

'''simple docstring'''

from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    LayoutLMvaConfig,
	    TFLayoutLMvaForQuestionAnswering,
	    TFLayoutLMvaForSequenceClassification,
	    TFLayoutLMvaForTokenClassification,
	    TFLayoutLMvaModel,
	)

if is_vision_available():
	from PIL import Image

	from transformers import LayoutLMvaImageProcessor




class 	A_			:






					'''simple docstring'''



					def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					int=2					,      lowercase_			:					Any=3					,      lowercase_			:					List[Any]=4					,      lowercase_			:					Any=2					,      lowercase_			:					Union[str, Any]=7					,      lowercase_			:					Tuple=True					,      lowercase_			:					Dict=True					,      lowercase_			:					Tuple=True					,      lowercase_			:					Tuple=True					,      lowercase_			:					List[str]=99					,      lowercase_			:					List[Any]=36					,      lowercase_			:					int=2					,      lowercase_			:					str=4					,      lowercase_			:					Union[str, Any]=37					,      lowercase_			:					Tuple="gelu"					,      lowercase_			:					List[Any]=0.1					,      lowercase_			:					int=0.1					,      lowercase_			:					Union[str, Any]=512					,      lowercase_			:					str=16					,      lowercase_			:					Union[str, Any]=2					,      lowercase_			:					Dict=0.02					,      lowercase_			:					str=6					,      lowercase_			:					Union[str, Any]=6					,      lowercase_			:					str=3					,      lowercase_			:					Optional[int]=4					,      lowercase_			:					int=None					,      lowercase_			:					Optional[int]=1_000					,      )						->     List[Any]:
						UpperCAmelCase	:     Tuple						 =			parent
						UpperCAmelCase	:     Tuple						 =			batch_size
						UpperCAmelCase	:     Any						 =			num_channels
						UpperCAmelCase	:     Optional[int]						 =			image_size
						UpperCAmelCase	:     str						 =			patch_size
						UpperCAmelCase	:     Dict						 =			is_training
						UpperCAmelCase	:     int						 =			use_input_mask
						UpperCAmelCase	:     str						 =			use_token_type_ids
						UpperCAmelCase	:     str						 =			use_labels
						UpperCAmelCase	:     List[str]						 =			vocab_size
						UpperCAmelCase	:     List[Any]						 =			hidden_size
						UpperCAmelCase	:     Optional[int]						 =			num_hidden_layers
						UpperCAmelCase	:     str						 =			num_attention_heads
						UpperCAmelCase	:     Any						 =			intermediate_size
						UpperCAmelCase	:     List[Any]						 =			hidden_act
						UpperCAmelCase	:     Dict						 =			hidden_dropout_prob
						UpperCAmelCase	:     int						 =			attention_probs_dropout_prob
						UpperCAmelCase	:     int						 =			max_position_embeddings
						UpperCAmelCase	:     List[str]						 =			type_vocab_size
						UpperCAmelCase	:     Union[str, Any]						 =			type_sequence_label_size
						UpperCAmelCase	:     List[str]						 =			initializer_range
						UpperCAmelCase	:     Any						 =			coordinate_size
						UpperCAmelCase	:     List[Any]						 =			shape_size
						UpperCAmelCase	:     List[Any]						 =			num_labels
						UpperCAmelCase	:     str						 =			num_choices
						UpperCAmelCase	:     Optional[int]						 =			scope
						UpperCAmelCase	:     List[Any]						 =			range_bbox

						# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
						UpperCAmelCase	:     Dict						 =			text_seq_length
						UpperCAmelCase	:     List[str]						 =			(image_size // patch_size) ** 2 + 1
						UpperCAmelCase	:     Optional[int]						 =			self.text_seq_length + self.image_seq_length


					def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Dict:
						UpperCAmelCase	:     int						 =			ids_tensor([self.batch_size, self.text_seq_length]					,      self.vocab_size	)

