



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 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_=2		,       lowerCamelCase_=3		,       lowerCamelCase_=4		,       lowerCamelCase_=2		,       lowerCamelCase_=7		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=99		,       lowerCamelCase_=36		,       lowerCamelCase_=2		,       lowerCamelCase_=4		,       lowerCamelCase_=37		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.1		,       lowerCamelCase_=512		,       lowerCamelCase_=16		,       lowerCamelCase_=2		,       lowerCamelCase_=0.02		,       lowerCamelCase_=6		,       lowerCamelCase_=6		,       lowerCamelCase_=3		,       lowerCamelCase_=4		,       lowerCamelCase_=None		,       lowerCamelCase_=1000		,       ):
						"""simple docstring"""

						a					     =   parent
						a					     =   batch_size
						a					     =   num_channels
						a					     =   image_size
						a					     =   patch_size
						a					     =   is_training
						a					     =   use_input_mask
						a					     =   use_token_type_ids
						a					     =   use_labels
						a					     =   vocab_size
						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					     =   max_position_embeddings
						a					     =   type_vocab_size
						a					     =   type_sequence_label_size
						a					     =   initializer_range
						a					     =   coordinate_size
						a					     =   shape_size
						a					     =   num_labels
						a					     =   num_choices
						a					     =   scope
						a					     =   range_bbox

						# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
						a					     =   text_seq_length
						a					     =   (image_size // patch_size) ** 2 + 1
						a					     =   self.text_seq_length + self.image_seq_length




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   ids_tensor([self.batch_size, self.text_seq_length]		,       self.vocab_size		)

						a					     =   ids_tensor([self.batch_size, self.text_seq_length, 4]		,       self.range_bbox		)
						a					     =   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]:
																					a					     =   bbox[i, j, 3]
																					a					     =   bbox[i, j, 1]
																					a					     =   tmp_coordinate
																if bbox[i, j, 2] < bbox[i, j, 0]:
																					a					     =   bbox[i, j, 2]
																					a					     =   bbox[i, j, 0]
																					a					     =   tmp_coordinate
						a					     =   tf.constant(lowerCamelCase_		)

						a					     =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						a					     =   None
						if self.use_input_mask:
											a					     =   random_attention_mask([self.batch_size, self.text_seq_length]		)

						a					     =   None
						if self.use_token_type_ids:
											a					     =   ids_tensor([self.batch_size, self.text_seq_length]		,       self.type_vocab_size		)

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

						a					     =   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		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   TFLayoutLMvaModel(config=lowerCamelCase_		)

						# text + image
						a					     =   model(lowerCamelCase_		,       pixel_values=lowerCamelCase_		,       training=lowerCamelCase_		)
						a					     =   model(
						    lowerCamelCase_		,       bbox=lowerCamelCase_		,       pixel_values=lowerCamelCase_		,       attention_mask=lowerCamelCase_		,       token_type_ids=lowerCamelCase_		,       training=lowerCamelCase_		,       )
						a					     =   model(lowerCamelCase_		,       bbox=lowerCamelCase_		,       pixel_values=lowerCamelCase_		,       training=lowerCamelCase_		)

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

						# text only
						a					     =   model(lowerCamelCase_		,       training=lowerCamelCase_		)
						self.parent.assertEqual(
						    result.last_hidden_state.shape		,       (self.batch_size, self.text_seq_length, self.hidden_size)		)

						# image only
						a					     =   model({"pixel_values": pixel_values}		,       training=lowerCamelCase_		)
						self.parent.assertEqual(
						    result.last_hidden_state.shape		,       (self.batch_size, self.image_seq_length, self.hidden_size)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.num_labels
						a					     =   TFLayoutLMvaForSequenceClassification(config=lowerCamelCase_		)
						a					     =   model(
						    lowerCamelCase_		,       bbox=lowerCamelCase_		,       pixel_values=lowerCamelCase_		,       attention_mask=lowerCamelCase_		,       token_type_ids=lowerCamelCase_		,       labels=lowerCamelCase_		,       training=lowerCamelCase_		,       )
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_labels)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.num_labels
						a					     =   TFLayoutLMvaForTokenClassification(config=lowerCamelCase_		)
						a					     =   model(
						    lowerCamelCase_		,       bbox=lowerCamelCase_		,       pixel_values=lowerCamelCase_		,       attention_mask=lowerCamelCase_		,       token_type_ids=lowerCamelCase_		,       labels=lowerCamelCase_		,       training=lowerCamelCase_		,       )
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.text_seq_length, self.num_labels)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   2
						a					     =   TFLayoutLMvaForQuestionAnswering(config=lowerCamelCase_		)
						a					     =   model(
						    lowerCamelCase_		,       bbox=lowerCamelCase_		,       pixel_values=lowerCamelCase_		,       attention_mask=lowerCamelCase_		,       token_type_ids=lowerCamelCase_		,       start_positions=lowerCamelCase_		,       end_positions=lowerCamelCase_		,       training=lowerCamelCase_		,       )
						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		):
						"""simple docstring"""

						a					     =   self.prepare_config_and_inputs()
						((a)     ,				(a)     ,				(a)     ,				(a)     ,				(a)     ,				(a)     ,				(a)     ,				(a))					     =   config_and_inputs
						a					     =   {
						    "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 	_lowercase							(   lowerCAmelCase,      lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							(
	    (
	        TFLayoutLMvaModel,
	        TFLayoutLMvaForQuestionAnswering,
	        TFLayoutLMvaForSequenceClassification,
	        TFLayoutLMvaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	__A							=							(
	    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
	    if is_tf_available()
	    else {}
	)

