




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       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=13      ,     SCREAMING_SNAKE_CASE=7      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=99      ,     SCREAMING_SNAKE_CASE=24      ,     SCREAMING_SNAKE_CASE=2      ,     SCREAMING_SNAKE_CASE=6      ,     SCREAMING_SNAKE_CASE=37      ,     SCREAMING_SNAKE_CASE="gelu"      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=512      ,     SCREAMING_SNAKE_CASE=16      ,     SCREAMING_SNAKE_CASE=2      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=1000      ,     )  ->					List[Any]:




				"""simple docstring"""



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





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



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

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

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

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

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

				SCREAMING_SNAKE_CASE_								=						self.get_config()

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





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




				"""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 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						LiltModel(config=SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )
				model.eval()
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     bbox=SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ,     token_type_ids=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     bbox=SCREAMING_SNAKE_CASE      ,     token_type_ids=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     bbox=SCREAMING_SNAKE_CASE      )

				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 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     )  ->					Optional[int]:




				"""simple docstring"""



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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						LiltForQuestionAnswering(config=SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )
				model.eval()
				SCREAMING_SNAKE_CASE_								=						model(
				    SCREAMING_SNAKE_CASE      ,     bbox=SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ,     token_type_ids=SCREAMING_SNAKE_CASE      ,     start_positions=SCREAMING_SNAKE_CASE      ,     end_positions=SCREAMING_SNAKE_CASE      ,     )
				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 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.prepare_config_and_inputs()
				(
				    (
				    SCREAMING_SNAKE_CASE_
				)						,		(
				    SCREAMING_SNAKE_CASE_
				)						,		(
				    SCREAMING_SNAKE_CASE_
				)						,		(
				    SCREAMING_SNAKE_CASE_
				)						,		(
				    SCREAMING_SNAKE_CASE_
				)						,		(
				    SCREAMING_SNAKE_CASE_
				)						,		(
				    SCREAMING_SNAKE_CASE_
				)						,		
				)								=						config_and_inputs
				SCREAMING_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       a_       (     SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= (
	    (
	        LiltModel,
	        LiltForSequenceClassification,
	        LiltForTokenClassification,
	        LiltForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	A    							= (
	    {
	        '''feature-extraction''': LiltModel,
	        '''question-answering''': LiltForQuestionAnswering,
	        '''text-classification''': LiltForSequenceClassification,
	        '''token-classification''': LiltForTokenClassification,
	        '''zero-shot''': LiltForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	A    							= False
	A    							= False





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					List[str]:




				"""simple docstring"""



				return True





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						LiltModelTester(self      )
				SCREAMING_SNAKE_CASE_								=						ConfigTester(self      ,     config_class=SCREAMING_SNAKE_CASE      ,     hidden_size=37      )





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				self.config_tester.run_common_tests()





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



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





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE      )





	@slow
	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



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




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





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



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

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

				# forward pass
				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model(input_ids=SCREAMING_SNAKE_CASE      ,     bbox=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						torch.Size([1, 2, 768]      )
				SCREAMING_SNAKE_CASE_								=						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=SCREAMING_SNAKE_CASE      ,     )

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






def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE )
			while cur > 1:
						# Find the maximum number in arr
						SCREAMING_SNAKE_CASE_								=						arr.index(max(arr[0:cur] ) )
						# Reverse from 0 to mi
						SCREAMING_SNAKE_CASE_								=						arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE )]
						# Reverse whole list
						SCREAMING_SNAKE_CASE_								=						arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE )]
						cur -= 1
			return arr


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     List[str]		    =       input("Enter numbers separated by a comma:\n").strip()
				SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       [int(item) for item in user_input.split(",")]
				print(pancake_sort(unsorted))
