





"""simple docstring"""




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTMAEForPreTraining, ViTMAEModel
						from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor
class SCREAMING_SNAKE_CASE_  :






						def __init__( self : int						,				_A : Optional[int]						,				_A : Union[str, Any]=13						,				_A : Dict=30						,				_A : Any=2						,				_A : Tuple=3						,				_A : List[str]=True						,				_A : int=True						,				_A : str=32						,				_A : Dict=5						,				_A : List[str]=4						,				_A : Optional[Any]=37						,				_A : Optional[int]="gelu"						,				_A : str=0.1						,				_A : Optional[int]=0.1						,				_A : Any=10						,				_A : Optional[Any]=0.0_2						,				_A : int=3						,				_A : Optional[Any]=0.6						,				_A : Dict=None						,				)							->      List[str]:







							"""simple docstring"""


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

							# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
							# (we add 1 for the [CLS] token)
							snake_case_						:     List[str]     =		(image_size // patch_size) ** 2
							snake_case_						:     Any     =		int(math.ceil((1 - mask_ratio) * (num_patches + 1)		)		)






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







							"""simple docstring"""


							snake_case_						:     List[str]     =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

							snake_case_						:     Optional[int]     =		self.get_config()

							return config, pixel_values, labels






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







							"""simple docstring"""


							return ViTMAEConfig(
							    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=_A						,				initializer_range=self.initializer_range						,				mask_ratio=self.mask_ratio						,				)






						def UpperCAmelCase_			( self : Dict						,				_A : int						,				_A : str						,				_A : Union[str, Any]		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		ViTMAEModel(config=_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     Optional[int]     =		model(_A		)
							self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size)		)






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : Union[str, Any]						,				_A : List[str]						,				_A : List[str]		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		ViTMAEForPreTraining(_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     List[Any]     =		model(_A		)
							snake_case_						:     Optional[Any]     =		(self.image_size // self.patch_size) ** 2
							snake_case_						:     Optional[Any]     =		self.patch_size**2 * self.num_channels
							self.parent.assertEqual(result.logits.shape						,				(self.batch_size, num_patches, expected_num_channels)		)

							# test greyscale images
							snake_case_						:     List[Any]     =		1
							snake_case_						:     Dict     =		ViTMAEForPreTraining(_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     Union[str, Any]     =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
							snake_case_						:     List[Any]     =		model(_A		)
							snake_case_						:     Union[str, Any]     =		self.patch_size**2
							self.parent.assertEqual(result.logits.shape						,				(self.batch_size, num_patches, expected_num_channels)		)






						def UpperCAmelCase_			( self : Dict		)							->      str:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		self.prepare_config_and_inputs()
							snake_case_						:     str     =		config_and_inputs
							snake_case_						:     List[Any]     =		{'pixel_values': pixel_values}
							return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     Optional[int]	 =    (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
						__magic_name__:     int	 =    {"feature-extraction": ViTMAEModel} if is_torch_available() else {}

						__magic_name__:     Tuple	 =    False
						__magic_name__:     str	 =    False
						__magic_name__:     str	 =    False
						__magic_name__:     Union[str, Any]	 =    False






						def UpperCAmelCase_			( self : Any		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		ViTMAEModelTester(self		)
							snake_case_						:     Optional[int]     =		ConfigTester(self						,				config_class=_A						,				has_text_modality=_A						,				hidden_size=37		)






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







							"""simple docstring"""


							self.config_tester.run_common_tests()






						@unittest.skip(reason='ViTMAE does not use inputs_embeds'		)
						def UpperCAmelCase_			( self : Optional[int]		)							->      Any:







							"""simple docstring"""


							pass






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







							"""simple docstring"""


							snake_case_						:     List[Any]     =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_						:     int     =		model_class(_A		)
								self.assertIsInstance(model.get_input_embeddings()						,				(nn.Module)		)
								snake_case_						:     Union[str, Any]     =		model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(_A						,				nn.Linear		)		)






						def UpperCAmelCase_			( self : Tuple		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_						:     Any     =		model_class(_A		)
								snake_case_						:     Optional[int]     =		inspect.signature(model.forward		)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								snake_case_						:     Any     =		[*signature.parameters.keys()]

								snake_case_						:     List[str]     =		['pixel_values']
								self.assertListEqual(arg_names[:1]						,				_A		)






