




from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
					import tensorflow as tf

					from transformers.models.layoutlm.modeling_tf_layoutlm import (
					    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFLayoutLMForMaskedLM,
					    TFLayoutLMForQuestionAnswering,
					    TFLayoutLMForSequenceClassification,
					    TFLayoutLMForTokenClassification,
					    TFLayoutLMModel,
					)
class     a__					:






						def __init__(     self	,    _A	,    _A=1_3	,    _A=7	,    _A=True	,    _A=True	,    _A=True	,    _A=True	,    _A=9_9	,    _A=3_2	,    _A=2	,    _A=4	,    _A=3_7	,    _A="gelu"	,    _A=0.1	,    _A=0.1	,    _A=5_1_2	,    _A=1_6	,    _A=2	,    _A=0.02	,    _A=3	,    _A=4	,    _A=None	,    _A=1_0_0_0	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = parent
											__lowerCAmelCase            = batch_size
											__lowerCAmelCase            = seq_length
											__lowerCAmelCase            = is_training
											__lowerCAmelCase            = use_input_mask
											__lowerCAmelCase            = use_token_type_ids
											__lowerCAmelCase            = use_labels
											__lowerCAmelCase            = vocab_size
											__lowerCAmelCase            = hidden_size
											__lowerCAmelCase            = num_hidden_layers
											__lowerCAmelCase            = num_attention_heads
											__lowerCAmelCase            = intermediate_size
											__lowerCAmelCase            = hidden_act
											__lowerCAmelCase            = hidden_dropout_prob
											__lowerCAmelCase            = attention_probs_dropout_prob
											__lowerCAmelCase            = max_position_embeddings
											__lowerCAmelCase            = type_vocab_size
											__lowerCAmelCase            = type_sequence_label_size
											__lowerCAmelCase            = initializer_range
											__lowerCAmelCase            = num_labels
											__lowerCAmelCase            = num_choices
											__lowerCAmelCase            = scope
											__lowerCAmelCase            = range_bbox






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size    )

											# convert bbox to numpy since TF does not support item assignment
											__lowerCAmelCase            = ids_tensor([self.batch_size, self.seq_length, 4]	,    self.range_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]:
																										__lowerCAmelCase            = bbox[i, j, 3]
																										__lowerCAmelCase            = bbox[i, j, 1]
																										__lowerCAmelCase            = t
																					if bbox[i, j, 2] < bbox[i, j, 0]:
																										__lowerCAmelCase            = bbox[i, j, 2]
																										__lowerCAmelCase            = bbox[i, j, 0]
																										__lowerCAmelCase            = t
											__lowerCAmelCase            = tf.convert_to_tensor(_A    )

											__lowerCAmelCase            = None
											if self.use_input_mask:
																__lowerCAmelCase            = random_attention_mask([self.batch_size, self.seq_length]    )

											__lowerCAmelCase            = None
											if self.use_token_type_ids:
																__lowerCAmelCase            = ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size    )

											__lowerCAmelCase            = None
											__lowerCAmelCase            = None
											__lowerCAmelCase            = None
											if self.use_labels:
																__lowerCAmelCase            = ids_tensor([self.batch_size]	,    self.type_sequence_label_size    )
																__lowerCAmelCase            = ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels    )
																__lowerCAmelCase            = ids_tensor([self.batch_size]	,    self.num_choices    )

											__lowerCAmelCase            = LayoutLMConfig(
											    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	,    )

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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFLayoutLMModel(config=_A    )

											__lowerCAmelCase            = model(_A	,    _A	,    attention_mask=_A	,    token_type_ids=_A    )
											__lowerCAmelCase            = model(_A	,    _A	,    token_type_ids=_A    )
											__lowerCAmelCase            = model(_A	,    _A    )
											self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)    )
											self.parent.assertEqual(result.pooler_output.shape	,    (self.batch_size, self.hidden_size)    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFLayoutLMForMaskedLM(config=_A    )

											__lowerCAmelCase            = model(_A	,    _A	,    attention_mask=_A	,    token_type_ids=_A	,    labels=_A    )
											self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.num_labels
											__lowerCAmelCase            = TFLayoutLMForSequenceClassification(config=_A    )

											__lowerCAmelCase            = model(_A	,    _A	,    attention_mask=_A	,    token_type_ids=_A    )
											self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.num_labels
											__lowerCAmelCase            = TFLayoutLMForTokenClassification(config=_A    )

											__lowerCAmelCase            = model(_A	,    _A	,    attention_mask=_A	,    token_type_ids=_A	,    labels=_A    )
											self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.num_labels)    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFLayoutLMForQuestionAnswering(config=_A    )

