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 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=13						,      lowerCAmelCase=7						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=99						,      lowerCAmelCase=24						,      lowerCAmelCase=2						,      lowerCAmelCase=6						,      lowerCAmelCase=37						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.1						,      lowerCAmelCase=5_12						,      lowerCAmelCase=16						,      lowerCAmelCase=2						,      lowerCAmelCase=0.02						,      lowerCAmelCase=3						,      lowerCAmelCase=None						,      lowerCAmelCase=10_00						,      ):






										"""simple docstring"""

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

										snake_case        =     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]:
																												snake_case        =     bbox[i, j, 3]
																												snake_case        =     bbox[i, j, 1]
																												snake_case        =     t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																												snake_case        =     bbox[i, j, 2]
																												snake_case        =     bbox[i, j, 0]
																												snake_case        =     t

										snake_case        =     None
										if self.use_input_mask:
																snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      vocab_size=2      )

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

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

										snake_case        =     self.get_config()

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										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						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     LiltModel(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      bbox=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase      )
										snake_case        =     model(lowerCAmelCase						,      bbox=lowerCAmelCase						,      token_type_ids=lowerCAmelCase      )
										snake_case        =     model(lowerCAmelCase						,      bbox=lowerCAmelCase      )

										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						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     LiltForTokenClassification(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(
										    lowerCAmelCase						,      bbox=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.num_labels)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     LiltForQuestionAnswering(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(
										    lowerCAmelCase						,      bbox=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      start_positions=lowerCAmelCase						,      end_positions=lowerCAmelCase						,      )
										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      ):






										"""simple docstring"""

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





@require_torch
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      lowerCAmelCase       ,      unittest.TestCase   ):



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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										return True

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     LiltModelTester(self      )
										snake_case        =     ConfigTester(self						,      config_class=lowerCAmelCase						,      hidden_size=37      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																snake_case        =     type
																self.model_tester.create_and_check_model(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case        =     LiltModel.from_pretrained(lowerCAmelCase      )
																self.assertIsNotNone(lowerCAmelCase      )





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



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'      ).to(lowerCAmelCase      )

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

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

										snake_case        =     torch.Size([1, 2, 7_68]      )
										snake_case        =     torch.tensor(
										    [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]						,      device=lowerCAmelCase						,      )

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



"""simple docstring"""
SCREAMING_SNAKE_CASE__   =					{str(digit): digit**5 for digit in range(10)}





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int   )   ->			int:





						"""simple docstring"""






						return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_UpperCamelCase   )   )





def 				lowerCAmelCase__      (     )   ->			int:





						"""simple docstring"""






						return sum(
						    number
						    for number in range(1_0_0_0	,     1_0_0_0_0_0_0   )
						    if number == digits_fifth_powers_sum(_UpperCamelCase   )   )


if __name__ == "__main__":
					print(solution())


