



'''simple docstring'''

import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase     		=   {'LayoutLMv2Config', 'LayoutLMv3Config'}

@is_pipeline_test
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
						UpperCAmelCase							:   List[Any]									=		TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

						if model_mapping is not None:
												UpperCAmelCase							:   int									=		{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
						if tf_model_mapping is not None:
												UpperCAmelCase							:   Tuple									=		{
												    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
												}


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     str:
							lowerCAmelCase   =     ZeroShotClassificationPipeline(
							    model=A_     ,	tokenizer=A_     ,	candidate_labels=["""polics""", """health"""]				)
							return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     Tuple:
							lowerCAmelCase   =     classifier("""Who are you voting for in 2020?"""     ,	candidate_labels="""politics"""				)
							self.assertEqual(A_     ,	{"""sequence""": ANY(A_				), """labels""": [ANY(A_				)], """scores""": [ANY(A_				)]}				)

							# No kwarg
							lowerCAmelCase   =     classifier("""Who are you voting for in 2020?"""     ,	["""politics"""]				)
							self.assertEqual(A_     ,	{"""sequence""": ANY(A_				), """labels""": [ANY(A_				)], """scores""": [ANY(A_				)]}				)

							lowerCAmelCase   =     classifier("""Who are you voting for in 2020?"""     ,	candidate_labels=["""politics"""]				)
							self.assertEqual(A_     ,	{"""sequence""": ANY(A_				), """labels""": [ANY(A_				)], """scores""": [ANY(A_				)]}				)

							lowerCAmelCase   =     classifier("""Who are you voting for in 2020?"""     ,	candidate_labels="""politics, public health"""				)
							self.assertEqual(
							    A_     ,	{"""sequence""": ANY(A_				), """labels""": [ANY(A_				), ANY(A_				)], """scores""": [ANY(A_				), ANY(A_				)]}				)
							self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]				)				)     ,	1.0				)

							lowerCAmelCase   =     classifier("""Who are you voting for in 2020?"""     ,	candidate_labels=["""politics""", """public health"""]				)
							self.assertEqual(
							    A_     ,	{"""sequence""": ANY(A_				), """labels""": [ANY(A_				), ANY(A_				)], """scores""": [ANY(A_				), ANY(A_				)]}				)
							self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]				)				)     ,	1.0				)

							lowerCAmelCase   =     classifier(
							    """Who are you voting for in 2020?"""     ,	candidate_labels="""politics"""     ,	hypothesis_template="""This text is about {}"""				)
							self.assertEqual(A_     ,	{"""sequence""": ANY(A_				), """labels""": [ANY(A_				)], """scores""": [ANY(A_				)]}				)

							# https://github.com/huggingface/transformers/issues/13846
							lowerCAmelCase   =     classifier(["""I am happy"""]     ,	["""positive""", """negative"""]				)
							self.assertEqual(
							    A_     ,	[
							        {"""sequence""": ANY(A_				), """labels""": [ANY(A_				), ANY(A_				)], """scores""": [ANY(A_				), ANY(A_				)]}
							        for i in range(1				)
							    ]     ,	)
							lowerCAmelCase   =     classifier(["""I am happy""", """I am sad"""]     ,	["""positive""", """negative"""]				)
							self.assertEqual(
							    A_     ,	[
							        {"""sequence""": ANY(A_				), """labels""": [ANY(A_				), ANY(A_				)], """scores""": [ANY(A_				), ANY(A_				)]}
							        for i in range(2				)
							    ]     ,	)

							with self.assertRaises(A_				):
								classifier(""""""     ,	candidate_labels="""politics"""				)

							with self.assertRaises(A_				):
								classifier(A_     ,	candidate_labels="""politics"""				)

							with self.assertRaises(A_				):
								classifier("""Who are you voting for in 2020?"""     ,	candidate_labels=""""""				)

							with self.assertRaises(A_				):
								classifier("""Who are you voting for in 2020?"""     ,	candidate_labels=A_				)

							with self.assertRaises(A_				):
								classifier(
								    """Who are you voting for in 2020?"""     ,	candidate_labels="""politics"""     ,	hypothesis_template="""Not formatting template"""     ,	)

							with self.assertRaises(A_				):
								classifier(
								    """Who are you voting for in 2020?"""     ,	candidate_labels="""politics"""     ,	hypothesis_template=A_     ,	)

							self.run_entailment_id(A_				)


						def     __snake_case   ( self     ,	A_				)     ->     Optional[Any]:
							lowerCAmelCase   =     zero_shot_classifier.model.config
							lowerCAmelCase   =     config.labelaid
							lowerCAmelCase   =     zero_shot_classifier.entailment_id