						UpperCAmelCase	:     Union[str, Any]						 =			ids_tensor([self.batch_size, self.text_seq_length, 4]					,      self.range_bbox	)
						UpperCAmelCase	:     List[Any]						 =			bbox.numpy()
						# Ensure that bbox is legal
						for i in range(bbox.shape[0]	):
							for j in range(bbox.shape[1]	):
								if bbox[i, j, 3] < bbox[i, j, 1]:
									UpperCAmelCase	:     str						 =			bbox[i, j, 3]
									UpperCAmelCase	:     Dict						 =			bbox[i, j, 1]
									UpperCAmelCase	:     Tuple						 =			tmp_coordinate
								if bbox[i, j, 2] < bbox[i, j, 0]:
									UpperCAmelCase	:     Any						 =			bbox[i, j, 2]
									UpperCAmelCase	:     Any						 =			bbox[i, j, 0]
									UpperCAmelCase	:     Dict						 =			tmp_coordinate
						UpperCAmelCase	:     Optional[Any]						 =			tf.constant(__lowercase	)

						UpperCAmelCase	:     List[Any]						 =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

						UpperCAmelCase	:     List[str]						 =			None
						if self.use_input_mask:
							UpperCAmelCase	:     Any						 =			random_attention_mask([self.batch_size, self.text_seq_length]	)

						UpperCAmelCase	:     Optional[int]						 =			None
						if self.use_token_type_ids:
							UpperCAmelCase	:     Tuple						 =			ids_tensor([self.batch_size, self.text_seq_length]					,      self.type_vocab_size	)

						UpperCAmelCase	:     int						 =			None
						UpperCAmelCase	:     Optional[int]						 =			None
						if self.use_labels:
							UpperCAmelCase	:     int						 =			ids_tensor([self.batch_size]					,      self.type_sequence_label_size	)
							UpperCAmelCase	:     int						 =			ids_tensor([self.batch_size, self.text_seq_length]					,      self.num_labels	)

						UpperCAmelCase	:     Optional[int]						 =			LayoutLMvaConfig(
						    vocab_size=self.vocab_size					,      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					,      max_position_embeddings=self.max_position_embeddings					,      type_vocab_size=self.type_vocab_size					,      initializer_range=self.initializer_range					,      coordinate_size=self.coordinate_size					,      shape_size=self.shape_size					,      input_size=self.image_size					,      patch_size=self.patch_size					,      )

						return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels


					def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					List[str]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					List[str]					,      lowercase_			:					List[Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Union[str, Any]	)						->     Union[str, Any]:
						UpperCAmelCase	:     str						 =			TFLayoutLMvaModel(config=__lowercase	)

						# text + image
						UpperCAmelCase	:     Dict						 =			model(__lowercase					,      pixel_values=__lowercase					,      training=__lowercase	)
						UpperCAmelCase	:     Tuple						 =			model(
						    __lowercase					,      bbox=__lowercase					,      pixel_values=__lowercase					,      attention_mask=__lowercase					,      token_type_ids=__lowercase					,      training=__lowercase					,      )
						UpperCAmelCase	:     Tuple						 =			model(__lowercase					,      bbox=__lowercase					,      pixel_values=__lowercase					,      training=__lowercase	)

						self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)	)

						# text only
						UpperCAmelCase	:     Optional[int]						 =			model(__lowercase					,      training=__lowercase	)
						self.parent.assertEqual(
						    result.last_hidden_state.shape					,      (self.batch_size, self.text_seq_length, self.hidden_size)	)

