



'''simple docstring'''



import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
							from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor
class 	lowerCamelCase_							:







						def __init__(  self   :      List[str]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Optional[int]=13				,    lowerCAmelCase__   :      Optional[Any]=30				,    lowerCAmelCase__   :      int=2				,    lowerCAmelCase__   :      Optional[Any]=3				,    lowerCAmelCase__   :      List[str]=True				,    lowerCAmelCase__   :      Optional[Any]=True				,    lowerCAmelCase__   :      Optional[int]=32				,    lowerCAmelCase__   :      Union[str, Any]=5				,    lowerCAmelCase__   :      Tuple=4				,    lowerCAmelCase__   :      Any=37				,    lowerCAmelCase__   :      Optional[int]="gelu"				,    lowerCAmelCase__   :      Any=0.1				,    lowerCAmelCase__   :      Union[str, Any]=0.1				,    lowerCAmelCase__   :      Union[str, Any]=10				,    lowerCAmelCase__   :      Union[str, Any]=0.02				,    lowerCAmelCase__   :      Optional[int]=None				,    ):

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

													# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			(image_size // patch_size) ** 2
													SCREAMING_SNAKE_CASE					:      int     					=			num_patches + 1







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			None
													if self.use_labels:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			ids_tensor([self.batch_size]				,    self.type_sequence_label_size				)

													SCREAMING_SNAKE_CASE					:      int     					=			self.get_config()

													return config, pixel_values, labels







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													return ViTMSNConfig(
													    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				,    initializer_range=self.initializer_range				,    )







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ViTMSNModel(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			model(lowerCAmelCase__				)
													self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)				)







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.type_sequence_label_size
													SCREAMING_SNAKE_CASE					:      int     					=			ViTMSNForImageClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      int     					=			model(lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''				)
													print('''Labels: {labels}'''				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.type_sequence_label_size)				)

													# test greyscale images
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			1
													SCREAMING_SNAKE_CASE					:      Tuple     					=			ViTMSNForImageClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()

													SCREAMING_SNAKE_CASE					:      str     					=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
													SCREAMING_SNAKE_CASE					:      int     					=			model(lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.type_sequence_label_size)				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{'''pixel_values''': pixel_values}
													return config, inputs_dict


@require_torch
class 	lowerCamelCase_							(					snake_case_    ,				snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   List[Any]	     =				(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						_lowerCAmelCase     :   str	     =				(
						    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_lowerCAmelCase     :   Any	     =				False
						_lowerCAmelCase     :   Dict	     =				False
						_lowerCAmelCase     :   Tuple	     =				False
						_lowerCAmelCase     :   Union[str, Any]	     =				False







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			ViTMSNModelTester(self				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ConfigTester(self				,    config_class=lowerCAmelCase__				,    has_text_modality=lowerCAmelCase__				,    hidden_size=37				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													self.config_tester.run_common_tests()







						@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''				)
						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													pass







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			model_class(lowerCAmelCase__				)
																				self.assertIsInstance(model.get_input_embeddings()				,    (nn.Module)				)
																				SCREAMING_SNAKE_CASE					:      Dict     					=			model.get_output_embeddings()
																				self.assertTrue(x is None or isinstance(lowerCAmelCase__				,    nn.Linear				)				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs_for_common()

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

																				SCREAMING_SNAKE_CASE					:      Tuple     					=			['''pixel_values''']
																				self.assertListEqual(arg_names[:1]				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			ViTMSNModel.from_pretrained(lowerCAmelCase__				)
																				self.assertIsNotNone(lowerCAmelCase__				)


def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image






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







						@cached_property
						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''				) if is_vision_available() else None







						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													torch.manual_seed(2				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''				).to(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.default_image_processor
													SCREAMING_SNAKE_CASE					:      Tuple     					=			prepare_img()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_processor(images=lowerCAmelCase__				,    return_tensors='''pt'''				).to(lowerCAmelCase__				)

													# forward pass
													with torch.no_grad():
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model(**lowerCAmelCase__				)

													# verify the logits
													SCREAMING_SNAKE_CASE					:      Any     					=			torch.Size((1, 10_00)				)
													self.assertEqual(outputs.logits.shape				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      str     					=			torch.tensor([-0.0803, -0.4454, -0.2375]				).to(lowerCAmelCase__				)

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









'''simple docstring'''



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 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   int	     =				['image_processor', 'tokenizer']
						_lowerCAmelCase     :   Union[str, Any]	     =				'LayoutLMv3ImageProcessor'
						_lowerCAmelCase     :   List[Any]	     =				('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int=None				,    lowerCAmelCase__   :      str=None				,    **lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			None
													if "feature_extractor" in kwargs:
																				warnings.warn(
																				    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																				    ''' instead.'''				,    lowerCAmelCase__				,    )
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			kwargs.pop('''feature_extractor'''				)

													SCREAMING_SNAKE_CASE					:      Any     					=			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__(lowerCAmelCase__				,    lowerCAmelCase__				)







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

													"""simple docstring"""
													# verify input
													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
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.image_processor(images=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				)

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

													SCREAMING_SNAKE_CASE					:      Tuple     					=			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=lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				,    padding=lowerCAmelCase__				,    truncation=lowerCAmelCase__				,    max_length=lowerCAmelCase__				,    stride=lowerCAmelCase__				,    pad_to_multiple_of=lowerCAmelCase__				,    return_token_type_ids=lowerCAmelCase__				,    return_attention_mask=lowerCAmelCase__				,    return_overflowing_tokens=lowerCAmelCase__				,    return_special_tokens_mask=lowerCAmelCase__				,    return_offsets_mapping=lowerCAmelCase__				,    return_length=lowerCAmelCase__				,    verbose=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				,    **lowerCAmelCase__				,    )

													# add pixel values
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			features.pop('''pixel_values'''				)
													if return_overflowing_tokens is True:
																				SCREAMING_SNAKE_CASE					:      Any     					=			self.get_overflowing_images(lowerCAmelCase__				,    encoded_inputs['''overflow_to_sample_mapping''']				)
													SCREAMING_SNAKE_CASE					:      Any     					=			images

													return encoded_inputs







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
													SCREAMING_SNAKE_CASE					:      Dict     					=			[]
													for sample_idx in overflow_to_sample_mapping:
																				images_with_overflow.append(images[sample_idx]				)

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

													return images_with_overflow







						def 					__lowercase    (  self   :      Any				,    *lowerCAmelCase__   :      Union[str, Any]				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													return self.tokenizer.batch_decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				,    *lowerCAmelCase__   :      Optional[Any]				,    **lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													return self.tokenizer.decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

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







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

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







						@property
						def 					__lowercase    (  self   :      List[Any]				):

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




