





'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
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 TFViTForImageClassification, TFViTModel


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def __init__(  self     :					Dict					,				__a     :					int					,				__a     :					int=13					,				__a     :					Dict=30					,				__a     :					List[str]=2					,				__a     :					Union[str, Any]=3					,				__a     :					Optional[Any]=True					,				__a     :					Optional[Any]=True					,				__a     :					Tuple=32					,				__a     :					List[Any]=2					,				__a     :					int=4					,				__a     :					Optional[Any]=37					,				__a     :					Tuple="gelu"					,				__a     :					Dict=0.1					,				__a     :					List[str]=0.1					,				__a     :					int=10					,				__a     :					int=0.02					,				__a     :					Tuple=3					,				__a     :					List[str]=None					,				):
								_a								= parent
								_a								= batch_size
								_a								= image_size
								_a								= patch_size
								_a								= num_channels
								_a								= is_training
								_a								= use_labels
								_a								= hidden_size
								_a								= num_hidden_layers
								_a								= num_attention_heads
								_a								= intermediate_size
								_a								= hidden_act
								_a								= hidden_dropout_prob
								_a								= attention_probs_dropout_prob
								_a								= type_sequence_label_size
								_a								= initializer_range
								_a								= scope

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




				def   UpperCamelCase__   (  self     :					int					):
								_a								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

								_a								= self.get_config()

								return config, pixel_values, labels




				def   UpperCamelCase__   (  self     :					Dict					):
								return ViTConfig(
								    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					,				)




				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					int					,				__a     :					Tuple					,				__a     :					Optional[Any]					):
								_a								= TFViTModel(config=__a					)
								_a								= model(__a					,				training=__a					)
								self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)					)

								# Test with an image with different size than the one specified in config.
								_a								= self.image_size // 2
								_a								= pixel_values[:, :, :image_size, :image_size]
								_a								= model(__a					,				interpolate_pos_encoding=__a					,				training=__a					)
								_a								= (image_size // self.patch_size) ** 2 + 1
								self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, seq_length, self.hidden_size)					)




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					List[Any]					,				__a     :					Optional[int]					,				__a     :					List[str]					):
								_a								= self.type_sequence_label_size
								_a								= TFViTForImageClassification(__a					)
								_a								= model(__a					,				labels=__a					,				training=__a					)
								self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)					)

								# Test with an image with different size than the one specified in config.
								_a								= self.image_size // 2
								_a								= pixel_values[:, :, :image_size, :image_size]
								_a								= model(__a					,				interpolate_pos_encoding=__a					,				training=__a					)
								self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)					)

								# test greyscale images
								_a								= 1
								_a								= TFViTForImageClassification(__a					)
								_a								= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
								_a								= model(__a					)
								self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= self.prepare_config_and_inputs()
								_a	,					_a	,					_a								= config_and_inputs
								_a								= {"pixel_values": pixel_values}
								return config, inputs_dict


@require_tf
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
				__a            =(
				    {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
				    if is_tf_available()
				    else {}
				)

				__a            =False
				__a            =False
				__a            =False




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= TFViTModelTester(self					)
								_a								= ConfigTester(self					,				config_class=__a					,				has_text_modality=__a					,				hidden_size=37					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								self.config_tester.run_common_tests()




				@unittest.skip(reason="ViT does not use inputs_embeds"					)
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								pass




				@unittest.skip(reason="ViT does not use inputs_embeds"					)
				def   UpperCamelCase__   (  self     :					str					):
								pass




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a	,					_a								= self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_a								= model_class(__a					)
												self.assertIsInstance(model.get_input_embeddings()					,				(tf.keras.layers.Layer)					)
												_a								= model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__a					,				tf.keras.layers.Layer					)					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a	,					_a								= self.model_tester.prepare_config_and_inputs_for_common()

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

												_a								= ["pixel_values"]
												self.assertListEqual(arg_names[:1]					,				__a					)




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a					)




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__a					)




				@slow
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= TFViTModel.from_pretrained("google/vit-base-patch16-224"					)
								self.assertIsNotNone(__a					)






def  _lowerCamelCase				( )	->	int:
				_a								= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image



@require_tf
@require_vision
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				@cached_property
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"					) if is_vision_available() else None




				@slow
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"					)

								_a								= self.default_image_processor
								_a								= prepare_img()
								_a								= image_processor(images=__a					,				return_tensors="tf"					)

								# forward pass
								_a								= model(**__a					)

								# verify the logits
								_a								= tf.TensorShape((1, 10_00)					)
								self.assertEqual(outputs.logits.shape					,				__a					)

								_a								= tf.constant([-0.2744, 0.8215, -0.0836]					)

								tf.debugging.assert_near(outputs.logits[0, :3]					,				__a					,				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 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =['image_processor', 'tokenizer']
				__a            ='LayoutLMv3ImageProcessor'
				__a            =('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')




				def __init__(  self     :					Tuple					,				__a     :					int=None					,				__a     :					Union[str, Any]=None					,				**__a     :					Optional[Any]					):
								_a								= 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					,				)
												_a								= kwargs.pop("feature_extractor"					)

								_a								= 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     :					Any					,				__a     :					List[str]					,				__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     :					Dict					,				):
								# 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
								_a								= 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					):
																_a								= [text]  # add batch dimension (as the image processor always adds a batch dimension)
												_a								= features["words"]

								_a								= 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
								_a								= features.pop("pixel_values"					)
								if return_overflowing_tokens is True:
												_a								= self.get_overflowing_images(__a					,				encoded_inputs["overflow_to_sample_mapping"]					)
								_a								= images

								return encoded_inputs




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					List[Any]					):
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								_a								= []
								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     :					int					,				*__a     :					str					,				**__a     :					Tuple					):
								return self.tokenizer.batch_decode(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					str					,				*__a     :					List[Any]					,				**__a     :					List[str]					):
								return self.tokenizer.decode(*__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					Tuple					):
								return ["input_ids", "bbox", "attention_mask", "pixel_values"]




				@property
				def   UpperCamelCase__   (  self     :					int					):
								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[str]					):
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,				__a					,				)
								return self.image_processor
