


import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_pytesseract_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor





class   __a      (   unittest.TestCase				):
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=7    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=18    ,			lowerCAmelCase__=30    ,			lowerCAmelCase__=400    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=True    ,			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Tuple        =       size if size is not None else {'height': 18, 'width': 18}
        lowercase__:       Tuple        =       parent
        lowercase__:       str        =       batch_size
        lowercase__:       Union[str, Any]        =       num_channels
        lowercase__:       Tuple        =       image_size
        lowercase__:       List[str]        =       min_resolution
        lowercase__:       str        =       max_resolution
        lowercase__:       Optional[int]        =       do_resize
        lowercase__:       Optional[Any]        =       size
        lowercase__:       int        =       apply_ocr
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class   __a      (   __UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Dict       					=			LayoutLMvaImageProcessor if is_pytesseract_available() else None
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       List[str]        =       LayoutLMvaImageProcessingTester(self			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.image_processor_tester.prepare_image_processor_dict()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Any        =       self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_resize'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'size'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'apply_ocr'			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.image_processing_class.from_dict(self.image_processor_dict			)
        self.assertEqual(image_processor.size    ,			{'height': 18, 'width': 18}			)

        lowercase__:       Union[str, Any]        =       self.image_processing_class.from_dict(self.image_processor_dict    ,			size=42			)
        self.assertEqual(image_processor.size    ,			{'height': 42, 'width': 42}			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       int        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowercase__:       Dict        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			Image.Image			)

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

        self.assertIsInstance(encoding.words    ,			lowerCAmelCase__			)
        self.assertIsInstance(encoding.boxes    ,			lowerCAmelCase__			)

        # Test batched
        lowercase__:       Any        =       image_processing(lowerCAmelCase__    ,			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'],
            )    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       List[str]        =       self.image_processing_class(**self.image_processor_dict			)
        # create random numpy tensors
        lowercase__:       str        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__    ,			numpify=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			np.ndarray			)

        # Test not batched input
        lowercase__:       Optional[Any]        =       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__:       Tuple        =       image_processing(lowerCAmelCase__    ,			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'],
            )    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       List[str]        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        lowercase__:       int        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__    ,			torchify=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			torch.Tensor			)

        # Test not batched input
        lowercase__:       Any        =       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__:       int        =       image_processing(lowerCAmelCase__    ,			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'],
            )    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        # with apply_OCR = True
        lowercase__:       Dict        =       LayoutLMvaImageProcessor()

        from datasets import load_dataset

        lowercase__:       str        =       load_dataset('hf-internal-testing/fixtures_docvqa'    ,			split='test'			)

        lowercase__:       List[str]        =       Image.open(ds[0]['file']			).convert('RGB'			)

        lowercase__:       Any        =       image_processing(lowerCAmelCase__    ,			return_tensors='pt'			)

        self.assertEqual(encoding.pixel_values.shape    ,			(1, 3, 224, 224)			)
        self.assertEqual(len(encoding.words			)    ,			len(encoding.boxes			)			)

        # fmt: off
        # the words and boxes were obtained with Tesseract 4.1.1
        lowercase__:       Union[str, Any]        =       [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']]  # noqa: E231
        lowercase__:       Optional[int]        =       [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
        # fmt: on

        self.assertListEqual(encoding.words    ,			lowerCAmelCase__			)
        self.assertListEqual(encoding.boxes    ,			lowerCAmelCase__			)

        # with apply_OCR = False
        lowercase__:       Optional[int]        =       LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__			)

        lowercase__:       int        =       image_processing(lowerCAmelCase__    ,			return_tensors='pt'			)

        self.assertEqual(encoding.pixel_values.shape    ,			(1, 3, 224, 224)			)






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDeiTForImageClassification,
     TFDeiTForImageClassificationWithTeacher,
     TFDeiTForMaskedImageModeling,
     TFDeiTModel,
 )
 from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor





class   __a      :
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=13    ,			lowerCAmelCase__=30    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=32    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=4    ,			lowerCAmelCase__=37    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=10    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=2    ,			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       List[str]        =       parent
        lowercase__:       Tuple        =       batch_size
        lowercase__:       Optional[Any]        =       image_size
        lowercase__:       int        =       patch_size
        lowercase__:       Union[str, Any]        =       num_channels
        lowercase__:       Dict        =       is_training
        lowercase__:       Any        =       use_labels
        lowercase__:       Optional[int]        =       hidden_size
        lowercase__:       Tuple        =       num_hidden_layers
        lowercase__:       List[str]        =       num_attention_heads
        lowercase__:       Any        =       intermediate_size
        lowercase__:       Union[str, Any]        =       hidden_act
        lowercase__:       Union[str, Any]        =       hidden_dropout_prob
        lowercase__:       Optional[Any]        =       attention_probs_dropout_prob
        lowercase__:       str        =       type_sequence_label_size
        lowercase__:       List[str]        =       initializer_range
        lowercase__:       Optional[Any]        =       scope
        lowercase__:       Dict        =       encoder_stride

        # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
        lowercase__:       List[str]        =       (image_size // patch_size) ** 2
        lowercase__:       Tuple        =       num_patches + 2
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase__:       int        =       None
        if self.use_labels:
               lowercase__:       str        =       ids_tensor([self.batch_size]    ,			self.type_sequence_label_size			)

        lowercase__:       List[str]        =       self.get_config()

        return config, pixel_values, labels
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        return DeiTConfig(
            image_size=self.image_size    ,			patch_size=self.patch_size    ,			num_channels=self.num_channels    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			is_decoder=lowerCAmelCase__    ,			initializer_range=self.initializer_range    ,			encoder_stride=self.encoder_stride    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    int:




        '''simple docstring'''
        lowercase__:       List[str]        =       TFDeiTModel(config=lowerCAmelCase__			)
        lowercase__:       Any        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Any        =       TFDeiTForMaskedImageModeling(config=lowerCAmelCase__			)
        lowercase__:       int        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(
            result.reconstruction.shape    ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

        # test greyscale images
        lowercase__:       Tuple        =       1
        lowercase__:       Optional[int]        =       TFDeiTForMaskedImageModeling(lowerCAmelCase__			)

        lowercase__:       int        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase__:       str        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(result.reconstruction.shape    ,			(self.batch_size, 1, self.image_size, self.image_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Any        =       self.type_sequence_label_size
        lowercase__:       Optional[Any]        =       TFDeiTForImageClassification(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       model(lowerCAmelCase__    ,			labels=lowerCAmelCase__			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase__:       Optional[Any]        =       1
        lowercase__:       Union[str, Any]        =       TFDeiTForImageClassification(lowerCAmelCase__			)

        lowercase__:       List[str]        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase__:       str        =       model(lowerCAmelCase__    ,			labels=lowerCAmelCase__			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Dict        =       self.prepare_config_and_inputs()
        lowercase__      ,						lowercase__      ,						lowercase__:       Dict        =       config_and_inputs
        lowercase__:       Optional[Any]        =       {'pixel_values': pixel_values}
        return config, inputs_dict





@require_tf
class   __a      (   __UpperCamelCase	,	__UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Union[str, Any]       					=			(
     (
         TFDeiTModel,
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 __lowercase			:							Union[str, Any]       					=			(
     {
         'feature-extraction': TFDeiTModel,
         'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
     }
     if is_tf_available()
     else {}
 )

 __lowercase			:							Union[str, Any]       					=			False
 __lowercase			:							Tuple       					=			False
 __lowercase			:							int       					=			False
 __lowercase			:							Optional[int]       					=			False
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       TFDeiTModelTester(self			)
        lowercase__:       int        =       ConfigTester(self    ,			config_class=lowerCAmelCase__    ,			has_text_modality=lowerCAmelCase__    ,			hidden_size=37			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        self.config_tester.run_common_tests()
 @unittest.skip(reason='DeiT does not use inputs_embeds'			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__      ,						lowercase__:       int        =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase__:       Tuple        =       model_class(lowerCAmelCase__			)
               self.assertIsInstance(model.get_input_embeddings()    ,			(tf.keras.layers.Layer)			)
               lowercase__:       Union[str, Any]        =       model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase__    ,			tf.keras.layers.Dense			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__      ,						lowercase__:       Union[str, Any]        =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase__:       Any        =       model_class(lowerCAmelCase__			)
               lowercase__:       Any        =       inspect.signature(model.call			)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase__:       Dict        =       [*signature.parameters.keys()]

               lowercase__:       Any        =       ['pixel_values']
               self.assertListEqual(arg_names[:1]    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Dict        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=False			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       super()._prepare_for_class(lowerCAmelCase__    ,			lowerCAmelCase__    ,			return_labels=lowerCAmelCase__			)

        if return_labels:
               if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call			).parameters:
                      del inputs_dict["labels"]

        return inputs_dict
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__:       Union[str, Any]        =       TFDeiTModel.from_pretrained(lowerCAmelCase__			)
               self.assertIsNotNone(lowerCAmelCase__			)





def        snake_case_					(  )  ->    Tuple:
       lowercase__:       Any        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image





@require_tf
@require_vision
class   __a      (   unittest.TestCase				):
 @cached_property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return (
            DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'			)
            if is_vision_available()
            else None
        )
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       str        =       TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'			)

        lowercase__:       str        =       self.default_image_processor
        lowercase__:       Dict        =       prepare_img()
        lowercase__:       Optional[int]        =       image_processor(images=lowerCAmelCase__    ,			return_tensors='tf'			)

        # forward pass
        lowercase__:       int        =       model(**lowerCAmelCase__			)

        # verify the logits
        lowercase__:       Tuple        =       tf.TensorShape((1, 1_000)			)
        self.assertEqual(outputs.logits.shape    ,			lowerCAmelCase__			)

        lowercase__:       List[Any]        =       tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]			)

        self.assertTrue(np.allclose(outputs.logits[0, :3]    ,			lowerCAmelCase__    ,			atol=1E-4			)			)


