






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case    :    Optional[int]         =     get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_torch_available():
					from transformers.models.mbart.modeling_mbart import shift_tokens_right

__snake_case    :    str         =     25_00_04
__snake_case    :    Dict         =     25_00_20


@require_sentencepiece
@require_tokenizers
class     __SCREAMING_SNAKE_CASE					(   _UpperCAmelCase		,					unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       Any   =					MBartaaTokenizer
						_SCREAMING_SNAKE_CASE	:       int   =					MBartaaTokenizerFast
						_SCREAMING_SNAKE_CASE	:       Tuple   =					True
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					True






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												super().setUp()

												# We have a SentencePiece fixture for testing
												lowerCAmelCase__    						=						MBartaaTokenizer(lowerCamelCase_     ,						src_lang='en_XX'     ,						tgt_lang='ro_RO'     ,						keep_accents=lowerCamelCase_   )
												tokenizer.save_pretrained(self.tmpdirname   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						'''<s>'''
												lowerCAmelCase__    						=						0

												self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_   )     ,						lowerCamelCase_   )
												self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_   )     ,						lowerCamelCase_   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						list(self.get_tokenizer().get_vocab().keys()   )

												self.assertEqual(vocab_keys[0]     ,						'<s>'   )
												self.assertEqual(vocab_keys[1]     ,						'<pad>'   )
												self.assertEqual(vocab_keys[-1]     ,						'<mask>'   )
												self.assertEqual(len(lowerCamelCase_   )     ,						10_54   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												self.assertEqual(self.get_tokenizer().vocab_size     ,						10_54   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						MBartaaTokenizer(lowerCamelCase_     ,						src_lang='en_XX'     ,						tgt_lang='ro_RO'     ,						keep_accents=lowerCamelCase_   )

												lowerCAmelCase__    						=						tokenizer.tokenize('This is a test'   )
												self.assertListEqual(lowerCamelCase_     ,						['▁This', '▁is', '▁a', '▁t', 'est']   )

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(lowerCamelCase_   )     ,						[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]     ,						)

												lowerCAmelCase__    						=						tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
												self.assertListEqual(
												    lowerCamelCase_     ,						[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']     ,						)
												lowerCAmelCase__    						=						tokenizer.convert_tokens_to_ids(lowerCamelCase_   )
												self.assertListEqual(
												    lowerCamelCase_     ,						[
												        value + tokenizer.fairseq_offset
												        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
												    ]     ,						)

												lowerCAmelCase__    						=						tokenizer.convert_ids_to_tokens(lowerCamelCase_   )
												self.assertListEqual(
												    lowerCamelCase_     ,						[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']     ,						)






						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						{'''input_ids''': [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
												# fmt: on

												self.tokenizer_integration_test_util(
												    expected_encoding=lowerCamelCase_     ,						model_name='facebook/mbart-large-50'     ,						revision='d3913889c59cd5c9e456b269c376325eabad57e2'     ,						)







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""




												if not self.test_slow_tokenizer:
																		# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																		return

												lowerCAmelCase__    						=						(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
																								lowerCAmelCase__    						=						self.rust_tokenizer_class.from_pretrained(lowerCamelCase_     ,						**lowerCamelCase_   )
																								lowerCAmelCase__    						=						self.tokenizer_class.from_pretrained(lowerCamelCase_     ,						**lowerCamelCase_   )

																								lowerCAmelCase__    						=						tempfile.mkdtemp()

																								lowerCAmelCase__    						=						tokenizer_r.save_pretrained(lowerCamelCase_   )
																								lowerCAmelCase__    						=						tokenizer_p.save_pretrained(lowerCamelCase_   )

																								# Checks it save with the same files + the tokenizer.json file for the fast one
																								self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files   )   )
																								lowerCAmelCase__    						=						tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f   )
																								self.assertSequenceEqual(lowerCamelCase_     ,						lowerCamelCase_   )

																								# Checks everything loads correctly in the same way
																								lowerCAmelCase__    						=						tokenizer_r.from_pretrained(lowerCamelCase_   )
																								lowerCAmelCase__    						=						tokenizer_p.from_pretrained(lowerCamelCase_   )

