



'''simple docstring'''

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

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


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)




'''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				)				)