





import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english







@require_tokenizers
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   BertTokenizer
      UpperCAmelCase__							=   BertTokenizerFast
      UpperCAmelCase__							=   True
      UpperCAmelCase__							=   True
      UpperCAmelCase__							=   filter_non_english


      def 					__snake_case      (		self):



             """simple docstring"""





             super().setUp()

             _lowerCamelCase				:  str						=		[
                 '''[UNK]''',
                 '''[CLS]''',
                 '''[SEP]''',
                 '''[PAD]''',
                 '''[MASK]''',
                 '''want''',
                 '''##want''',
                 '''##ed''',
                 '''wa''',
                 '''un''',
                 '''runn''',
                 '''##ing''',
                 ''',''',
                 '''low''',
                 '''lowest''',
             ]
             _lowerCamelCase				:  str						=		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file'''])
             with open(self.vocab_file   ,				'''w'''   ,				encoding='''utf-8''') as vocab_writer:
                    vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		'''UNwant\u00E9d,running'''
             _lowerCamelCase				:  List[str]						=		'''unwanted, running'''
             return input_text, output_text


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		self.tokenizer_class(self.vocab_file)

             _lowerCamelCase				:  str						=		tokenizer.tokenize('''UNwant\u00E9d,running''')
             self.assertListEqual(a__   ,				['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''])
             self.assertListEqual(tokenizer.convert_tokens_to_ids(a__)   ,				[9, 6, 7, 12, 10, 11])


      def 					__snake_case      (		self):



             """simple docstring"""





             if not self.test_rust_tokenizer:
                    return

             _lowerCamelCase				:  str						=		self.get_tokenizer()
             _lowerCamelCase				:  List[Any]						=		self.get_rust_tokenizer()

             _lowerCamelCase				:  Optional[int]						=		'''UNwant\u00E9d,running'''

             _lowerCamelCase				:  Any						=		tokenizer.tokenize(a__)
             _lowerCamelCase				:  int						=		rust_tokenizer.tokenize(a__)
             self.assertListEqual(a__   ,				a__)

             _lowerCamelCase				:  int						=		tokenizer.encode(a__   ,				add_special_tokens=a__)
             _lowerCamelCase				:  Tuple						=		rust_tokenizer.encode(a__   ,				add_special_tokens=a__)
             self.assertListEqual(a__   ,				a__)

             _lowerCamelCase				:  str						=		self.get_rust_tokenizer()
             _lowerCamelCase				:  str						=		tokenizer.encode(a__)
             _lowerCamelCase				:  int						=		rust_tokenizer.encode(a__)
             self.assertListEqual(a__   ,				a__)

             # With lower casing
             _lowerCamelCase				:  Union[str, Any]						=		self.get_tokenizer(do_lower_case=a__)
             _lowerCamelCase				:  Optional[int]						=		self.get_rust_tokenizer(do_lower_case=a__)

             _lowerCamelCase				:  Any						=		'''UNwant\u00E9d,running'''

             _lowerCamelCase				:  Any						=		tokenizer.tokenize(a__)
             _lowerCamelCase				:  Optional[int]						=		rust_tokenizer.tokenize(a__)
             self.assertListEqual(a__   ,				a__)

             _lowerCamelCase				:  Dict						=		tokenizer.encode(a__   ,				add_special_tokens=a__)
             _lowerCamelCase				:  int						=		rust_tokenizer.encode(a__   ,				add_special_tokens=a__)
             self.assertListEqual(a__   ,				a__)

             _lowerCamelCase				:  Optional[int]						=		self.get_rust_tokenizer()
             _lowerCamelCase				:  Dict						=		tokenizer.encode(a__)
             _lowerCamelCase				:  List[str]						=		rust_tokenizer.encode(a__)
             self.assertListEqual(a__   ,				a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		BasicTokenizer()

             self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''')   ,				['''ah''', '''\u535A''', '''\u63A8''', '''zz'''])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		BasicTokenizer(do_lower_case=a__)

             self.assertListEqual(
                 tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''')   ,				['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''])
             self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')   ,				['''hello'''])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		BasicTokenizer(do_lower_case=a__   ,				strip_accents=a__)

             self.assertListEqual(
                 tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')   ,				['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
             self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')   ,				['''h\u00E9llo'''])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		BasicTokenizer(do_lower_case=a__   ,				strip_accents=a__)

