


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    UpperCAmelCase					:




			def __init__(						self							:		Dict       ,       __magic_name__							:		Dict       ,       __magic_name__							:		List[Any]=1_3       ,       __magic_name__							:		Optional[Any]=7       ,       __magic_name__							:		Union[str, Any]=True       ,       __magic_name__							:		int=True       ,       __magic_name__							:		Any=True       ,       __magic_name__							:		Union[str, Any]=True       ,       __magic_name__							:		str=9_9       ,       __magic_name__							:		Any=3_2       ,       __magic_name__							:		Dict=2       ,       __magic_name__							:		List[Any]=4       ,       __magic_name__							:		List[str]=3_7       ,       __magic_name__							:		str="gelu"       ,       __magic_name__							:		int=0.1       ,       __magic_name__							:		List[str]=0.1       ,       __magic_name__							:		str=5_1_2       ,       __magic_name__							:		Union[str, Any]=1_6       ,       __magic_name__							:		Optional[int]=2       ,       __magic_name__							:		Dict=0.02       ,       __magic_name__							:		Any=3       ,       __magic_name__							:		Dict=4       ,       __magic_name__							:		Any=None       ,       __magic_name__							:		Optional[int]=1_0_0_0       ,       ):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







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

						# convert bbox to numpy since TF does not support item assignment
						UpperCamelCase         =					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]:
															UpperCamelCase         =					bbox[i, j, 3]
															UpperCamelCase         =					bbox[i, j, 1]
															UpperCamelCase         =					t
												if bbox[i, j, 2] < bbox[i, j, 0]:
															UpperCamelCase         =					bbox[i, j, 2]
															UpperCamelCase         =					bbox[i, j, 0]
															UpperCamelCase         =					t
						UpperCamelCase         =					tf.convert_to_tensor(__magic_name__						)

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

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

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

						UpperCamelCase         =					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   lowerCamelCase_					(						self							:		str       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Any       ,       __magic_name__							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					TFLayoutLMModel(config=__magic_name__						)

						UpperCamelCase         =					model(__magic_name__       ,       __magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__						)
						UpperCamelCase         =					model(__magic_name__       ,       __magic_name__       ,       token_type_ids=__magic_name__						)
						UpperCamelCase         =					model(__magic_name__       ,       __magic_name__						)
						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   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		int       ,       __magic_name__							:		int       ,       __magic_name__							:		Any       ,       __magic_name__							:		Any       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					TFLayoutLMForMaskedLM(config=__magic_name__						)

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




			def   lowerCamelCase_					(						self							:		List[Any]       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		int       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		int       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.num_labels
						UpperCamelCase         =					TFLayoutLMForSequenceClassification(config=__magic_name__						)

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




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		Dict       ,       __magic_name__							:		Any       ,       __magic_name__							:		Dict       ,       __magic_name__							:		Dict       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		int       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.num_labels
						UpperCamelCase         =					TFLayoutLMForTokenClassification(config=__magic_name__						)

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




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Dict       ,       __magic_name__							:		Any       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					TFLayoutLMForQuestionAnswering(config=__magic_name__						)

						UpperCamelCase         =					model(__magic_name__       ,       __magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__						)
						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							:		Dict						):







						"""simple docstring"""







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




@require_tf
class    UpperCAmelCase					(       __snake_case      , __snake_case      , unittest.TestCase    ):
			lowercase						= (
			    (
			        TFLayoutLMModel,
			        TFLayoutLMForMaskedLM,
			        TFLayoutLMForTokenClassification,
			        TFLayoutLMForSequenceClassification,
			        TFLayoutLMForQuestionAnswering,
			    )
			    if is_tf_available()
			    else ()
			)
			lowercase						= (
			    {
			        """feature-extraction""": TFLayoutLMModel,
			        """fill-mask""": TFLayoutLMForMaskedLM,
			        """text-classification""": TFLayoutLMForSequenceClassification,
			        """token-classification""": TFLayoutLMForTokenClassification,
			        """zero-shot""": TFLayoutLMForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			lowercase						= False
			lowercase						= True
			lowercase						= 1_0




			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					TFLayoutLMModelTester(self						)
						UpperCamelCase         =					ConfigTester(self       ,       config_class=__magic_name__       ,       hidden_size=3_7						)




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						self.config_tester.run_common_tests()




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__magic_name__						)




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__magic_name__						)




			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__magic_name__						)




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__magic_name__						)




			@slow
			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







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





			@unittest.skip("""Onnx compliancy broke with TF 2.10"""						)
			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						pass






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]	)  # noqa: E231
			UpperCamelCase         =					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
			UpperCamelCase         =					tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1_000,1_000,1_000,1_000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1_000,1_000,1_000,1_000]]]	)  # noqa: E231
			UpperCamelCase         =					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)
			UpperCamelCase         =					tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]	)  # noqa: E231
			# fmt: on

			return input_ids, attention_mask, bbox, token_type_ids, labels




@require_tf
class    UpperCAmelCase					(       unittest.TestCase    ):




			@slow
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







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

						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					prepare_layoutlm_batch_inputs()

						# forward pass
						UpperCamelCase         =					model(input_ids=__magic_name__       ,       bbox=__magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__						)

						# test the sequence output on [0, :3, :3]
						UpperCamelCase         =					tf.convert_to_tensor(
						    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]       ,       )

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

						# test the pooled output on [1, :3]
						UpperCamelCase         =					tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]						)

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




			@slow
			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







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

						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					prepare_layoutlm_batch_inputs()

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

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

						# test the shape of the logits
						UpperCamelCase         =					outputs.logits
						UpperCamelCase         =					(2, 2)
						self.assertEqual(logits.shape       ,       __magic_name__						)




			@slow
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







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

						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					prepare_layoutlm_batch_inputs()

						# forward pass
						UpperCamelCase         =					model(
						    input_ids=__magic_name__       ,       bbox=__magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__       ,       labels=__magic_name__						)

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





			@slow
			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







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

						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					prepare_layoutlm_batch_inputs()

						# forward pass
						UpperCamelCase         =					model(input_ids=__magic_name__       ,       bbox=__magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__						)

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








from ..utils import DummyObject, requires_backends




class    UpperCAmelCase					(       metaclass=__snake_case    ):
			lowercase						= ["""keras_nlp"""]




			def __init__(						self							:		List[str]       ,       *__magic_name__							:		str       ,       **__magic_name__							:		int						):







						"""simple docstring"""







						requires_backends(self       ,       ["""keras_nlp"""]						)