						# image only
						UpperCAmelCase	:     List[Any]						 =			model({'pixel_values': pixel_values}					,      training=__lowercase	)
						self.parent.assertEqual(
						    result.last_hidden_state.shape					,      (self.batch_size, self.image_seq_length, self.hidden_size)	)


					def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					str					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Any					,      lowercase_			:					Tuple					,      lowercase_			:					Tuple					,      lowercase_			:					Any					,      lowercase_			:					int	)						->     Optional[int]:
						UpperCAmelCase	:     int						 =			self.num_labels
						UpperCAmelCase	:     Union[str, Any]						 =			TFLayoutLMvaForSequenceClassification(config=__lowercase	)
						UpperCAmelCase	:     Optional[int]						 =			model(
						    __lowercase					,      bbox=__lowercase					,      pixel_values=__lowercase					,      attention_mask=__lowercase					,      token_type_ids=__lowercase					,      labels=__lowercase					,      training=__lowercase					,      )
						self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)	)


					def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					Dict					,      lowercase_			:					List[Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Any					,      lowercase_			:					str	)						->     str:
						UpperCAmelCase	:     int						 =			self.num_labels
						UpperCAmelCase	:     Dict						 =			TFLayoutLMvaForTokenClassification(config=__lowercase	)
						UpperCAmelCase	:     Union[str, Any]						 =			model(
						    __lowercase					,      bbox=__lowercase					,      pixel_values=__lowercase					,      attention_mask=__lowercase					,      token_type_ids=__lowercase					,      labels=__lowercase					,      training=__lowercase					,      )
						self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.text_seq_length, self.num_labels)	)


					def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					str					,      lowercase_			:					List[str]					,      lowercase_			:					List[str]					,      lowercase_			:					List[Any]					,      lowercase_			:					Dict					,      lowercase_			:					Dict					,      lowercase_			:					str	)						->     Optional[Any]:
						UpperCAmelCase	:     Optional[int]						 =			2
						UpperCAmelCase	:     Union[str, Any]						 =			TFLayoutLMvaForQuestionAnswering(config=__lowercase	)
						UpperCAmelCase	:     Optional[int]						 =			model(
						    __lowercase					,      bbox=__lowercase					,      pixel_values=__lowercase					,      attention_mask=__lowercase					,      token_type_ids=__lowercase					,      start_positions=__lowercase					,      end_positions=__lowercase					,      training=__lowercase					,      )
						self.parent.assertEqual(result.start_logits.shape					,      (self.batch_size, self.seq_length)	)
						self.parent.assertEqual(result.end_logits.shape					,      (self.batch_size, self.seq_length)	)




					def        UpperCAmelCase_	(		self			:					str	)						->     Optional[Any]:
						UpperCAmelCase	:     int						 =			self.prepare_config_and_inputs()
						((UpperCAmelCase) ,			(UpperCAmelCase) ,			(UpperCAmelCase) ,			(UpperCAmelCase) ,			(UpperCAmelCase) ,			(UpperCAmelCase) ,			(UpperCAmelCase) ,			(UpperCAmelCase))	:     Union[str, Any]						 =			config_and_inputs
						UpperCAmelCase	:     Union[str, Any]						 =			{
						    'input_ids': input_ids,
						    'bbox': bbox,
						    'pixel_values': pixel_values,
						    'token_type_ids': token_type_ids,
						    'attention_mask': input_mask,
						}
						return config, inputs_dict




@require_tf
class 	A_			(    __A	,       __A	,       unittest.TestCase							):






					'''simple docstring'''



					UpperCAmelCase_						:     Optional[Any]  		=  (
					    (
					        TFLayoutLMvaModel,
					        TFLayoutLMvaForQuestionAnswering,
					        TFLayoutLMvaForSequenceClassification,
					        TFLayoutLMvaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCAmelCase_						:     Optional[Any]  		=  (
					    {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
					    if is_tf_available()
					    else {}
					)