							lowerCAmelCase   =     {"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
							self.assertEqual(zero_shot_classifier.entailment_id     ,	-1				)

							lowerCAmelCase   =     {"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
							self.assertEqual(zero_shot_classifier.entailment_id     ,	0				)

							lowerCAmelCase   =     {"""ENTAIL""": 0, """NON-ENTAIL""": 1}
							self.assertEqual(zero_shot_classifier.entailment_id     ,	0				)

							lowerCAmelCase   =     {"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
							self.assertEqual(zero_shot_classifier.entailment_id     ,	2				)

							lowerCAmelCase   =     original_labelaid
							self.assertEqual(A_     ,	zero_shot_classifier.entailment_id				)


						@require_torch
						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     pipeline(
							    """zero-shot-classification"""     ,	model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,	framework="""pt"""     ,	)
							# There was a regression in 4.10 for this
							# Adding a test so we don't make the mistake again.
							# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
							zero_shot_classifier(
							    """Who are you voting for in 2020?""" * 100     ,	candidate_labels=["""politics""", """public health""", """science"""]				)


						@require_torch
						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     pipeline(
							    """zero-shot-classification"""     ,	model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,	framework="""pt"""     ,	)
							lowerCAmelCase   =     zero_shot_classifier(
							    """Who are you voting for in 2020?"""     ,	candidate_labels=["""politics""", """public health""", """science"""]				)

							self.assertEqual(
							    nested_simplify(A_				)     ,	{
							        """sequence""": """Who are you voting for in 2020?""",
							        """labels""": ["""science""", """public health""", """politics"""],
							        """scores""": [0.3_3_3, 0.3_3_3, 0.3_3_3],
							    }     ,	)


						@require_tf
						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     pipeline(
							    """zero-shot-classification"""     ,	model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,	framework="""tf"""     ,	)
							lowerCAmelCase   =     zero_shot_classifier(
							    """Who are you voting for in 2020?"""     ,	candidate_labels=["""politics""", """public health""", """science"""]				)

							self.assertEqual(
							    nested_simplify(A_				)     ,	{
							        """sequence""": """Who are you voting for in 2020?""",
							        """labels""": ["""science""", """public health""", """politics"""],
							        """scores""": [0.3_3_3, 0.3_3_3, 0.3_3_3],
							    }     ,	)


						@slow
						@require_torch
						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     pipeline("""zero-shot-classification"""     ,	model="""roberta-large-mnli"""     ,	framework="""pt"""				)
							lowerCAmelCase   =     zero_shot_classifier(
							    """Who are you voting for in 2020?"""     ,	candidate_labels=["""politics""", """public health""", """science"""]				)

							self.assertEqual(
							    nested_simplify(A_				)     ,	{
							        """sequence""": """Who are you voting for in 2020?""",
							        """labels""": ["""politics""", """public health""", """science"""],
							        """scores""": [0.9_7_6, 0.0_1_5, 0.0_0_9],
							    }     ,	)
							lowerCAmelCase   =     zero_shot_classifier(
							    """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
							    """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
							    """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
							    """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
							    """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
							    """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
							    """ English-to-German translation task, improving over the existing best results, including ensembles by"""
							    """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
							    """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
							    """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
							    """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
							    """ large and limited training data."""     ,	candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,	multi_label=A_     ,	)
							self.assertEqual(
							    nested_simplify(A_				)     ,	{
							        """sequence""": (
							            """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
							            """ networks in an encoder-decoder configuration. The best performing models also connect the"""
							            """ encoder and decoder through an attention mechanism. We propose a new simple network"""
							            """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
							            """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
							            """ superior in quality while being more parallelizable and requiring significantly less time to"""
							            """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
							            """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
							            """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
							            """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
							            """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
							            """ other tasks by applying it successfully to English constituency parsing both with large and"""
							            """ limited training data."""
							        ),
							        """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
							        """scores""": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
							    }     ,	)




