




"""simple docstring"""



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_  :




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




     '''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 							__magic_name__							(   self						:	List[Any]				)				->							Dict:




     '''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 							__magic_name__							(   self						:	str				)				->							List[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 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Any     ,			snake_case_						:	int     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Dict     ,			snake_case_						:	List[str]     ,			snake_case_						:	str     ,			)				->							Optional[int]:




     '''simple docstring'''

     A__	=    LiltModel(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			bbox=snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_				)
     A__	=    model(snake_case_     ,			bbox=snake_case_     ,			token_type_ids=snake_case_				)
     A__	=    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 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	int     ,			snake_case_						:	Dict     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Dict     ,			snake_case_						:	List[Any]     ,			)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.num_labels
     A__	=    LiltForTokenClassification(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    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 							__magic_name__							(   self						:	int     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[str]     ,			snake_case_						:	List[str]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	List[str]     ,			snake_case_						:	str     ,			snake_case_						:	str     ,			)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    LiltForQuestionAnswering(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    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 							__magic_name__							(   self						:	Any				)				->							Optional[Any]:




     '''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      UpperCAmelCase_  (						A_,       A_,       A_,       unittest.TestCase							):
    lowercase__						 =				(
        (
            LiltModel,
            LiltForSequenceClassification,
            LiltForTokenClassification,
            LiltForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowercase__						 =				(
        {
            '''feature-extraction''': LiltModel,
            '''question-answering''': LiltForQuestionAnswering,
            '''text-classification''': LiltForSequenceClassification,
            '''token-classification''': LiltForTokenClassification,
            '''zero-shot''': LiltForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowercase__						 =				False
    lowercase__						 =				False




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	int     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	List[str]     ,			snake_case_						:	List[Any]				)				->							Optional[int]:




     '''simple docstring'''

     return True




    def 							__magic_name__							(   self						:	List[str]				)				->							Tuple:




     '''simple docstring'''

     A__	=    LiltModelTester(self				)
     A__	=    ConfigTester(self     ,			config_class=snake_case_     ,			hidden_size=37				)




    def 							__magic_name__							(   self						:	Tuple				)				->							List[str]:




     '''simple docstring'''

     self.config_tester.run_common_tests()




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




     '''simple docstring'''

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




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




     '''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(*snake_case_				)




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




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	int				)				->							Optional[int]:




     '''simple docstring'''

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




    @slow
    def 							__magic_name__							(   self						:	Any				)				->							Optional[int]:




     '''simple docstring'''

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






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




    def 							__magic_name__							(   self						:	Tuple				)				->							Any:




     '''simple docstring'''

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

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

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

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











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	[
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

SCREAMING_SNAKE_CASE    =	[
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

SCREAMING_SNAKE_CASE    =	[
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

SCREAMING_SNAKE_CASE    =	[
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

SCREAMING_SNAKE_CASE    =	[
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

SCREAMING_SNAKE_CASE    =	[
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

SCREAMING_SNAKE_CASE    =	[
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

SCREAMING_SNAKE_CASE    =	[
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]