					UpperCAmelCase_						:     Union[str, Any]  		=  False
					UpperCAmelCase_						:     List[Any]  		=  False
					UpperCAmelCase_						:     Union[str, Any]  		=  False


					def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					Dict					,      lowercase_			:					Optional[Any]					,      lowercase_			:					str					,      lowercase_			:					int					,      lowercase_			:					List[Any]	)						->     Any:
						return True


					def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					Optional[int]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Dict=False	)						->     str:
						UpperCAmelCase	:     Optional[Any]						 =			copy.deepcopy(__lowercase	)

						if model_class in get_values(__lowercase	):
							UpperCAmelCase	:     Optional[int]						 =			{
							    k: tf.tile(tf.expand_dims(__lowercase					,      1	)					,      (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)	)
							    if isinstance(__lowercase					,      tf.Tensor	) and v.ndim > 0
							    else v
							    for k, v in inputs_dict.items()
							}

						if return_labels:
							if model_class in get_values(__lowercase	):
								UpperCAmelCase	:     int						 =			tf.ones(self.model_tester.batch_size					,      dtype=tf.intaa	)
							elif model_class in get_values(__lowercase	):
								UpperCAmelCase	:     Optional[int]						 =			tf.zeros(self.model_tester.batch_size					,      dtype=tf.intaa	)
								UpperCAmelCase	:     Optional[int]						 =			tf.zeros(self.model_tester.batch_size					,      dtype=tf.intaa	)
							elif model_class in get_values(__lowercase	):
								UpperCAmelCase	:     Optional[Any]						 =			tf.zeros(self.model_tester.batch_size					,      dtype=tf.intaa	)
							elif model_class in get_values(__lowercase	):
								UpperCAmelCase	:     int						 =			tf.zeros(
								    (self.model_tester.batch_size, self.model_tester.text_seq_length)					,      dtype=tf.intaa	)

						return inputs_dict


					def        UpperCAmelCase_	(		self			:					str	)						->     str:
						UpperCAmelCase	:     str						 =			TFLayoutLMvaModelTester(self	)
						UpperCAmelCase	:     Union[str, Any]						 =			ConfigTester(self					,      config_class=__lowercase					,      hidden_size=37	)


					def        UpperCAmelCase_	(		self			:					Dict	)						->     Union[str, Any]:
						self.config_tester.run_common_tests()


					def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Any:
						UpperCAmelCase ,			UpperCAmelCase	:     Optional[Any]						 =			self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
							UpperCAmelCase	:     List[str]						 =			model_class(__lowercase	)
							if getattr(__lowercase					,      'hf_compute_loss'					,      __lowercase	):
								# The number of elements in the loss should be the same as the number of elements in the label
								UpperCAmelCase	:     Dict						 =			self._prepare_for_class(inputs_dict.copy()					,      __lowercase					,      return_labels=__lowercase	)
								UpperCAmelCase	:     int						 =			prepared_for_class[
								    sorted(prepared_for_class.keys() - inputs_dict.keys()					,      reverse=__lowercase	)[0]
								]
								UpperCAmelCase	:     str						 =			added_label.shape.as_list()[:1]

								# Test that model correctly compute the loss with kwargs
								UpperCAmelCase	:     int						 =			self._prepare_for_class(inputs_dict.copy()					,      __lowercase					,      return_labels=__lowercase	)
								UpperCAmelCase	:     List[str]						 =			prepared_for_class.pop('input_ids'	)

								UpperCAmelCase	:     Dict						 =			model(__lowercase					,      **__lowercase	)[0]
								self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

								# Test that model correctly compute the loss when we mask some positions
								UpperCAmelCase	:     Optional[Any]						 =			self._prepare_for_class(inputs_dict.copy()					,      __lowercase					,      return_labels=__lowercase	)
								UpperCAmelCase	:     str						 =			prepared_for_class.pop('input_ids'	)
								if "labels" in prepared_for_class:
									UpperCAmelCase	:     int						 =			prepared_for_class['labels'].numpy()
									if len(labels.shape	) > 1 and labels.shape[1] != 1:
										UpperCAmelCase	:     Optional[int]						 =			-100
										UpperCAmelCase	:     Optional[int]						 =			tf.convert_to_tensor(__lowercase	)
										UpperCAmelCase	:     Tuple						 =			model(__lowercase					,      **__lowercase	)[0]
										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)
										self.assertTrue(not np.any(np.isnan(loss.numpy()	)	)	)