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







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A		)






						def UpperCAmelCase_			( self : Optional[int]		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*_A		)






						def UpperCAmelCase_			( self : str						,				_A : Union[str, Any]						,				_A : List[str]						,				_A : Tuple		)							->      Union[str, Any]:







							"""simple docstring"""


							np.random.seed(2		)

							snake_case_						:     Optional[Any]     =		int((pt_model.config.image_size // pt_model.config.patch_size) ** 2		)
							snake_case_						:     List[Any]     =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
							snake_case_						:     Optional[int]     =		torch.from_numpy(_A		)

							# Add `noise` argument.
							# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
							snake_case_						:     Optional[int]     =		pt_noise

							super().check_pt_tf_models(_A						,				_A						,				_A		)






						def UpperCAmelCase_			( self : int		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Dict     =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_						:     Optional[int]     =		model_class(_A		)
								model.to(_A		)
								model.eval()
								# make random mask reproducible
								torch.manual_seed(2		)
								with torch.no_grad():
									snake_case_						:     List[Any]     =		model(**self._prepare_for_class(_A						,				_A		)		)

								snake_case_						:     Optional[int]     =		outputs[0].cpu().numpy()
								snake_case_						:     Any     =		0

								with tempfile.TemporaryDirectory() as tmpdirname:
									model.save_pretrained(_A		)
									snake_case_						:     Union[str, Any]     =		model_class.from_pretrained(_A		)
									model.to(_A		)
									# make random mask reproducible
									torch.manual_seed(2		)
									with torch.no_grad():
										snake_case_						:     List[str]     =		model(**self._prepare_for_class(_A						,				_A		)		)

									# Make sure we don't have nans
									snake_case_						:     List[Any]     =		after_outputs[0].cpu().numpy()
									snake_case_						:     Union[str, Any]     =		0
									snake_case_						:     Optional[int]     =		np.amax(np.abs(out_a - out_a		)		)
									self.assertLessEqual(_A						,				1E-5		)






						@unittest.skip(
						    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'		)
						def UpperCAmelCase_			( self : Optional[int]		)							->      Optional[int]:







							"""simple docstring"""


							pass






						@unittest.skip(
						    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'		)
						def UpperCAmelCase_			( self : Optional[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							pass






						@unittest.skip(
						    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'		)
						def UpperCAmelCase_			( self : str		)							->      Any:







							"""simple docstring"""


							pass






						@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'		)
						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Optional[Any]:







							"""simple docstring"""


							pass






						@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Union[str, Any]:







							"""simple docstring"""


							pass






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







							"""simple docstring"""


							for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_						:     Any     =		ViTMAEModel.from_pretrained(_A		)
								self.assertIsNotNone(_A		)


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Optional[Any]     =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image


@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						@cached_property
						def UpperCAmelCase_			( self : List[str]		)							->      str:







							"""simple docstring"""


							return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'		) if is_vision_available() else None






						@slow
						def UpperCAmelCase_			( self : Dict		)							->      str:







							"""simple docstring"""


							np.random.seed(2		)

							snake_case_						:     Union[str, Any]     =		ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'		).to(_A		)

							snake_case_						:     Optional[Any]     =		self.default_image_processor
							snake_case_						:     Any     =		prepare_img()
							snake_case_						:     Any     =		image_processor(images=_A						,				return_tensors='pt'		).to(_A		)

							# prepare a noise vector that will be also used for testing the TF model
							# (this way we can ensure that the PT and TF models operate on the same inputs)
							snake_case_						:     int     =		ViTMAEConfig()
							snake_case_						:     Union[str, Any]     =		int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
							snake_case_						:     Any     =		np.random.uniform(size=(1, num_patches)		)

							# forward pass
							with torch.no_grad():
								snake_case_						:     int     =		model(**_A						,				noise=torch.from_numpy(_A		).to(device=_A		)		)

							# verify the logits
							snake_case_						:     int     =		torch.Size((1, 196, 768)		)
							self.assertEqual(outputs.logits.shape						,				_A		)

							snake_case_						:     List[str]     =		torch.tensor(
							    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]		)