             self.assertListEqual(
                 tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')   ,				['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
             self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')   ,				['''hello'''])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		BasicTokenizer(do_lower_case=a__)

             self.assertListEqual(
                 tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')   ,				['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
             self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')   ,				['''hello'''])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		BasicTokenizer(do_lower_case=a__)

             self.assertListEqual(
                 tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''')   ,				['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		BasicTokenizer(do_lower_case=a__   ,				strip_accents=a__)

             self.assertListEqual(
                 tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')   ,				['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		BasicTokenizer(do_lower_case=a__   ,				strip_accents=a__)

             self.assertListEqual(
                 tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')   ,				['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		BasicTokenizer(do_lower_case=a__   ,				never_split=['''[UNK]'''])

             self.assertListEqual(
                 tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]''')   ,				['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		BasicTokenizer()
             _lowerCamelCase				:  Optional[int]						=		'''a\n\'ll !!to?\'d of, can\'t.'''
             _lowerCamelCase				:  Optional[Any]						=		['''a''', '''\'''', '''ll''', '''!''', '''!''', '''to''', '''?''', '''\'''', '''d''', '''of''', ''',''', '''can''', '''\'''', '''t''', '''.''']
             self.assertListEqual(tokenizer.tokenize(a__)   ,				a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

             _lowerCamelCase				:  Union[str, Any]						=		{}
             for i, token in enumerate(a__):
                    _lowerCamelCase				:  Tuple						=		i
             _lowerCamelCase				:  str						=		WordpieceTokenizer(vocab=a__   ,				unk_token='''[UNK]''')

             self.assertListEqual(tokenizer.tokenize('''''')   ,				[])

             self.assertListEqual(tokenizer.tokenize('''unwanted running''')   ,				['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''])

             self.assertListEqual(tokenizer.tokenize('''unwantedX running''')   ,				['''[UNK]''', '''runn''', '''##ing'''])


      def 					__snake_case      (		self):



             """simple docstring"""





             self.assertTrue(_is_whitespace(''' '''))
             self.assertTrue(_is_whitespace('''\t'''))
             self.assertTrue(_is_whitespace('''\r'''))
             self.assertTrue(_is_whitespace('''\n'''))
             self.assertTrue(_is_whitespace('''\u00A0'''))

             self.assertFalse(_is_whitespace('''A'''))
             self.assertFalse(_is_whitespace('''-'''))


      def 					__snake_case      (		self):



             """simple docstring"""





             self.assertTrue(_is_control('''\u0005'''))

             self.assertFalse(_is_control('''A'''))
             self.assertFalse(_is_control(''' '''))
             self.assertFalse(_is_control('''\t'''))
             self.assertFalse(_is_control('''\r'''))


      def 					__snake_case      (		self):



             """simple docstring"""





             self.assertTrue(_is_punctuation('''-'''))
             self.assertTrue(_is_punctuation('''$'''))
             self.assertTrue(_is_punctuation('''`'''))
             self.assertTrue(_is_punctuation('''.'''))

             self.assertFalse(_is_punctuation('''A'''))
             self.assertFalse(_is_punctuation(''' '''))


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		self.get_tokenizer()
             _lowerCamelCase				:  str						=		self.get_rust_tokenizer()

             # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
             self.assertListEqual([tokenizer.tokenize(a__) for t in ['''Test''', '''\xad''', '''test''']]   ,				[['''[UNK]'''], [], ['''[UNK]''']])

             self.assertListEqual(
                 [rust_tokenizer.tokenize(a__) for t in ['''Test''', '''\xad''', '''test''']]   ,				[['''[UNK]'''], [], ['''[UNK]''']])


      @slow
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		self.tokenizer_class.from_pretrained('''bert-base-uncased''')

             _lowerCamelCase				:  Tuple						=		tokenizer.encode('''sequence builders'''   ,				add_special_tokens=a__)
             _lowerCamelCase				:  Any						=		tokenizer.encode('''multi-sequence build'''   ,				add_special_tokens=a__)

             _lowerCamelCase				:  Optional[Any]						=		tokenizer.build_inputs_with_special_tokens(a__)
             _lowerCamelCase				:  Union[str, Any]						=		tokenizer.build_inputs_with_special_tokens(a__   ,				a__)

             assert encoded_sentence == [101] + text + [102]
             assert encoded_pair == [101] + text + [102] + text_a + [102]


      def 					__snake_case      (		self):