											__lowerCAmelCase            = model(_A	,    _A	,    attention_mask=_A	,    token_type_ids=_A    )
											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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.prepare_config_and_inputs()
											(
											    (
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							
											)            = config_and_inputs
											__lowerCAmelCase            = {
											    "input_ids": input_ids,
											    "bbox": bbox,
											    "token_type_ids": token_type_ids,
											    "attention_mask": input_mask,
											}
											return config, inputs_dict






@require_tf
class     a__					(      snake_case__				,       snake_case__				,       unittest.TestCase   ):
						_a							:					Dict      =					(
						    (
						        TFLayoutLMModel,
						        TFLayoutLMForMaskedLM,
						        TFLayoutLMForTokenClassification,
						        TFLayoutLMForSequenceClassification,
						        TFLayoutLMForQuestionAnswering,
						    )
						    if is_tf_available()
						    else ()
						)
						_a							:					Dict      =					(
						    {
						        """feature-extraction""": TFLayoutLMModel,
						        """fill-mask""": TFLayoutLMForMaskedLM,
						        """text-classification""": TFLayoutLMForSequenceClassification,
						        """token-classification""": TFLayoutLMForTokenClassification,
						        """zero-shot""": TFLayoutLMForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_a							:					Tuple      =					False
						_a							:					Optional[int]      =					True
						_a							:					str      =					1_0






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFLayoutLMModelTester(self    )
											__lowerCAmelCase            = ConfigTester(self	,    config_class=_A	,    hidden_size=3_7    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*_A    )






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase            = TFLayoutLMModel.from_pretrained(_A    )
																self.assertIsNotNone(_A    )






						@unittest.skip("Onnx compliancy broke with TF 2.10"    )
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											pass







def 			_a   (		):
					# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
					# fmt: off
					__lowerCAmelCase            = tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]  )  # noqa: E231
					__lowerCAmelCase            = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]  )  # noqa: E231
					__lowerCAmelCase            = tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]  )  # noqa: E231
					__lowerCAmelCase            = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]  )  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					__lowerCAmelCase            = tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]  )  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels


@require_tf
class     a__					(      unittest.TestCase   ):






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"    )

											__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase            = prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase            = model(input_ids=_A	,    bbox=_A	,    attention_mask=_A	,    token_type_ids=_A    )

											# test the sequence output on [0, :3, :3]
											__lowerCAmelCase            = tf.convert_to_tensor(
											    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]	,    )

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

											# test the pooled output on [1, :3]
											__lowerCAmelCase            = tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]    )

											self.assertTrue(np.allclose(outputs.pooler_output[1, :3]	,    _A	,    atol=1E-3    )    )






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"	,    num_labels=2    )

											__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase            = prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase            = model(
											    input_ids=_A	,    bbox=_A	,    attention_mask=_A	,    token_type_ids=_A	,    labels=tf.convert_to_tensor([1, 1]    )	,    )

											# test whether we get a loss as a scalar
											__lowerCAmelCase            = outputs.loss
											__lowerCAmelCase            = (2,)
											self.assertEqual(loss.shape	,    _A    )

											# test the shape of the logits
											__lowerCAmelCase            = outputs.logits
											__lowerCAmelCase            = (2, 2)
											self.assertEqual(logits.shape	,    _A    )






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"	,    num_labels=1_3    )

											__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase            = prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase            = model(
											    input_ids=_A	,    bbox=_A	,    attention_mask=_A	,    token_type_ids=_A	,    labels=_A    )

											# test the shape of the logits
											__lowerCAmelCase            = outputs.logits
											__lowerCAmelCase            = tf.convert_to_tensor((2, 2_5, 1_3)    )
											self.assertEqual(logits.shape	,    _A    )






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"    )

											__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase            = prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase            = model(input_ids=_A	,    bbox=_A	,    attention_mask=_A	,    token_type_ids=_A    )

											# test the shape of the logits
											__lowerCAmelCase            = tf.convert_to_tensor((2, 2_5)    )
											self.assertEqual(outputs.start_logits.shape	,    _A    )
											self.assertEqual(outputs.end_logits.shape	,    _A    )










def 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):
					if divisor % 5 == 0 or divisor % 2 == 0:
										return 0
					__lowerCAmelCase            = 1
					__lowerCAmelCase            = 1
					while repunit:
										__lowerCAmelCase            = (10 * repunit + 1) % divisor
										repunit_index += 1
					return repunit_index







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int = 1_00_00_00  ):
					__lowerCAmelCase            = limit - 1
					if divisor % 2 == 0:
										divisor += 1
					while least_divisible_repunit(SCREAMING_SNAKE_CASE_  ) <= limit:
										divisor += 2
					return divisor


if __name__ == "__main__":
					print(f'''{solution() = }''')