							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]						,				expected_slice.to(_A		)						,				atol=1E-4		)		)








import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Optional[Any]	 =    ["image_processor", "tokenizer"]
						__magic_name__:     Optional[Any]	 =    "LayoutLMv3ImageProcessor"
						__magic_name__:     str	 =    ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






						def __init__( self : int						,				_A : List[str]=None						,				_A : Dict=None						,				**_A : int		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Any     =		None
							if "feature_extractor" in kwargs:
								warnings.warn(
								    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
								    ' instead.'						,				_A						,				)
								snake_case_						:     Any     =		kwargs.pop('feature_extractor'		)

							snake_case_						:     Optional[int]     =		image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
								raise ValueError('You need to specify an `image_processor`.'		)
							if tokenizer is None:
								raise ValueError('You need to specify a `tokenizer`.'		)

							super().__init__(_A						,				_A		)






						def __call__( self : List[str]						,				_A : Optional[Any]						,				_A : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,				_A : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,				_A : Union[List[List[int]], List[List[List[int]]]] = None						,				_A : Optional[Union[List[int], List[List[int]]]] = None						,				_A : bool = True						,				_A : Union[bool, str, PaddingStrategy] = False						,				_A : Union[bool, str, TruncationStrategy] = None						,				_A : Optional[int] = None						,				_A : int = 0						,				_A : Optional[int] = None						,				_A : Optional[bool] = None						,				_A : Optional[bool] = None						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = True						,				_A : Optional[Union[str, TensorType]] = None						,				**_A : str						,				)							->      BatchEncoding:







							"""simple docstring"""


							if self.image_processor.apply_ocr and (boxes is not None):
								raise ValueError(
								    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'		)

							if self.image_processor.apply_ocr and (word_labels is not None):
								raise ValueError(
								    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)

							# first, apply the image processor
							snake_case_						:     str     =		self.image_processor(images=_A						,				return_tensors=_A		)

							# second, apply the tokenizer
							if text is not None and self.image_processor.apply_ocr and text_pair is None:
								if isinstance(_A						,				_A		):
									snake_case_						:     List[Any]     =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
								snake_case_						:     str     =		features['words']

							snake_case_						:     Optional[int]     =		self.tokenizer(
							    text=text if text is not None else features['words']						,				text_pair=text_pair if text_pair is not None else None						,				boxes=boxes if boxes is not None else features['boxes']						,				word_labels=_A						,				add_special_tokens=_A						,				padding=_A						,				truncation=_A						,				max_length=_A						,				stride=_A						,				pad_to_multiple_of=_A						,				return_token_type_ids=_A						,				return_attention_mask=_A						,				return_overflowing_tokens=_A						,				return_special_tokens_mask=_A						,				return_offsets_mapping=_A						,				return_length=_A						,				verbose=_A						,				return_tensors=_A						,				**_A						,				)

							# add pixel values
							snake_case_						:     List[str]     =		features.pop('pixel_values'		)
							if return_overflowing_tokens is True:
								snake_case_						:     Dict     =		self.get_overflowing_images(_A						,				encoded_inputs['overflow_to_sample_mapping']		)
							snake_case_						:     Optional[Any]     =		images

							return encoded_inputs






						def UpperCAmelCase_			( self : Dict						,				_A : Tuple						,				_A : Dict		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		[]
							for sample_idx in overflow_to_sample_mapping:
								images_with_overflow.append(images[sample_idx]		)

							if len(_A		) != len(_A		):
								raise ValueError(
								    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
								    F""" {len(_A		)} and {len(_A		)}"""		)

							return images_with_overflow






						def UpperCAmelCase_			( self : Optional[Any]						,				*_A : Optional[Any]						,				**_A : List[Any]		)							->      List[str]:







							"""simple docstring"""


							return self.tokenizer.batch_decode(*_A						,				**_A		)






						def UpperCAmelCase_			( self : Union[str, Any]						,				*_A : Dict						,				**_A : str		)							->      Any:







							"""simple docstring"""


							return self.tokenizer.decode(*_A						,				**_A		)






						@property
						def UpperCAmelCase_			( self : Optional[int]		)							->      int:







							"""simple docstring"""


							return ["input_ids", "bbox", "attention_mask", "pixel_values"]






						@property
						def UpperCAmelCase_			( self : Any		)							->      Any:







							"""simple docstring"""


							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,				_A						,				)
							return self.image_processor_class






						@property
						def UpperCAmelCase_			( self : List[Any]		)							->      int:







							"""simple docstring"""


							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,				_A						,				)
							return self.image_processor

