

import numpy as np
from PIL import Image


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      np.array(SCREAMING_SNAKE_CASE__			)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('''The input array is not a square matrix'''			)
						snake_case_  =      0
						snake_case_  =      0
						snake_case_  =      0
						snake_case_  =      0

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

						return updated_arr


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      np.array(SCREAMING_SNAKE_CASE__			)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('''The input array is not a square matrix'''			)
						snake_case_  =      0
						snake_case_  =      0
						snake_case_  =      0
						snake_case_  =      0

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




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''xlm-roberta'''




     def __init__(					self			,    a__=3_0_5_2_2			,    a__=7_6_8			,    a__=1_2			,    a__=1_2			,    a__=3_0_7_2			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=2			,    a__=0.0_2			,    a__=1e-12			,    a__=1			,    a__=0			,    a__=2			,    a__="absolute"			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(pad_token_id=a__			,    bos_token_id=a__			,    eos_token_id=a__			,    **a__)

         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       hidden_act
         A__					=       intermediate_size
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       max_position_embeddings
         A__					=       type_vocab_size
         A__					=       initializer_range
         A__					=       layer_norm_eps
         A__					=       position_embedding_type
         A__					=       use_cache
         A__					=       classifier_dropout







class      _UpperCAmelCase      (							A__  ):




     @property
     def 		snake_case_		(					self):
         if self.task == "multiple-choice":
             A__					=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A__					=       {0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ])