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																														self.assertTrue(hasattr(lowerCamelCase_     ,						lowerCamelCase_   )   )
																														# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																														# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																								shutil.rmtree(lowerCamelCase_   )

																								# Save tokenizer rust, legacy_format=True
																								lowerCAmelCase__    						=						tempfile.mkdtemp()

																								lowerCAmelCase__    						=						tokenizer_r.save_pretrained(lowerCamelCase_     ,						legacy_format=lowerCamelCase_   )
																								lowerCAmelCase__    						=						tokenizer_p.save_pretrained(lowerCamelCase_   )

																								# Checks it save with the same files
																								self.assertSequenceEqual(lowerCamelCase_     ,						lowerCamelCase_   )

																								# Checks everything loads correctly in the same way
																								lowerCAmelCase__    						=						tokenizer_r.from_pretrained(lowerCamelCase_   )
																								lowerCAmelCase__    						=						tokenizer_p.from_pretrained(lowerCamelCase_   )

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																														self.assertTrue(hasattr(lowerCamelCase_     ,						lowerCamelCase_   )   )

																								shutil.rmtree(lowerCamelCase_   )

																								# Save tokenizer rust, legacy_format=False
																								lowerCAmelCase__    						=						tempfile.mkdtemp()

																								lowerCAmelCase__    						=						tokenizer_r.save_pretrained(lowerCamelCase_     ,						legacy_format=lowerCamelCase_   )
																								lowerCAmelCase__    						=						tokenizer_p.save_pretrained(lowerCamelCase_   )

																								# Checks it saved the tokenizer.json file
																								self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files   )   )

																								# Checks everything loads correctly in the same way
																								lowerCAmelCase__    						=						tokenizer_r.from_pretrained(lowerCamelCase_   )
																								lowerCAmelCase__    						=						tokenizer_p.from_pretrained(lowerCamelCase_   )

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																														self.assertTrue(hasattr(lowerCamelCase_     ,						lowerCamelCase_   )   )

																								shutil.rmtree(lowerCamelCase_   )




@require_torch
@require_sentencepiece
@require_tokenizers
class     __SCREAMING_SNAKE_CASE					(   unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       List[Any]   =					'''facebook/mbart-large-50-one-to-many-mmt'''
						_SCREAMING_SNAKE_CASE	:       int   =					[
						    ''' UN Chief Says There Is No Military Solution in Syria''',
						    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
						]
						_SCREAMING_SNAKE_CASE	:       Optional[Any]   =					[
						    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
						    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
						    ''' pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor'''
						    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
						]
						_SCREAMING_SNAKE_CASE	:       Tuple   =					[EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]






						@classmethod
						def       UpperCamelCase__      (	cls   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						MBartaaTokenizer.from_pretrained(
												    cls.checkpoint_name     ,						src_lang='en_XX'     ,						tgt_lang='ro_RO'   )
												lowerCAmelCase__    						=						1
												return cls






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']     ,						25_00_01   )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']     ,						25_00_04   )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']     ,						25_00_20   )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']     ,						25_00_38   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
												self.assertListEqual(self.expected_src_tokens     ,						lowerCamelCase_   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												self.assertIn(lowerCamelCase_     ,						self.tokenizer.all_special_ids   )
												lowerCAmelCase__    						=						[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
												lowerCAmelCase__    						=						self.tokenizer.decode(lowerCamelCase_     ,						skip_special_tokens=lowerCamelCase_   )
												lowerCAmelCase__    						=						self.tokenizer.decode(generated_ids[1:]     ,						skip_special_tokens=lowerCamelCase_   )
												self.assertEqual(lowerCamelCase_     ,						lowerCamelCase_   )
												self.assertNotIn(self.tokenizer.eos_token     ,						lowerCamelCase_   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						['''this is gunna be a long sentence ''' * 20]
												assert isinstance(src_text[0]     ,						lowerCamelCase_   )
												lowerCAmelCase__    						=						10
												lowerCAmelCase__    						=						self.tokenizer(lowerCamelCase_     ,						max_length=lowerCamelCase_     ,						truncation=lowerCamelCase_   ).input_ids[0]
												self.assertEqual(ids[0]     ,						lowerCamelCase_   )
												self.assertEqual(ids[-1]     ,						2   )
												self.assertEqual(len(lowerCamelCase_   )     ,						lowerCamelCase_   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']   )     ,						[25_00_53, 25_00_01]   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						tempfile.mkdtemp()
												lowerCAmelCase__    						=						self.tokenizer.fairseq_tokens_to_ids
												self.tokenizer.save_pretrained(lowerCamelCase_   )
												lowerCAmelCase__    						=						MBartaaTokenizer.from_pretrained(lowerCamelCase_   )
												self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,						lowerCamelCase_   )