             """simple docstring"""





             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                           _lowerCamelCase				:  List[str]						=		self.rust_tokenizer_class.from_pretrained(a__   ,				**a__)

                           _lowerCamelCase				:  Optional[int]						=		F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
                           _lowerCamelCase				:  List[str]						=		tokenizer_r.encode_plus(
                               a__   ,				return_attention_mask=a__   ,				return_token_type_ids=a__   ,				return_offsets_mapping=a__   ,				add_special_tokens=a__   ,				)

                           _lowerCamelCase				:  List[Any]						=		tokenizer_r.do_lower_case if hasattr(a__   ,				'''do_lower_case''') else False
                           _lowerCamelCase				:  Union[str, Any]						=		(
                               [
                                   ((0, 0), tokenizer_r.cls_token),
                                   ((0, 1), '''A'''),
                                   ((1, 2), ''','''),
                                   ((3, 5), '''na'''),
                                   ((5, 6), '''##ï'''),
                                   ((6, 8), '''##ve'''),
                                   ((9, 15), tokenizer_r.mask_token),
                                   ((16, 21), '''Allen'''),
                                   ((21, 23), '''##NL'''),
                                   ((23, 24), '''##P'''),
                                   ((25, 33), '''sentence'''),
                                   ((33, 34), '''.'''),
                                   ((0, 0), tokenizer_r.sep_token),
                               ]
                               if not do_lower_case
                               else [
                                   ((0, 0), tokenizer_r.cls_token),
                                   ((0, 1), '''a'''),
                                   ((1, 2), ''','''),
                                   ((3, 8), '''naive'''),
                                   ((9, 15), tokenizer_r.mask_token),
                                   ((16, 21), '''allen'''),
                                   ((21, 23), '''##nl'''),
                                   ((23, 24), '''##p'''),
                                   ((25, 33), '''sentence'''),
                                   ((33, 34), '''.'''),
                                   ((0, 0), tokenizer_r.sep_token),
                               ]
                           )

                           self.assertEqual(
                               [e[1] for e in expected_results]   ,				tokenizer_r.convert_ids_to_tokens(tokens['''input_ids''']))
                           self.assertEqual([e[0] for e in expected_results]   ,				tokens['''offset_mapping'''])



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		['''的''', '''人''', '''有''']
             _lowerCamelCase				:  str						=		''''''.join(a__)
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                           _lowerCamelCase				:  Dict						=		True
                           _lowerCamelCase				:  Optional[int]						=		self.tokenizer_class.from_pretrained(a__   ,				**a__)
                           _lowerCamelCase				:  int						=		self.rust_tokenizer_class.from_pretrained(a__   ,				**a__)

                           _lowerCamelCase				:  Dict						=		tokenizer_p.encode(a__   ,				add_special_tokens=a__)
                           _lowerCamelCase				:  Optional[int]						=		tokenizer_r.encode(a__   ,				add_special_tokens=a__)

                           _lowerCamelCase				:  List[str]						=		tokenizer_r.convert_ids_to_tokens(a__)
                           _lowerCamelCase				:  Any						=		tokenizer_p.convert_ids_to_tokens(a__)

                           # it is expected that each Chinese character is not preceded by "##"
                           self.assertListEqual(a__   ,				a__)
                           self.assertListEqual(a__   ,				a__)

                           _lowerCamelCase				:  int						=		False
                           _lowerCamelCase				:  List[Any]						=		self.rust_tokenizer_class.from_pretrained(a__   ,				**a__)
                           _lowerCamelCase				:  int						=		self.tokenizer_class.from_pretrained(a__   ,				**a__)

                           _lowerCamelCase				:  List[str]						=		tokenizer_r.encode(a__   ,				add_special_tokens=a__)
                           _lowerCamelCase				:  List[str]						=		tokenizer_p.encode(a__   ,				add_special_tokens=a__)

                           _lowerCamelCase				:  Union[str, Any]						=		tokenizer_r.convert_ids_to_tokens(a__)
                           _lowerCamelCase				:  str						=		tokenizer_p.convert_ids_to_tokens(a__)