                # Test that model correctly compute the loss with a dict
								UpperCAmelCase	:     Dict						 =			self._prepare_for_class(inputs_dict.copy()					,      __lowercase					,      return_labels=__lowercase	)
								UpperCAmelCase	:     Optional[Any]						 =			model(__lowercase	)[0]
								self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

								# Test that model correctly compute the loss with a tuple
								UpperCAmelCase	:     List[Any]						 =			self._prepare_for_class(inputs_dict.copy()					,      __lowercase					,      return_labels=__lowercase	)

								# Get keys that were added with the _prepare_for_class function
								UpperCAmelCase	:     List[Any]						 =			prepared_for_class.keys() - inputs_dict.keys()
								UpperCAmelCase	:     Any						 =			inspect.signature(model.call	).parameters
								UpperCAmelCase	:     int						 =			list(signature.keys()	)

								# Create a dictionary holding the location of the tensors in the tuple
								UpperCAmelCase	:     Optional[int]						 =			{0: 'input_ids'}
								for label_key in label_keys:
									UpperCAmelCase	:     Any						 =			signature_names.index(__lowercase	)
									UpperCAmelCase	:     str						 =			label_key
								UpperCAmelCase	:     Optional[Any]						 =			sorted(tuple_index_mapping.items()	)
								# Initialize a list with their default values, update the values and convert to a tuple
								UpperCAmelCase	:     Tuple						 =			[]

								for name in signature_names:
									if name != "kwargs":
										list_input.append(signature[name].default	)

								for index, value in sorted_tuple_index_mapping:
									UpperCAmelCase	:     Dict						 =			prepared_for_class[value]

								UpperCAmelCase	:     List[Any]						 =			tuple(__lowercase	)

								# Send to model
								UpperCAmelCase	:     Any						 =			model(tuple_input[:-1]	)[0]

								self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)


					def        UpperCAmelCase_	(		self			:					int	)						->     List[Any]:
						(
						    (
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			   
						)	:     List[Any]						 =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(__lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase	)


					def        UpperCAmelCase_	(		self			:					List[Any]	)						->     List[str]:
						(
						    (
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			   
						)	:     List[Any]						 =			self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
							UpperCAmelCase	:     Any						 =			type
							self.model_tester.create_and_check_model(__lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase	)


					def        UpperCAmelCase_	(		self			:					int	)						->     List[Any]:
						(
						    (
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			   
						)	:     List[str]						 =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(
						    __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase	)


					def        UpperCAmelCase_	(		self			:					str	)						->     Optional[int]:
						(
						    (
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			   
						)	:     Optional[int]						 =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(
						    __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase	)


					def        UpperCAmelCase_	(		self			:					List[str]	)						->     Tuple:
						(
						    (
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			(
						    UpperCAmelCase   
						) ,			   
						)	:     List[Any]						 =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(
						    __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase	)




					@slow
					def        UpperCAmelCase_	(		self			:					Dict	)						->     Dict:
						for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase	:     str						 =			TFLayoutLMvaModel.from_pretrained(__lowercase	)
							self.assertIsNotNone(__lowercase	)




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




@require_tf
class 	A_			(    unittest.TestCase							):






					'''simple docstring'''



					@cached_property
					def        UpperCAmelCase_	(		self			:					int	)						->     Union[str, Any]:
						return LayoutLMvaImageProcessor(apply_ocr=__lowercase	) if is_vision_available() else None




					@slow
					def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[Any]:
						UpperCAmelCase	:     List[Any]						 =			TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'	)

						UpperCAmelCase	:     str						 =			self.default_image_processor
						UpperCAmelCase	:     Any						 =			prepare_img()
						UpperCAmelCase	:     Optional[Any]						 =			image_processor(images=__lowercase					,      return_tensors='tf'	).pixel_values

