





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     __magic_name__     :





     """simple docstring"""



     def __init__(							self				:int	,					snake_case				:Optional[int]	,					snake_case				:Union[str, Any]=13	,					snake_case				:Optional[int]=7	,					snake_case				:int=True	,					snake_case				:Any=True	,					snake_case				:Union[str, Any]=True	,					snake_case				:List[Any]=True	,					snake_case				:int=99	,					snake_case				:Tuple=24	,					snake_case				:Any=2	,					snake_case				:Tuple=6	,					snake_case				:int=37	,					snake_case				:Optional[Any]="gelu"	,					snake_case				:Optional[Any]=0.1	,					snake_case				:List[str]=0.1	,					snake_case				:str=512	,					snake_case				:List[str]=16	,					snake_case				:int=2	,					snake_case				:List[str]=0.02	,					snake_case				:int=3	,					snake_case				:Optional[Any]=None	,					snake_case				:str=1_000	,					):





        '''simple docstring'''




        A_    : List[str]								=	parent
        A_    : Union[str, Any]								=	batch_size
        A_    : Tuple								=	seq_length
        A_    : Any								=	is_training
        A_    : Optional[int]								=	use_input_mask
        A_    : Any								=	use_token_type_ids
        A_    : int								=	use_labels
        A_    : str								=	vocab_size
        A_    : int								=	hidden_size
        A_    : Dict								=	num_hidden_layers
        A_    : List[Any]								=	num_attention_heads
        A_    : str								=	intermediate_size
        A_    : int								=	hidden_act
        A_    : str								=	hidden_dropout_prob
        A_    : List[Any]								=	attention_probs_dropout_prob
        A_    : Union[str, Any]								=	max_position_embeddings
        A_    : Optional[Any]								=	type_vocab_size
        A_    : int								=	type_sequence_label_size
        A_    : str								=	initializer_range
        A_    : List[Any]								=	num_labels
        A_    : List[str]								=	scope
        A_    : str								=	range_bbox



     def   SCREAMING_SNAKE_CASE						(							self				:Tuple     ):





        '''simple docstring'''




        A_    : List[Any]								=	ids_tensor([self.batch_size, self.seq_length]	,					self.vocab_size     )

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

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

        A_    : List[Any]								=	None
        if self.use_token_type_ids:
           A_    : Tuple								=	ids_tensor([self.batch_size, self.seq_length]	,					self.type_vocab_size     )

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

        A_    : Tuple								=	self.get_config()

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



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''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   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]	,					snake_case				:Optional[Any]	,					snake_case				:str	,					snake_case				:Union[str, Any]	,					snake_case				:Any	,					snake_case				:Any	,					snake_case				:List[str]	,					snake_case				:Any	,					):





        '''simple docstring'''




        A_    : str								=	LiltModel(config=snake_case     )
        model.to(snake_case     )
        model.eval()
        A_    : str								=	model(snake_case	,					bbox=snake_case	,					attention_mask=snake_case	,					token_type_ids=snake_case     )
        A_    : int								=	model(snake_case	,					bbox=snake_case	,					token_type_ids=snake_case     )
        A_    : List[Any]								=	model(snake_case	,					bbox=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   SCREAMING_SNAKE_CASE						(							self				:Tuple	,					snake_case				:str	,					snake_case				:List[Any]	,					snake_case				:Union[str, Any]	,					snake_case				:Union[str, Any]	,					snake_case				:int	,					snake_case				:Union[str, Any]	,					snake_case				:Tuple	,					):





        '''simple docstring'''




        A_    : Tuple								=	self.num_labels
        A_    : List[str]								=	LiltForTokenClassification(config=snake_case     )
        model.to(snake_case     )
        model.eval()
        A_    : Dict								=	model(
            snake_case	,					bbox=snake_case	,					attention_mask=snake_case	,					token_type_ids=snake_case	,					labels=snake_case     )
        self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.seq_length, self.num_labels)     )



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:int	,					snake_case				:Optional[Any]	,					snake_case				:List[str]	,					snake_case				:str	,					snake_case				:str	,					snake_case				:Any	,					snake_case				:Union[str, Any]	,					):





        '''simple docstring'''




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





        '''simple docstring'''




        A_    : Tuple								=	self.prepare_config_and_inputs()
        (
            A_							
        )    : Optional[int]								=	config_and_inputs
        A_    : Any								=	{
            'input_ids': input_ids,
            'bbox': bbox,
            'token_type_ids': token_type_ids,
            'attention_mask': input_mask,
        }
        return config, inputs_dict




@require_torch
class     __magic_name__     (							__UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase			, unittest.TestCase							):





     """simple docstring"""



     __UpperCamelCase    			=   (
         (
             LiltModel,
             LiltForSequenceClassification,
             LiltForTokenClassification,
             LiltForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     __UpperCamelCase    			=   (
         {
             '''feature-extraction''': LiltModel,
             '''question-answering''': LiltForQuestionAnswering,
             '''text-classification''': LiltForSequenceClassification,
             '''token-classification''': LiltForTokenClassification,
             '''zero-shot''': LiltForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __UpperCamelCase    			=   False
     __UpperCamelCase    			=   False



     def   SCREAMING_SNAKE_CASE						(							self				:Dict	,					snake_case				:List[Any]	,					snake_case				:int	,					snake_case				:Any	,					snake_case				:str	,					snake_case				:List[str]     ):





        '''simple docstring'''




        return True



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





        '''simple docstring'''




        A_    : int								=	LiltModelTester(self     )
        A_    : Tuple								=	ConfigTester(self	,					config_class=snake_case	,					hidden_size=37     )



     def   SCREAMING_SNAKE_CASE						(							self				:int     ):





        '''simple docstring'''




        self.config_tester.run_common_tests()



     def   SCREAMING_SNAKE_CASE						(							self				:int     ):





        '''simple docstring'''




        A_    : Optional[int]								=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case     )



     def   SCREAMING_SNAKE_CASE						(							self				:Dict     ):





        '''simple docstring'''




        A_    : int								=	self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
           A_    : Union[str, Any]								=	type
           self.model_tester.create_and_check_model(*snake_case     )



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]     ):





        '''simple docstring'''




        A_    : Optional[int]								=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*snake_case     )



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





        '''simple docstring'''




        A_    : Union[str, Any]								=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*snake_case     )





     @slow
     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




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




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





     """simple docstring"""



     def   SCREAMING_SNAKE_CASE						(							self				:str     ):





        '''simple docstring'''




        A_    : List[str]								=	LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"     ).to(snake_case     )

        A_    : List[Any]								=	torch.tensor([[1, 2]]	,					device=snake_case     )
        A_    : List[Any]								=	torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]	,					device=snake_case     )

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

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

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







from itertools import count


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase = 50 )			->     int:
    lowerCamelCase__    :      Optional[Any]					 =		[1] * min_block_length

    for n in count(_UpperCAmelCase ):
        fill_count_functions.append(1 )

        for block_length in range(_UpperCAmelCase						,	n + 1 ):
            for block_start in range(n - block_length ):
                fill_count_functions[n] += fill_count_functions[
                    n - block_start - block_length - 1
                ]

            fill_count_functions[n] += 1

        if fill_count_functions[n] > 100_0000:
            break

    return n


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





