

import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union


@dataclass
class 			A						:







    """simple docstring"""





    lowerCamelCase		     =							None
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							False
    lowerCamelCase		     =							True
    lowerCamelCase		     =							None
    lowerCamelCase		     =							1
    lowerCamelCase		     =							None
    lowerCamelCase		     =							False
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None


    def 						snake_case__					(				self			:	Dict		)->   "DownloadConfig":





        '''simple docstring'''







        return self.__class__(**{k: copy.deepcopy(lowercase_		) for k, v in self.__dict__.items()}		)




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						:







    """simple docstring"""





    def __init__(				self			:	Any,lowercase_			:	Optional[int],lowercase_			:	Optional[int]=1_3,lowercase_			:	int=7,lowercase_			:	List[str]=True,lowercase_			:	str=True,lowercase_			:	List[str]=True,lowercase_			:	Optional[Any]=True,lowercase_			:	Dict=9_9,lowercase_			:	Dict=2_4,lowercase_			:	Union[str, Any]=2,lowercase_			:	str=6,lowercase_			:	Dict=3_7,lowercase_			:	Optional[Any]="gelu",lowercase_			:	Any=0.1,lowercase_			:	Any=0.1,lowercase_			:	Any=5_1_2,lowercase_			:	Dict=1_6,lowercase_			:	List[str]=2,lowercase_			:	Dict=0.02,lowercase_			:	Any=3,lowercase_			:	Dict=None,lowercase_			:	List[str]=1_0_0_0,)->   Optional[Any]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	List[Any]		)->   Tuple:





        '''simple docstring'''







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

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

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

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

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

        A__   				=       self.get_config()

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


    def 						snake_case__					(				self			:	Dict		)->   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 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Tuple,lowercase_			:	str,lowercase_			:	Optional[int],lowercase_			:	Optional[Any],lowercase_			:	str,lowercase_			:	List[str],lowercase_			:	Tuple,)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       LiltModel(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__   				=       model(lowercase_,bbox=lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_		)
        A__   				=       model(lowercase_,bbox=lowercase_,token_type_ids=lowercase_		)
        A__   				=       model(lowercase_,bbox=lowercase_		)

        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			:	Any,lowercase_			:	Dict,lowercase_			:	List[Any],lowercase_			:	List[str],lowercase_			:	Optional[int],lowercase_			:	Optional[int],lowercase_			:	Optional[int],lowercase_			:	List[Any],)->   List[str]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	int,lowercase_			:	Union[str, Any],lowercase_			:	int,lowercase_			:	Tuple,lowercase_			:	Any,lowercase_			:	Optional[int],lowercase_			:	Tuple,lowercase_			:	List[str],)->   Any:





        '''simple docstring'''







        A__   				=       LiltForQuestionAnswering(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__   				=       model(
            lowercase_,bbox=lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_,start_positions=lowercase_,end_positions=lowercase_,)
        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]		)->   Tuple:





        '''simple docstring'''







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




@require_torch
class 			A						(							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							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 						snake_case__					(				self			:	List[str],lowercase_			:	int,lowercase_			:	List[str],lowercase_			:	str,lowercase_			:	Optional[Any],lowercase_			:	Optional[Any]		)->   Any:





        '''simple docstring'''







        return True


    def 						snake_case__					(				self			:	int		)->   Tuple:





        '''simple docstring'''







        A__   				=       LiltModelTester(self		)
        A__   				=       ConfigTester(self,config_class=lowercase_,hidden_size=3_7		)


    def 						snake_case__					(				self			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        self.config_tester.run_common_tests()


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





        '''simple docstring'''







        A__   				=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


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





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	str		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase_		)


    def 						snake_case__					(				self			:	List[Any]		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase_		)


    @slow
    def 						snake_case__					(				self			:	List[Any]		)->   int:





        '''simple docstring'''







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




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







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any]		)->   Dict:





        '''simple docstring'''







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

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

        # forward pass
        with torch.no_grad():
            A__   				=       model(input_ids=lowercase_,bbox=lowercase_		)

        A__   				=       torch.Size([1, 2, 7_6_8]		)
        A__   				=       torch.tensor(
            [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]],device=lowercase_,)

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