						UpperCAmelCase	:     List[str]						 =			tf.constant([[1, 2]]	)
						UpperCAmelCase	:     str						 =			tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]	)					,      axis=0	)

						# forward pass
						UpperCAmelCase	:     Optional[int]						 =			model(input_ids=__lowercase					,      bbox=__lowercase					,      pixel_values=__lowercase					,      training=__lowercase	)

						# verify the logits
						UpperCAmelCase	:     List[Any]						 =			(1, 199, 768)
						self.assertEqual(outputs.last_hidden_state.shape					,      __lowercase	)

						UpperCAmelCase	:     str						 =			tf.constant(
						    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]	)

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







'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			b.T
 UpperCAmelCase	:     Optional[int]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=1     )
 UpperCAmelCase	:     List[Any]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=0     )
 UpperCAmelCase	:     List[str]						 =			np.matmul(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			aa[:, None] - 2 * ab + ba[None, :]
 return d




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			x.reshape(-1  ,	3     )
 UpperCAmelCase	:     Optional[int]						 =			squared_euclidean_distance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return np.argmin(UpperCAmelCase_  ,	axis=1     )




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  ["""pixel_values"""]


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Any						 =			size if size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			np.array(lowercase_	) if clusters is not None else None
      UpperCAmelCase	:     Any						 =			do_resize
      UpperCAmelCase	:     List[Any]						 =			size
      UpperCAmelCase	:     Any						 =			resample
      UpperCAmelCase	:     Dict						 =			do_normalize
      UpperCAmelCase	:     List[Any]						 =			do_color_quantize


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     np.ndarray:
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_	)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""	)
      return resize(
          lowercase_					,      size=(size['height'], size['width'])					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      )						->     np.ndarray:
      UpperCAmelCase	:     int						 =			rescale(image=lowercase_					,      scale=1 / 127.5					,      data_format=lowercase_	)
      UpperCAmelCase	:     Dict						 =			image - 1
      return image




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[bool] = None					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,      **lowercase_			:					List[str]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Optional[int]						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     Optional[Any]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     Any						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     Optional[Any]						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCAmelCase	:     Optional[int]						 =			clusters if clusters is not None else self.clusters
      UpperCAmelCase	:     List[str]						 =			np.array(lowercase_	)

      UpperCAmelCase	:     int						 =			make_list_of_images(lowercase_	)

      if not valid_images(lowercase_	):
       raise ValueError(
           'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
           'torch.Tensor, tf.Tensor or jax.ndarray.'	)

      if do_resize and size is None or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'	)

      if do_color_quantize and clusters is None:
       raise ValueError('Clusters must be specified if do_color_quantize is True.'	)

      # All transformations expect numpy arrays.
      UpperCAmelCase	:     Dict						 =			[to_numpy_array(lowercase_	) for image in images]

      if do_resize:
       UpperCAmelCase	:     List[Any]						 =			[self.resize(image=lowercase_					,      size=lowercase_					,      resample=lowercase_	) for image in images]

      if do_normalize:
       UpperCAmelCase	:     Tuple						 =			[self.normalize(image=lowercase_	) for image in images]

      if do_color_quantize:
       UpperCAmelCase	:     List[str]						 =			[to_channel_dimension_format(lowercase_					,      ChannelDimension.LAST	) for image in images]
       # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
       UpperCAmelCase	:     int						 =			np.array(lowercase_	)
       UpperCAmelCase	:     str						 =			color_quantize(lowercase_					,      lowercase_	).reshape(images.shape[:-1]	)

       # flatten to (batch_size, height*width)
       UpperCAmelCase	:     Optional[int]						 =			images.shape[0]
       UpperCAmelCase	:     Union[str, Any]						 =			images.reshape(lowercase_					,      -1	)

       # We need to convert back to a list of images to keep consistent behaviour across processors.
       UpperCAmelCase	:     int						 =			list(lowercase_	)
      else:
       UpperCAmelCase	:     Dict						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     Any						 =			{'input_ids': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