						@require_torch
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.tokenizer(self.src_text     ,						text_target=self.tgt_text     ,						padding=lowerCamelCase_     ,						return_tensors='pt'   )
												lowerCAmelCase__    						=						shift_tokens_right(batch['labels']     ,						self.tokenizer.pad_token_id   )

												# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
												assert batch.input_ids[1][0] == EN_CODE
												assert batch.input_ids[1][-1] == 2
												assert batch.labels[1][0] == RO_CODE
												assert batch.labels[1][-1] == 2
												assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]






						@require_torch
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.tokenizer(
												    self.src_text     ,						text_target=self.tgt_text     ,						padding=lowerCamelCase_     ,						truncation=lowerCamelCase_     ,						max_length=len(self.expected_src_tokens   )     ,						return_tensors='pt'     ,						)
												lowerCAmelCase__    						=						shift_tokens_right(batch['labels']     ,						self.tokenizer.pad_token_id   )

												self.assertIsInstance(lowerCamelCase_     ,						lowerCamelCase_   )

												self.assertEqual((2, 14)     ,						batch.input_ids.shape   )
												self.assertEqual((2, 14)     ,						batch.attention_mask.shape   )
												lowerCAmelCase__    						=						batch.input_ids.tolist()[0]
												self.assertListEqual(self.expected_src_tokens     ,						lowerCamelCase_   )
												self.assertEqual(2     ,						batch.decoder_input_ids[0, 0]   )  # decoder_start_token_id
												# Test that special tokens are reset
												self.assertEqual(self.tokenizer.prefix_tokens     ,						[EN_CODE]   )
												self.assertEqual(self.tokenizer.suffix_tokens     ,						[self.tokenizer.eos_token_id]   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.tokenizer(self.src_text     ,						padding=lowerCamelCase_     ,						truncation=lowerCamelCase_     ,						max_length=3     ,						return_tensors='pt'   )
												lowerCAmelCase__    						=						self.tokenizer(
												    text_target=self.tgt_text     ,						padding=lowerCamelCase_     ,						truncation=lowerCamelCase_     ,						max_length=10     ,						return_tensors='pt'   )
												lowerCAmelCase__    						=						targets['''input_ids''']
												lowerCAmelCase__    						=						shift_tokens_right(lowerCamelCase_     ,						self.tokenizer.pad_token_id   )

												self.assertEqual(batch.input_ids.shape[1]     ,						3   )
												self.assertEqual(batch.decoder_input_ids.shape[1]     ,						10   )







						@require_torch
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.tokenizer._build_translation_inputs(
												    'A test'     ,						return_tensors='pt'     ,						src_lang='en_XX'     ,						tgt_lang='ar_AR'   )

												self.assertEqual(
												    nested_simplify(lowerCamelCase_   )     ,						{
												        # en_XX, A, test, EOS
												        'input_ids': [[25_00_04, 62, 30_34, 2]],
												        'attention_mask': [[1, 1, 1, 1]],
												        # ar_AR
												        'forced_bos_token_id': 25_00_01,
												    }     ,						)













import numpy as np
from PIL import Image







def 	_UpperCamelCase   (    UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					int		)  ->   np.ndarray:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'		)
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0

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

						return updated_arr







def 	_UpperCamelCase   (    UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					int		)  ->   np.ndarray:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'		)
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0

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

						return updated_arr


# Main Function
if __name__ == "__main__":
					from doctest import testmod

					testmod(name="""avgpooling""", verbose=True)

					# Loading the image
					__snake_case    :    List[str]         =     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()





