





import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar

class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   logging.get_logger()

         # the current default level is logging.WARNING
         __snake_case							       =   logging.get_verbosity()

         logging.set_verbosity_error()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         logging.set_verbosity_warning()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         logging.set_verbosity_info()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         logging.set_verbosity_debug()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         # restore to the original level
         logging.set_verbosity(a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   logging.get_verbosity()

         __snake_case							       =   logging.get_logger('''transformers.models.bart.tokenization_bart'''      )
         __snake_case							       =   '''Testing 1, 2, 3'''

         # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
         if level_origin <= logging.WARNING:
            with CaptureLogger(a__      ) as cl:
               logger.warning(a__      )
            self.assertEqual(cl.out				,			msg + '''\n'''      )

         # this is setting the level for all of `transformers.*` loggers
         logging.set_verbosity_error()

         # should not be able to log warnings
         with CaptureLogger(a__      ) as cl:
            logger.warning(a__      )
         self.assertEqual(cl.out				,			''''''      )

         # should be able to log warnings again
         logging.set_verbosity_warning()
         with CaptureLogger(a__      ) as cl:
            logger.warning(a__      )
         self.assertEqual(cl.out				,			msg + '''\n'''      )

         # restore to the original level
         logging.set_verbosity(a__      )


      @mockenv(TRANSFORMERS_VERBOSITY='''error'''      )
      def    a (self       :      Dict      ):





         """simple docstring"""



         transformers.utils.logging._reset_library_root_logger()
         # this action activates the env var
         __snake_case							       =   logging.get_logger('''transformers.models.bart.tokenization_bart'''      )

         __snake_case							       =   os.getenv('''TRANSFORMERS_VERBOSITY'''				,			a__      )
         __snake_case							       =   logging.log_levels[env_level_str]

         __snake_case							       =   logging.get_verbosity()
         self.assertEqual(
             a__				,			a__				,			f"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"""				,			)

         # restore to the original level
         __snake_case							       =   ''''''
         transformers.utils.logging._reset_library_root_logger()


      @mockenv(TRANSFORMERS_VERBOSITY='''super-error'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         transformers.utils.logging._reset_library_root_logger()
         __snake_case							       =   logging.logging.getLogger()
         with CaptureLogger(a__      ) as cl:
            # this action activates the env var
            logging.get_logger('''transformers.models.bart.tokenization_bart'''      )
         self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''				,			cl.out      )

         # no need to restore as nothing was changed


      def    a (self       :      Any      ):





         """simple docstring"""



         transformers.utils.logging._reset_library_root_logger()

         __snake_case							       =   logging.get_logger('''transformers.models.bart.tokenization_bart'''      )
         __snake_case							       =   '''Testing 1, 2, 3'''

         with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''      ):
            # nothing should be logged as env var disables this method
            with CaptureLogger(a__      ) as cl:
               logger.warning_advice(a__      )
            self.assertEqual(cl.out				,			''''''      )

         with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''      ):
            # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
            with CaptureLogger(a__      ) as cl:
               logger.warning_advice(a__      )
            self.assertEqual(cl.out				,			msg + '''\n'''      )






def lowerCamelCase__    (							)      ->				str:
   disable_progress_bar()
   assert are_progress_bars_disabled()

   enable_progress_bar()
   assert not are_progress_bars_disabled()








import numpy as np
from PIL import Image



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				np.array(lowerCamelCase )
      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 _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				np.array(lowerCamelCase )
      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
       snake_case__					:							Optional[Any]													=     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()





