


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a			:			Optional[Any]												=					get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					XLMRobertaTokenizer
					__UpperCAmelCase							  =					XLMRobertaTokenizerFast
					__UpperCAmelCase							  =					True
					__UpperCAmelCase							  =					True






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								super().setUp()

								# We have a SentencePiece fixture for testing
								__lowercase			       = XLMRobertaTokenizer(snake_case_			,							keep_accents=snake_case_    )
								tokenizer.save_pretrained(self.tmpdirname    )






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = '''<pad>'''
								__lowercase			       = 1

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






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = 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(snake_case_    )			,							1_0_0_2    )






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								self.assertEqual(self.get_tokenizer().vocab_size			,							1_0_0_2    )






					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								__lowercase			       = XLMRobertaTokenizer(snake_case_			,							keep_accents=snake_case_    )

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

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(snake_case_    )			,							[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]			,							)

								__lowercase			       = tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
								self.assertListEqual(
								    snake_case_			,							[
								        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''',
								        '''.''',
								    ]			,							)
								__lowercase			       = tokenizer.convert_tokens_to_ids(snake_case_    )
								self.assertListEqual(
								    snake_case_			,							[
								        value + tokenizer.fairseq_offset
								        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ]			,							)

								__lowercase			       = tokenizer.convert_ids_to_tokens(snake_case_    )
								self.assertListEqual(
								    snake_case_			,							[
								        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>''',
								        '''.''',
								    ]			,							)






					def A   (					self    ) ->      Tuple:







								'''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

								__lowercase			       = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
														__lowercase			       = self.rust_tokenizer_class.from_pretrained(snake_case_			,							**snake_case_    )
														__lowercase			       = self.tokenizer_class.from_pretrained(snake_case_			,							**snake_case_    )

														__lowercase			       = tempfile.mkdtemp()

														__lowercase			       = tokenizer_r.save_pretrained(snake_case_    )
														__lowercase			       = tokenizer_p.save_pretrained(snake_case_    )

														# 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    )    )
														__lowercase			       = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f    )
														self.assertSequenceEqual(snake_case_			,							snake_case_    )

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

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

														shutil.rmtree(snake_case_    )

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

														__lowercase			       = tokenizer_r.save_pretrained(snake_case_			,							legacy_format=snake_case_    )
														__lowercase			       = tokenizer_p.save_pretrained(snake_case_    )

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

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

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

														shutil.rmtree(snake_case_    )

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

														__lowercase			       = tokenizer_r.save_pretrained(snake_case_			,							legacy_format=snake_case_    )
														__lowercase			       = tokenizer_p.save_pretrained(snake_case_    )

														# 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
														__lowercase			       = tokenizer_r.from_pretrained(snake_case_    )
														__lowercase			       = tokenizer_p.from_pretrained(snake_case_    )

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

														shutil.rmtree(snake_case_    )






					@cached_property
					def A   (					self    ) ->      int:







								'''simple docstring'''

								return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''    )






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								with tempfile.NamedTemporaryFile() as f:
											shutil.copyfile(snake_case_			,							f.name    )
											__lowercase			       = XLMRobertaTokenizer(f.name			,							keep_accents=snake_case_    )
											__lowercase			       = pickle.dumps(snake_case_    )
								pickle.loads(snake_case_    )






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								if not self.test_rust_tokenizer:
											return

								__lowercase			       = self.get_tokenizer()
								__lowercase			       = self.get_rust_tokenizer()

								__lowercase			       = '''I was born in 92000, and this is falsé.'''

								__lowercase			       = tokenizer.tokenize(snake_case_    )
								__lowercase			       = rust_tokenizer.tokenize(snake_case_    )
								self.assertListEqual(snake_case_			,							snake_case_    )

								__lowercase			       = tokenizer.encode(snake_case_			,							add_special_tokens=snake_case_    )
								__lowercase			       = rust_tokenizer.encode(snake_case_			,							add_special_tokens=snake_case_    )
								self.assertListEqual(snake_case_			,							snake_case_    )

								__lowercase			       = self.get_rust_tokenizer()
								__lowercase			       = tokenizer.encode(snake_case_    )
								__lowercase			       = rust_tokenizer.encode(snake_case_    )
								self.assertListEqual(snake_case_			,							snake_case_    )






