'''simple docstring'''







from __future__ import annotations

from scipy.special import comb  # type: ignore




class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       Union[str, Any]							,							UpperCamelCase_ :       list[tuple[float, float]]      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = list_of_points
													# Degree determines the flexibility of the curve.
													# Degree = 1 will produce a straight line.
													SCREAMING_SNAKE_CASE__     :str						    = len(UpperCamelCase_      ) - 1




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       float      )     ->				list[float]:
													assert 0 <= t <= 1, "Time t must be between 0 and 1."
													SCREAMING_SNAKE_CASE__     :list[float]						    = []
													for i in range(len(self.list_of_points      )      ):
																				# basis function for each i
																				output_values.append(
																				    comb(self.degree							,							UpperCamelCase_      ) * ((1 - t) ** (self.degree - i)) * (t**i)      )
													# the basis must sum up to 1 for it to produce a valid Bezier curve.
													assert round(sum(UpperCamelCase_      )							,							5      ) == 1
													return output_values




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       float      )     ->				tuple[float, float]:
													assert 0 <= t <= 1, "Time t must be between 0 and 1."

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.basis_function(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 0.0
													SCREAMING_SNAKE_CASE__     :Any						    = 0.0
													for i in range(len(self.list_of_points      )      ):
																				# For all points, sum up the product of i-th basis function and i-th point.
																				x += basis_function[i] * self.list_of_points[i][0]
																				y += basis_function[i] * self.list_of_points[i][1]
													return (x, y)




						def      __lowerCamelCase       (    self :       Tuple							,							UpperCamelCase_ :       float = 0.01      )     ->				str:
													from matplotlib import pyplot as plt  # type: ignore

													SCREAMING_SNAKE_CASE__     :list[float]						    = []  # x coordinates of points to plot
													SCREAMING_SNAKE_CASE__     :list[float]						    = []  # y coordinates of points to plot

													SCREAMING_SNAKE_CASE__     :Tuple						    = 0.0
													while t <= 1:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = self.bezier_curve_function(UpperCamelCase_      )
																				to_plot_x.append(value[0]      )
																				to_plot_y.append(value[1]      )
																				t += step_size

													SCREAMING_SNAKE_CASE__     :Any						    = [i[0] for i in self.list_of_points]
													SCREAMING_SNAKE_CASE__     :List[str]						    = [i[1] for i in self.list_of_points]

													plt.plot(
													    UpperCamelCase_							,							UpperCamelCase_							,							color='blue'							,							label='Curve of Degree ' + str(self.degree      )							,							)
													plt.scatter(UpperCamelCase_							,							UpperCamelCase_							,							color='red'							,							label='Control Points'      )
													plt.legend()
													plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
		BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
		BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3







'''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 _SCREAMING_SNAKE_CASE:




						def __init__(    self :       Optional[Any]							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Tuple=2							,							UpperCamelCase_ :       List[Any]=3							,							UpperCamelCase_ :       Union[str, Any]=4							,							UpperCamelCase_ :       int=2							,							UpperCamelCase_ :       List[str]=7							,							UpperCamelCase_ :       List[str]=True							,							UpperCamelCase_ :       List[Any]=True							,							UpperCamelCase_ :       List[str]=True							,							UpperCamelCase_ :       int=True							,							UpperCamelCase_ :       List[str]=99							,							UpperCamelCase_ :       Dict=36							,							UpperCamelCase_ :       int=2							,							UpperCamelCase_ :       Union[str, Any]=4							,							UpperCamelCase_ :       str=37							,							UpperCamelCase_ :       List[Any]="gelu"							,							UpperCamelCase_ :       Optional[int]=0.1							,							UpperCamelCase_ :       Optional[int]=0.1							,							UpperCamelCase_ :       int=5_12							,							UpperCamelCase_ :       List[Any]=16							,							UpperCamelCase_ :       Optional[Any]=2							,							UpperCamelCase_ :       Union[str, Any]=0.02							,							UpperCamelCase_ :       str=6							,							UpperCamelCase_ :       int=6							,							UpperCamelCase_ :       List[str]=3							,							UpperCamelCase_ :       Optional[Any]=4							,							UpperCamelCase_ :       Any=None							,							UpperCamelCase_ :       Union[str, Any]=10_00							,							)     ->				int:
													SCREAMING_SNAKE_CASE__     :int						    = parent
													SCREAMING_SNAKE_CASE__     :str						    = batch_size
													SCREAMING_SNAKE_CASE__     :Dict						    = num_channels
													SCREAMING_SNAKE_CASE__     :Any						    = image_size
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = patch_size
													SCREAMING_SNAKE_CASE__     :List[Any]						    = is_training
													SCREAMING_SNAKE_CASE__     :Tuple						    = use_input_mask
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = use_token_type_ids
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = use_labels
													SCREAMING_SNAKE_CASE__     :Tuple						    = vocab_size
													SCREAMING_SNAKE_CASE__     :List[Any]						    = hidden_size
													SCREAMING_SNAKE_CASE__     :int						    = num_hidden_layers
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = num_attention_heads
													SCREAMING_SNAKE_CASE__     :Any						    = intermediate_size
													SCREAMING_SNAKE_CASE__     :Tuple						    = hidden_act
													SCREAMING_SNAKE_CASE__     :Any						    = hidden_dropout_prob
													SCREAMING_SNAKE_CASE__     :Any						    = attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__     :List[str]						    = max_position_embeddings
													SCREAMING_SNAKE_CASE__     :Tuple						    = type_vocab_size
													SCREAMING_SNAKE_CASE__     :List[str]						    = type_sequence_label_size
													SCREAMING_SNAKE_CASE__     :Any						    = initializer_range
													SCREAMING_SNAKE_CASE__     :List[Any]						    = coordinate_size
													SCREAMING_SNAKE_CASE__     :List[Any]						    = shape_size
													SCREAMING_SNAKE_CASE__     :str						    = num_labels
													SCREAMING_SNAKE_CASE__     :Any						    = num_choices
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = scope
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = range_bbox