						@slow
						@require_tf
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     pipeline("""zero-shot-classification"""     ,	model="""roberta-large-mnli"""     ,	framework="""tf"""				)
							lowerCAmelCase   =     zero_shot_classifier(
							    """Who are you voting for in 2020?"""     ,	candidate_labels=["""politics""", """public health""", """science"""]				)

							self.assertEqual(
							    nested_simplify(A_				)     ,	{
							        """sequence""": """Who are you voting for in 2020?""",
							        """labels""": ["""politics""", """public health""", """science"""],
							        """scores""": [0.9_7_6, 0.0_1_5, 0.0_0_9],
							    }     ,	)
							lowerCAmelCase   =     zero_shot_classifier(
							    """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
							    """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
							    """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
							    """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
							    """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
							    """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
							    """ English-to-German translation task, improving over the existing best results, including ensembles by"""
							    """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
							    """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
							    """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
							    """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
							    """ large and limited training data."""     ,	candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,	multi_label=A_     ,	)
							self.assertEqual(
							    nested_simplify(A_				)     ,	{
							        """sequence""": (
							            """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
							            """ networks in an encoder-decoder configuration. The best performing models also connect the"""
							            """ encoder and decoder through an attention mechanism. We propose a new simple network"""
							            """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
							            """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
							            """ superior in quality while being more parallelizable and requiring significantly less time to"""
							            """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
							            """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
							            """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
							            """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
							            """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
							            """ other tasks by applying it successfully to English constituency parsing both with large and"""
							            """ limited training data."""
							        ),
							        """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
							        """scores""": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
							    }     ,	)




'''simple docstring'''

import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    LiltForQuestionAnswering,
				    LiltForSequenceClassification,
				    LiltForTokenClassification,
				    LiltModel,
				)
				from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=13     ,	A_=7     ,	A_=True     ,	A_=True     ,	A_=True     ,	A_=True     ,	A_=99     ,	A_=24     ,	A_=2     ,	A_=6     ,	A_=37     ,	A_="gelu"     ,	A_=0.1     ,	A_=0.1     ,	A_=512     ,	A_=16     ,	A_=2     ,	A_=0.0_2     ,	A_=3     ,	A_=None     ,	A_=1000     ,	)     ->     Union[str, Any]:
							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   =     scope
							lowerCAmelCase   =     range_bbox


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size				)

							lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length, 4]     ,	self.range_bbox				)
							# 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   =     None
							if self.use_input_mask:
								lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	vocab_size=2				)

							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
							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   =     self.get_config()

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


						def     __snake_case   ( self				)     ->     int:
							return LiltConfig(
							    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     ,	)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	)     ->     int:
							lowerCAmelCase   =     LiltModel(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_     ,	bbox=A_     ,	attention_mask=A_     ,	token_type_ids=A_				)
							lowerCAmelCase   =     model(A_     ,	bbox=A_     ,	token_type_ids=A_				)
							lowerCAmelCase   =     model(A_     ,	bbox=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     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     LiltForTokenClassification(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(
							    A_     ,	bbox=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     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	)     ->     Optional[Any]:
							lowerCAmelCase   =     LiltForQuestionAnswering(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(
							    A_     ,	bbox=A_     ,	attention_mask=A_     ,	token_type_ids=A_     ,	start_positions=A_     ,	end_positions=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     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.prepare_config_and_inputs()
							(
							    (
							    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_torch
class __snake_case(				_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   int									=		(
						    (
						        LiltModel,
						        LiltForSequenceClassification,
						        LiltForTokenClassification,
						        LiltForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase							:   Union[str, Any]									=		(
						    {
						        "feature-extraction": LiltModel,
						        "question-answering": LiltForQuestionAnswering,
						        "text-classification": LiltForSequenceClassification,
						        "token-classification": LiltForTokenClassification,
						        "zero-shot": LiltForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase							:   Optional[int]									=		False
						UpperCAmelCase							:   Dict									=		False


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Union[str, Any]:
							return True


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     LiltModelTester(self				)
							lowerCAmelCase   =     ConfigTester(self     ,	config_class=A_     ,	hidden_size=37				)


						def     __snake_case   ( self				)     ->     Any:
							self.config_tester.run_common_tests()


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_				)


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								lowerCAmelCase   =     type
								self.model_tester.create_and_check_model(*A_				)


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A_				)


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*A_				)




						@slow
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCAmelCase   =     LiltModel.from_pretrained(A_				)
								self.assertIsNotNone(A_				)







@require_torch
@slow
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""				).to(A_				)

							lowerCAmelCase   =     torch.tensor([[1, 2]]     ,	device=A_				)
							lowerCAmelCase   =     torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]     ,	device=A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(input_ids=A_     ,	bbox=A_				)

							lowerCAmelCase   =     torch.Size([1, 2, 768]				)
							lowerCAmelCase   =     torch.tensor(
							    [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]     ,	device=A_     ,	)

							self.assertTrue(outputs.last_hidden_state.shape     ,	A_				)
							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]     ,	A_     ,	atol=1e-3				)				)