                           # it is expected that only the first Chinese character is not preceded by "##".
                           _lowerCamelCase				:  Optional[Any]						=		[
                               F"""##{token}""" if idx != 0 else token for idx, token in enumerate(a__)
                           ]
                           self.assertListEqual(a__   ,				a__)
                           self.assertListEqual(a__   ,				a__)








import unittest

import numpy as np
import requests

from transformers.testing_utils import 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

       from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
       _lowerCamelCase      							=   False

if is_vision_available():
       from PIL import Image

       from transformers import PixaStructImageProcessor







class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__=7   ,				a__=3   ,				a__=18   ,				a__=30   ,				a__=400   ,				a__=None   ,				a__=True   ,				a__=True   ,				a__=None   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		size if size is not None else {'''height''': 20, '''width''': 20}
             _lowerCamelCase				:  int						=		parent
             _lowerCamelCase				:  Tuple						=		batch_size
             _lowerCamelCase				:  Union[str, Any]						=		num_channels
             _lowerCamelCase				:  Tuple						=		image_size
             _lowerCamelCase				:  Dict						=		min_resolution
             _lowerCamelCase				:  int						=		max_resolution
             _lowerCamelCase				:  Dict						=		size
             _lowerCamelCase				:  Dict						=		do_normalize
             _lowerCamelCase				:  List[Any]						=		do_convert_rgb
             _lowerCamelCase				:  Optional[int]						=		[512, 1024, 2048, 4096]
             _lowerCamelCase				:  Optional[int]						=		patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}


      def 					__snake_case      (		self):



             """simple docstring"""





             return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
             _lowerCamelCase				:  Union[str, Any]						=		Image.open(requests.get(a__   ,				stream=a__).raw).convert('''RGB''')
             return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,)
@require_torch
@require_vision
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   PixaStructImageProcessor if is_vision_available() else None


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		PixaStructImageProcessingTester(self)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(a__   ,				'''do_normalize'''))
             self.assertTrue(hasattr(a__   ,				'''do_convert_rgb'''))


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.image_processor_tester.prepare_dummy_image()

             _lowerCamelCase				:  List[Any]						=		self.image_processing_class(**self.image_processor_dict)
             _lowerCamelCase				:  Optional[int]						=		2048

             _lowerCamelCase				:  Any						=		image_processor(a__   ,				return_tensors='''pt'''   ,				max_patches=a__)
             self.assertTrue(torch.allclose(inputs.flattened_patches.mean()   ,				torch.tensor(0.0606)   ,				atol=1e-3   ,				rtol=1e-3))


      def 					__snake_case      (		self):



             """simple docstring"""





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

             # Test not batched input
             _lowerCamelCase				:  Optional[Any]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Optional[int]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Dict						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.image_processing_class(**self.image_processor_dict)
             # create random PIL images
             _lowerCamelCase				:  Optional[int]						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				Image.Image)

             # Test not batched input
             _lowerCamelCase				:  List[str]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             _lowerCamelCase				:  Dict						=		True

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    with self.assertRaises(a__):
                           _lowerCamelCase				:  List[Any]						=		image_processor(
                               image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches

                    _lowerCamelCase				:  List[Any]						=		'''Hello'''

                    _lowerCamelCase				:  int						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__   ,				header_text=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Dict						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__   ,				header_text=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		self.image_processing_class(**self.image_processor_dict)
             # create random numpy tensors
             _lowerCamelCase				:  Tuple						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__   ,				numpify=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				np.ndarray)

             _lowerCamelCase				:  Any						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  List[str]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Tuple						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		self.image_processing_class(**self.image_processor_dict)
             # create random PyTorch tensors
             _lowerCamelCase				:  str						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__   ,				torchify=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				torch.Tensor)

             # Test not batched input
             _lowerCamelCase				:  Tuple						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Optional[int]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  List[str]						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,)
@require_torch
@require_vision
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   PixaStructImageProcessor if is_vision_available() else None


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		PixaStructImageProcessingTester(self   ,				num_channels=4)
             _lowerCamelCase				:  Union[str, Any]						=		3


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  str						=		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(a__   ,				'''do_normalize'''))
             self.assertTrue(hasattr(a__   ,				'''do_convert_rgb'''))



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.image_processing_class(**self.image_processor_dict)
             # create random PIL images
             _lowerCamelCase				:  Any						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				Image.Image)

             # Test not batched input
             _lowerCamelCase				:  List[Any]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * (self.image_processor_tester.num_channels - 1)
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Union[str, Any]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Union[str, Any]						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)