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




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = ids_tensor([self.batch_size, self.text_seq_length]							,							self.vocab_size      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = ids_tensor([self.batch_size, self.text_seq_length, 4]							,							self.range_bbox      )
													SCREAMING_SNAKE_CASE__     :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]:
																																		SCREAMING_SNAKE_CASE__     :str						    = bbox[i, j, 3]
																																		SCREAMING_SNAKE_CASE__     :str						    = bbox[i, j, 1]
																																		SCREAMING_SNAKE_CASE__     :Dict						    = tmp_coordinate
																											if bbox[i, j, 2] < bbox[i, j, 0]:
																																		SCREAMING_SNAKE_CASE__     :Optional[int]						    = bbox[i, j, 2]
																																		SCREAMING_SNAKE_CASE__     :Dict						    = bbox[i, j, 0]
																																		SCREAMING_SNAKE_CASE__     :Any						    = tmp_coordinate
													SCREAMING_SNAKE_CASE__     :Tuple						    = tf.constant(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = None
													if self.use_input_mask:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = random_attention_mask([self.batch_size, self.text_seq_length]      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = None
													if self.use_token_type_ids:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = ids_tensor([self.batch_size, self.text_seq_length]							,							self.type_vocab_size      )

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

													SCREAMING_SNAKE_CASE__     :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      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       str      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Dict						    = TFLayoutLMvaModel(config=UpperCamelCase_      )

													# text + image
													SCREAMING_SNAKE_CASE__     :int						    = model(UpperCamelCase_							,							pixel_values=UpperCamelCase_							,							training=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = model(
													    UpperCamelCase_							,							bbox=UpperCamelCase_							,							pixel_values=UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							token_type_ids=UpperCamelCase_							,							training=UpperCamelCase_							,							)
													SCREAMING_SNAKE_CASE__     :str						    = model(UpperCamelCase_							,							bbox=UpperCamelCase_							,							pixel_values=UpperCamelCase_							,							training=UpperCamelCase_      )

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

													# text only
													SCREAMING_SNAKE_CASE__     :List[Any]						    = model(UpperCamelCase_							,							training=UpperCamelCase_      )
													self.parent.assertEqual(
													    result.last_hidden_state.shape							,							(self.batch_size, self.text_seq_length, self.hidden_size)      )

													# image only
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = model({'pixel_values': pixel_values}							,							training=UpperCamelCase_      )
													self.parent.assertEqual(
													    result.last_hidden_state.shape							,							(self.batch_size, self.image_seq_length, self.hidden_size)      )




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       Union[str, Any]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Any						    = self.num_labels
													SCREAMING_SNAKE_CASE__     :Any						    = TFLayoutLMvaForSequenceClassification(config=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = model(
													    UpperCamelCase_							,							bbox=UpperCamelCase_							,							pixel_values=UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							token_type_ids=UpperCamelCase_							,							labels=UpperCamelCase_							,							training=UpperCamelCase_							,							)
													self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)      )




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




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       Optional[Any]      )     ->				int:
													SCREAMING_SNAKE_CASE__     :int						    = 2
													SCREAMING_SNAKE_CASE__     :str						    = TFLayoutLMvaForQuestionAnswering(config=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = model(
													    UpperCamelCase_							,							bbox=UpperCamelCase_							,							pixel_values=UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							token_type_ids=UpperCamelCase_							,							start_positions=UpperCamelCase_							,							end_positions=UpperCamelCase_							,							training=UpperCamelCase_							,							)
													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      __lowerCamelCase       (    self :       Optional[Any]      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.prepare_config_and_inputs()
													((SCREAMING_SNAKE_CASE__)    ,      (SCREAMING_SNAKE_CASE__)    ,      (SCREAMING_SNAKE_CASE__)    ,      (SCREAMING_SNAKE_CASE__)    ,      (SCREAMING_SNAKE_CASE__)    ,      (SCREAMING_SNAKE_CASE__)    ,      (SCREAMING_SNAKE_CASE__)    ,      (SCREAMING_SNAKE_CASE__))     :List[str]						    = config_and_inputs
													SCREAMING_SNAKE_CASE__     :Tuple						    = {
													    '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 _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							List[str]     		= (
						    (
						        TFLayoutLMvaModel,
						        TFLayoutLMvaForQuestionAnswering,
						        TFLayoutLMvaForSequenceClassification,
						        TFLayoutLMvaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						A_			:							str     		= (
						    {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
						    if is_tf_available()
						    else {}
						)