					@slow
					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = '''Hello World!'''
								__lowercase			       = [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

								self.assertListEqual(snake_case_			,							self.big_tokenizer.encode(snake_case_    )    )






					@slow
					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = (
								    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
								    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
								)
								__lowercase			       = [
								    0,
								    3_2_9_3,
								    8_3,
								    1_0,
								    4_5_5_2,
								    4_9_8_9,
								    7_9_8_6,
								    6_7_8,
								    1_0,
								    5_9_1_5,
								    1_1_1,
								    1_7_9_4_5_9,
								    1_2_4_8_5_0,
								    4,
								    6_0_4_4,
								    2_3_7,
								    1_2,
								    6,
								    5,
								    6,
								    4,
								    6_7_8_0,
								    7_0_5,
								    1_5,
								    1_3_8_8,
								    4_4,
								    3_7_8,
								    1_0_1_1_4,
								    7_1_1,
								    1_5_2,
								    2_0,
								    6,
								    5,
								    2_2_3_7_6,
								    6_4_2,
								    1_2_2_1,
								    1_5_1_9_0,
								    3_4_1_5_3,
								    4_5_0,
								    5_6_0_8,
								    9_5_9,
								    1_1_1_9,
								    5_7_7_0_2,
								    1_3_6,
								    1_8_6,
								    4_7,
								    1_0_9_8,
								    2_9_3_6_7,
								    4_7,
								    # 4426, # What fairseq tokenizes from "<unk>": "_<"
								    # 3678, # What fairseq tokenizes from "<unk>": "unk"
								    # 2740, # What fairseq tokenizes from "<unk>": ">"
								    3,  # What we tokenize from "<unk>": "<unk>"
								    6,  # Residue from the tokenization: an extra sentencepiece underline
								    4,
								    6_0_4_4,
								    2_3_7,
								    6_2_8_4,
								    5_0_9_0_1,
								    5_2_8,
								    3_1,
								    9_0,
								    3_4,
								    9_2_7,
								    2,
								]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

								self.assertListEqual(snake_case_			,							self.big_tokenizer.encode(snake_case_    )    )






					@slow
					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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=snake_case_			,							model_name='''xlm-roberta-base'''			,							revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''			,							)






import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DonutImageProcessor
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=7			,							snake_case_=3			,							snake_case_=1_8			,							snake_case_=3_0			,							snake_case_=4_0_0			,							snake_case_=True			,							snake_case_=None			,							snake_case_=True			,							snake_case_=False			,							snake_case_=True			,							snake_case_=True			,							snake_case_=[0.5, 0.5, 0.5]			,							snake_case_=[0.5, 0.5, 0.5]			,							) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = parent
								__lowercase			       = batch_size
								__lowercase			       = num_channels
								__lowercase			       = image_size
								__lowercase			       = min_resolution
								__lowercase			       = max_resolution
								__lowercase			       = do_resize
								__lowercase			       = size if size is not None else {'''height''': 1_8, '''width''': 2_0}
								__lowercase			       = do_thumbnail
								__lowercase			       = do_align_axis
								__lowercase			       = do_pad
								__lowercase			       = do_normalize
								__lowercase			       = image_mean
								__lowercase			       = image_std






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}




@require_torch
@require_vision
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					DonutImageProcessor if is_vision_available() else None






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = DonutImageProcessingTester(self    )






					@property
					def A   (					self    ) ->      str:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(snake_case_			,							'''do_resize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''size'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_thumbnail'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_align_long_axis'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_pad'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_normalize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_mean'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_std'''    )    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict    )
								self.assertEqual(image_processor.size			,							{'''height''': 1_8, '''width''': 2_0}    )

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=4_2    )
								self.assertEqual(image_processor.size			,							{'''height''': 4_2, '''width''': 4_2}    )

								# Previous config had dimensions in (width, height) order
								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=(4_2, 8_4)    )
								self.assertEqual(image_processor.size			,							{'''height''': 8_4, '''width''': 4_2}    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								pass






					@is_flaky()
					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							Image.Image    )

								# Test not batched input
								__lowercase			       = image_processing(image_inputs[0]			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_			,							numpify=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							np.ndarray    )

								# Test not batched input
								__lowercase			       = image_processing(image_inputs[0]			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_			,							torchify=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							torch.Tensor    )

								# Test not batched input
								__lowercase			       = image_processing(image_inputs[0]			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)