	__A							=							False
	__A							=							False
	__A							=							False




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						return True




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_=False		):
						"""simple docstring"""

						a					     =   copy.deepcopy(lowerCamelCase_		)

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

						if return_labels:
											if model_class in get_values(lowerCamelCase_		):
																a					     =   tf.ones(self.model_tester.batch_size		,       dtype=tf.intaa		)
											elif model_class in get_values(lowerCamelCase_		):
																a					     =   tf.zeros(self.model_tester.batch_size		,       dtype=tf.intaa		)
																a					     =   tf.zeros(self.model_tester.batch_size		,       dtype=tf.intaa		)
											elif model_class in get_values(lowerCamelCase_		):
																a					     =   tf.zeros(self.model_tester.batch_size		,       dtype=tf.intaa		)
											elif model_class in get_values(lowerCamelCase_		):
																a					     =   tf.zeros(
																    (self.model_tester.batch_size, self.model_tester.text_seq_length)		,       dtype=tf.intaa		)

						return inputs_dict




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   TFLayoutLMvaModelTester(self		)
						a					     =   ConfigTester(self		,       config_class=lowerCamelCase_		,       hidden_size=37		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.config_tester.run_common_tests()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
											a					     =   model_class(lowerCamelCase_		)
											if getattr(lowerCamelCase_		,       "hf_compute_loss"		,       lowerCamelCase_		):
																# The number of elements in the loss should be the same as the number of elements in the label
																a					     =   self._prepare_for_class(inputs_dict.copy()		,       lowerCamelCase_		,       return_labels=lowerCamelCase_		)
																a					     =   prepared_for_class[
																    sorted(prepared_for_class.keys() - inputs_dict.keys()		,       reverse=lowerCamelCase_		)[0]
																]
																a					     =   added_label.shape.as_list()[:1]

																# Test that model correctly compute the loss with kwargs
																a					     =   self._prepare_for_class(inputs_dict.copy()		,       lowerCamelCase_		,       return_labels=lowerCamelCase_		)
																a					     =   prepared_for_class.pop("input_ids"		)

																a					     =   model(lowerCamelCase_		,       **lowerCamelCase_		)[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
																a					     =   self._prepare_for_class(inputs_dict.copy()		,       lowerCamelCase_		,       return_labels=lowerCamelCase_		)
																a					     =   prepared_for_class.pop("input_ids"		)
																if "labels" in prepared_for_class:
																					a					     =   prepared_for_class["labels"].numpy()
																					if len(labels.shape		) > 1 and labels.shape[1] != 1:
																										a					     =   -100
																										a					     =   tf.convert_to_tensor(lowerCamelCase_		)
																										a					     =   model(lowerCamelCase_		,       **lowerCamelCase_		)[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
																a					     =   self._prepare_for_class(inputs_dict.copy()		,       lowerCamelCase_		,       return_labels=lowerCamelCase_		)
																a					     =   model(lowerCamelCase_		)[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
																a					     =   self._prepare_for_class(inputs_dict.copy()		,       lowerCamelCase_		,       return_labels=lowerCamelCase_		)

																# Get keys that were added with the _prepare_for_class function
																a					     =   prepared_for_class.keys() - inputs_dict.keys()
																a					     =   inspect.signature(model.call		).parameters
																a					     =   list(signature.keys()		)

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

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

																for index, value in sorted_tuple_index_mapping:
																					a					     =   prepared_for_class[value]

																a					     =   tuple(lowerCamelCase_		)

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						(
						    (
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				
						)					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						(
						    (
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				
						)					     =   self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
											a					     =   type
											self.model_tester.create_and_check_model(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						(
						    (
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				
						)					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						(
						    (
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				
						)					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						(
						    (
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				(
						    a
						)     ,				
						)					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a					     =   TFLayoutLMvaModel.from_pretrained(lowerCamelCase_		)
											self.assertIsNotNone(lowerCamelCase_		)






def     a(      )		->       Union[str, Any]:
					"""simple docstring"""





					a					     =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
					return image







@require_tf
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	@cached_property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase_		) if is_vision_available() else None




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		)

						a					     =   self.default_image_processor
						a					     =   prepare_img()
						a					     =   image_processor(images=lowerCamelCase_		,       return_tensors="tf"		).pixel_values

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

						# forward pass
						a					     =   model(input_ids=lowerCamelCase_		,       bbox=lowerCamelCase_		,       pixel_values=lowerCamelCase_		,       training=lowerCamelCase_		)

						# verify the logits
						a					     =   (1, 199, 768)
						self.assertEqual(outputs.last_hidden_state.shape		,       lowerCamelCase_		)

						a					     =   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]		,       lowerCamelCase_		,       atol=1E-4		)		)





def     a(      A       :    list       )		->       list:
					"""simple docstring"""





					if any(not isinstance(A ,     A       ) or x < 0 for x in sequence       ):
										raise TypeError("Sequence must be list of non-negative integers"       )
					for _ in range(len(A       )       ):
										for i, (rod_upper, rod_lower) in enumerate(zip(A ,     sequence[1:]       )       ):
															if rod_upper > rod_lower:
																				sequence[i] -= rod_upper - rod_lower
																				sequence[i + 1] += rod_upper - rod_lower
					return sequence


if __name__ == "__main__":
				assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
				assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