						A_			:							Tuple     		= False
						A_			:							Tuple     		= False
						A_			:							Tuple     		= False




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       List[str]      )     ->				Optional[Any]:
													return True




						def      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       Any=False      )     ->				dict:
													SCREAMING_SNAKE_CASE__     :Dict						    = copy.deepcopy(UpperCamelCase_      )

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

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

													return inputs_dict




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = TFLayoutLMvaModelTester(self      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = ConfigTester(self							,							config_class=UpperCamelCase_							,							hidden_size=37      )




						def      __lowerCamelCase       (    self :       str      )     ->				Dict:
													self.config_tester.run_common_tests()




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

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

																											SCREAMING_SNAKE_CASE__     :Optional[int]						    = model(UpperCamelCase_							,							**UpperCamelCase_      )[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
																											SCREAMING_SNAKE_CASE__     :Tuple						    = self._prepare_for_class(inputs_dict.copy()							,							UpperCamelCase_							,							return_labels=UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :str						    = prepared_for_class.pop('input_ids'      )
																											if "labels" in prepared_for_class:
																																		SCREAMING_SNAKE_CASE__     :Optional[int]						    = prepared_for_class['labels'].numpy()
																																		if len(labels.shape      ) > 1 and labels.shape[1] != 1:
																																									SCREAMING_SNAKE_CASE__     :str						    = -1_00
																																									SCREAMING_SNAKE_CASE__     :int						    = tf.convert_to_tensor(UpperCamelCase_      )
																																									SCREAMING_SNAKE_CASE__     :Tuple						    = model(UpperCamelCase_							,							**UpperCamelCase_      )[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
																											SCREAMING_SNAKE_CASE__     :Optional[int]						    = self._prepare_for_class(inputs_dict.copy()							,							UpperCamelCase_							,							return_labels=UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :Any						    = model(UpperCamelCase_      )[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
																											SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self._prepare_for_class(inputs_dict.copy()							,							UpperCamelCase_							,							return_labels=UpperCamelCase_      )

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

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

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

																											for index, value in sorted_tuple_index_mapping:
																																		SCREAMING_SNAKE_CASE__     :List[str]						    = prepared_for_class[value]

																											SCREAMING_SNAKE_CASE__     :List[str]						    = tuple(UpperCamelCase_      )

																											# Send to model
																											SCREAMING_SNAKE_CASE__     :List[str]						    = model(tuple_input[:-1]      )[0]

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




						def      __lowerCamelCase       (    self :       Tuple      )     ->				str:
													(
													    (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      
													)     :Optional[int]						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[int]:
													(
													    (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      
													)     :List[Any]						    = self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = type
																				self.model_tester.create_and_check_model(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Dict      )     ->				Optional[Any]:
													(
													    (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      
													)     :int						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(
													    UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Dict      )     ->				Any:
													(
													    (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      
													)     :List[str]						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(
													    UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Tuple      )     ->				str:
													(
													    (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      
													)     :Dict						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(
													    UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      )




						@slow
						def      __lowerCamelCase       (    self :       Any      )     ->				Any:
													for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE__     :int						    = TFLayoutLMvaModel.from_pretrained(UpperCamelCase_      )
																				self.assertIsNotNone(UpperCamelCase_      )


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


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
							return image





@require_tf
class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						@cached_property
						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				List[str]:
													return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase_      ) if is_vision_available() else None




						@slow
						def      __lowerCamelCase       (    self :       List[Any]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'      )

													SCREAMING_SNAKE_CASE__     :str						    = self.default_image_processor
													SCREAMING_SNAKE_CASE__     :int						    = prepare_img()
													SCREAMING_SNAKE_CASE__     :str						    = image_processor(images=UpperCamelCase_							,							return_tensors='tf'      ).pixel_values

													SCREAMING_SNAKE_CASE__     :Tuple						    = tf.constant([[1, 2]]      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]      )							,							axis=0      )

													# forward pass
													SCREAMING_SNAKE_CASE__     :Tuple						    = model(input_ids=UpperCamelCase_							,							bbox=UpperCamelCase_							,							pixel_values=UpperCamelCase_							,							training=UpperCamelCase_      )

													# verify the logits
													SCREAMING_SNAKE_CASE__     :List[Any]						    = (1, 1_99, 7_68)
													self.assertEqual(outputs.last_hidden_state.shape							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :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]							,							UpperCamelCase_							,							atol=1e-4      )      )






