

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 _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: str      ,      a							: List[Any]      ,      a							: Optional[Any]=13      ,      a							: int=7      ,      a							: Dict=True      ,      a							: Any=True      ,      a							: Tuple=True      ,      a							: Dict=True      ,      a							: Union[str, Any]=99      ,      a							: Optional[Any]=24      ,      a							: Optional[int]=2      ,      a							: Union[str, Any]=6      ,      a							: str=37      ,      a							: Dict="gelu"      ,      a							: Dict=0.1      ,      a							: Dict=0.1      ,      a							: Optional[int]=512      ,      a							: Optional[int]=16      ,      a							: Optional[Any]=2      ,      a							: Optional[Any]=0.02      ,      a							: List[Any]=3      ,      a							: str=None      ,      a							: Dict=1000      ,      )							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       parent
				SCREAMING_SNAKE_CASE   :      Tuple				     =       batch_size
				SCREAMING_SNAKE_CASE   :      str				     =       seq_length
				SCREAMING_SNAKE_CASE   :      Tuple				     =       is_training
				SCREAMING_SNAKE_CASE   :      Dict				     =       use_input_mask
				SCREAMING_SNAKE_CASE   :      int				     =       use_token_type_ids
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       use_labels
				SCREAMING_SNAKE_CASE   :      Any				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      Any				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      List[str]				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      List[str]				     =       hidden_dropout_prob
				SCREAMING_SNAKE_CASE   :      Any				     =       attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE   :      str				     =       max_position_embeddings
				SCREAMING_SNAKE_CASE   :      Tuple				     =       type_vocab_size
				SCREAMING_SNAKE_CASE   :      Any				     =       type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      List[str]				     =       num_labels
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       scope
				SCREAMING_SNAKE_CASE   :      Dict				     =       range_bbox
			def 							__UpperCamelCase (			self							: int	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size	)

				SCREAMING_SNAKE_CASE   :      Any				     =       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   :      Union[str, Any]				     =       bbox[i, j, 3]
							SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       bbox[i, j, 1]
							SCREAMING_SNAKE_CASE   :      Optional[int]				     =       t
						if bbox[i, j, 2] < bbox[i, j, 0]:
							SCREAMING_SNAKE_CASE   :      str				     =       bbox[i, j, 2]
							SCREAMING_SNAKE_CASE   :      Dict				     =       bbox[i, j, 0]
							SCREAMING_SNAKE_CASE   :      Dict				     =       t

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

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None
				if self.use_token_type_ids:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ids_tensor([self.batch_size, self.seq_length]      ,      self.type_vocab_size	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       None
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       None
				if self.use_labels:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       ids_tensor([self.batch_size]      ,      self.type_sequence_label_size	)
					SCREAMING_SNAKE_CASE   :      List[str]				     =       ids_tensor([self.batch_size, self.seq_length]      ,      self.num_labels	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.get_config()

				return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
			def 							__UpperCamelCase (			self							: List[Any]	)							->       int:

				"""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 							__UpperCamelCase (			self							: Any      ,      a							: str      ,      a							: List[str]      ,      a							: Any      ,      a							: Optional[int]      ,      a							: Optional[int]      ,      a							: Any      ,      a							: str      ,      )							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       LiltModel(config=a	)
				model.to(a	)
				model.eval()
				SCREAMING_SNAKE_CASE   :      str				     =       model(a      ,      bbox=a      ,      attention_mask=a      ,      token_type_ids=a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model(a      ,      bbox=a      ,      token_type_ids=a	)
				SCREAMING_SNAKE_CASE   :      str				     =       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 							__UpperCamelCase (			self							: List[Any]      ,      a							: Optional[int]      ,      a							: List[Any]      ,      a							: Optional[int]      ,      a							: Tuple      ,      a							: Dict      ,      a							: str      ,      a							: int      ,      )							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.num_labels
				SCREAMING_SNAKE_CASE   :      Tuple				     =       LiltForTokenClassification(config=a	)
				model.to(a	)
				model.eval()
				SCREAMING_SNAKE_CASE   :      str				     =       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 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: str      ,      a							: Dict      ,      a							: List[Any]      ,      a							: int      ,      a							: Any      ,      a							: List[Any]      ,      a							: Dict      ,      )							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       LiltForQuestionAnswering(config=a	)
				model.to(a	)
				model.eval()
				SCREAMING_SNAKE_CASE   :      str				     =       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 							__UpperCamelCase (			self							: Optional[Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       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
				)     ,
				)   :      Tuple				     =       config_and_inputs
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       {
				    "input_ids": input_ids,
				    "bbox": bbox,
				    "token_type_ids": token_type_ids,
				    "attention_mask": input_mask,
				}
				return config, inputs_dict






@require_torch
class _UpperCamelCase							(							__A							,	__A							,	__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(
			    (
			        LiltModel,
			        LiltForSequenceClassification,
			        LiltForTokenClassification,
			        LiltForQuestionAnswering,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__           =(
			    {
			        'feature-extraction': LiltModel,
			        'question-answering': LiltForQuestionAnswering,
			        'text-classification': LiltForSequenceClassification,
			        'token-classification': LiltForTokenClassification,
			        'zero-shot': LiltForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: str      ,      a							: Optional[Any]      ,      a							: Tuple      ,      a							: Tuple      ,      a							: Any      ,      a							: str	)							->       List[str]:

				"""simple docstring"""

				return True
			def 							__UpperCamelCase (			self							: Dict	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       LiltModelTester(self	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ConfigTester(self      ,      config_class=a      ,      hidden_size=37	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[str]:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 							__UpperCamelCase (			self							: Dict	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					SCREAMING_SNAKE_CASE   :      str				     =       type
					self.model_tester.create_and_check_model(*a	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*a	)
			def 							__UpperCamelCase (			self							: Any	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*a	)






			@slow
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Dict:

				"""simple docstring"""

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






@require_torch
@slow
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"	).to(a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.tensor([[1, 2]]      ,      device=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]      ,      device=a	)

				# forward pass
				with torch.no_grad():
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model(input_ids=a      ,      bbox=a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.Size([1, 2, 768]	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.tensor(
				    [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]      ,      device=a      ,      )

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


def  lowerCamelCase__ (					_a = 50):
	SCREAMING_SNAKE_CASE   :      int				     =       [1] * (length + 1)

	for row_length in range(length + 1):
		for tile_length in range(2							,			5):
			for tile_start in range(row_length - tile_length + 1):
				ways_number[row_length] += ways_number[
				    row_length - tile_start - tile_length
				]

	return ways_number[length]


if __name__ == "__main__":
			print(F'''{solution() = }''')