _snake_case	:int	,   _snake_case	:int				)  ->     Dict:
   _A      =       np.array(snake_case__				)
   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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict	,   _snake_case	:str	,   _snake_case	:Dict				)  ->     Tuple:
   _A      =       np.array(snake_case__				)
   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
   UpperCAmelCase_		 =					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()





'''simple docstring'''




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


lowercase     :							List[str]       						=     logging.get_logger(__name__)

lowercase     :							Optional[int]       						=     {
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''switch_transformers'''
     __magic_name__          =						['''past_key_values''']
     __magic_name__          =						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





     def __init__(				self		,     SCREAMING_SNAKE_CASE=32128		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=64		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=64		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=0.01		,     SCREAMING_SNAKE_CASE="float32"		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=128		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=0.001		,     SCREAMING_SNAKE_CASE=0.001		,     SCREAMING_SNAKE_CASE=1.0		,     SCREAMING_SNAKE_CASE="relu"		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=0		,     SCREAMING_SNAKE_CASE=1		,     **SCREAMING_SNAKE_CASE		,     )					->							List[Any]:






         """simple docstring"""






         A			:  Union[str, Any]   =		vocab_size
         A			:  Any   =		d_model
         A			:  Dict   =		d_kv
         A			:  Optional[Any]   =		d_ff

         A			:  List[Any]   =		num_sparse_encoder_layers

         A			:  List[Any]   =		num_layers
         A			:  Optional[int]   =		(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         A			:  List[str]   =		num_sparse_decoder_layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_encoder_layers > 0:
             A			:  int   =		self.num_layers // self.num_sparse_encoder_layers
         else:
             A			:  int   =		self.num_layers  # HACK: this will create 0 sparse layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_decoder_layers > 0:
             A			:  str   =		self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
             A			:  Dict   =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

         A			:  List[Any]   =		num_heads
         A			:  str   =		num_experts
         A			:  Dict   =		expert_capacity
         A			:  Optional[int]   =		router_bias
         A			:  Any   =		router_jitter_noise
         if router_dtype not in ["float32", "float16", "bfloat16"]:
             raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'      )
         A			:  Dict   =		router_dtype

         A			:  int   =		router_ignore_padding_tokens
         A			:  List[Any]   =		relative_attention_num_buckets
         A			:  Dict   =		relative_attention_max_distance

         A			:  Optional[int]   =		dropout_rate
         A			:  Dict   =		layer_norm_epsilon
         A			:  Optional[int]   =		initializer_factor
         A			:  Union[str, Any]   =		feed_forward_proj
         A			:  Any   =		use_cache
         A			:  Tuple   =		add_router_probs

         A			:  List[str]   =		router_z_loss_coef
         A			:  str   =		router_aux_loss_coef

         A			:  Union[str, Any]   =		self.feed_forward_proj.split('''-'''      )
         A			:  Optional[Any]   =		act_info[-1]
         A			:  Union[str, Any]   =		act_info[0] == '''gated'''

         if len(SCREAMING_SNAKE_CASE      ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE      ) > 2:
             raise ValueError(
                 F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                 '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                 '''\'gated-gelu\' or \'relu\''''      )

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
             A			:  str   =		'''gelu_new'''

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE		,     eos_token_id=SCREAMING_SNAKE_CASE		,     is_encoder_decoder=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE		,     )




