
"""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  lowerCAmelCase__							:







     def __init__(				self     :     Any       , _lowerCamelCase     :     int       , _lowerCamelCase     :     Optional[Any]=13       , _lowerCamelCase     :     Any=7       , _lowerCamelCase     :     Union[str, Any]=True       , _lowerCamelCase     :     Optional[Any]=True       , _lowerCamelCase     :     Union[str, Any]=True       , _lowerCamelCase     :     Optional[Any]=True       , _lowerCamelCase     :     Tuple=99       , _lowerCamelCase     :     List[str]=24       , _lowerCamelCase     :     List[Any]=2       , _lowerCamelCase     :     Optional[int]=6       , _lowerCamelCase     :     Optional[Any]=37       , _lowerCamelCase     :     Tuple="gelu"       , _lowerCamelCase     :     Optional[int]=0.1       , _lowerCamelCase     :     Optional[Any]=0.1       , _lowerCamelCase     :     List[Any]=512       , _lowerCamelCase     :     Optional[Any]=16       , _lowerCamelCase     :     Tuple=2       , _lowerCamelCase     :     Tuple=0.0_2       , _lowerCamelCase     :     Dict=3       , _lowerCamelCase     :     Optional[Any]=None       , _lowerCamelCase     :     Optional[Any]=1000       , ):
       _snake_case   	=			parent
       _snake_case   	=			batch_size
       _snake_case   	=			seq_length
       _snake_case   	=			is_training
       _snake_case   	=			use_input_mask
       _snake_case   	=			use_token_type_ids
       _snake_case   	=			use_labels
       _snake_case   	=			vocab_size
       _snake_case   	=			hidden_size
       _snake_case   	=			num_hidden_layers
       _snake_case   	=			num_attention_heads
       _snake_case   	=			intermediate_size
       _snake_case   	=			hidden_act
       _snake_case   	=			hidden_dropout_prob
       _snake_case   	=			attention_probs_dropout_prob
       _snake_case   	=			max_position_embeddings
       _snake_case   	=			type_vocab_size
       _snake_case   	=			type_sequence_label_size
       _snake_case   	=			initializer_range
       _snake_case   	=			num_labels
       _snake_case   	=			scope
       _snake_case   	=			range_bbox







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

       _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]:
             _snake_case   	=			bbox[i, j, 3]
             _snake_case   	=			bbox[i, j, 1]
             _snake_case   	=			t
           if bbox[i, j, 2] < bbox[i, j, 0]:
             _snake_case   	=			bbox[i, j, 2]
             _snake_case   	=			bbox[i, j, 0]
             _snake_case   	=			t

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

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

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

       _snake_case   	=			self.get_config()

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







     def 	lowercase      (				self     :     Optional[int]     ):
       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 	lowercase      (				self     :     Dict       , _lowerCamelCase     :     Tuple       , _lowerCamelCase     :     str       , _lowerCamelCase     :     Any       , _lowerCamelCase     :     int       , _lowerCamelCase     :     int       , _lowerCamelCase     :     Optional[Any]       , _lowerCamelCase     :     List[str]       , ):
       _snake_case   	=			LiltModel(config=_lowercase     )
       model.to(_lowercase     )
       model.eval()
       _snake_case   	=			model(_lowercase       , bbox=_lowercase       , attention_mask=_lowercase       , token_type_ids=_lowercase     )
       _snake_case   	=			model(_lowercase       , bbox=_lowercase       , token_type_ids=_lowercase     )
       _snake_case   	=			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 	lowercase      (				self     :     Optional[int]       , _lowerCamelCase     :     int       , _lowerCamelCase     :     str       , _lowerCamelCase     :     Tuple       , _lowerCamelCase     :     Optional[int]       , _lowerCamelCase     :     str       , _lowerCamelCase     :     Any       , _lowerCamelCase     :     List[Any]       , ):
       _snake_case   	=			self.num_labels
       _snake_case   	=			LiltForTokenClassification(config=_lowercase     )
       model.to(_lowercase     )
       model.eval()
       _snake_case   	=			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 	lowercase      (				self     :     str       , _lowerCamelCase     :     str       , _lowerCamelCase     :     Optional[int]       , _lowerCamelCase     :     Union[str, Any]       , _lowerCamelCase     :     Union[str, Any]       , _lowerCamelCase     :     Dict       , _lowerCamelCase     :     Tuple       , _lowerCamelCase     :     List[Any]       , ):
       _snake_case   	=			LiltForQuestionAnswering(config=_lowercase     )
       model.to(_lowercase     )
       model.eval()
       _snake_case   	=			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 	lowercase      (				self     :     List[str]     ):
       _snake_case   	=			self.prepare_config_and_inputs()
       (
           (
           _snake_case       
       )	,   (
           _snake_case       
       )	,   (
           _snake_case       
       )	,   (
           _snake_case       
       )	,   (
           _snake_case       
       )	,   (
           _snake_case       
       )	,   (
           _snake_case       
       )	,          
       )   	=			config_and_inputs
       _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  lowerCAmelCase__							(		A_					,      A_					,      A_					,      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 	lowercase      (				self     :     List[Any]       , _lowerCamelCase     :     Optional[Any]       , _lowerCamelCase     :     List[str]       , _lowerCamelCase     :     Union[str, Any]       , _lowerCamelCase     :     Any       , _lowerCamelCase     :     Optional[Any]     ):
       return True







     def 	lowercase      (				self     :     List[Any]     ):
       _snake_case   	=			LiltModelTester(self     )
       _snake_case   	=			ConfigTester(self       , config_class=_lowercase       , hidden_size=37     )







     def 	lowercase      (				self     :     Optional[int]     ):
       self.config_tester.run_common_tests()







     def 	lowercase      (				self     :     Dict     ):
       _snake_case   	=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowercase     )







     def 	lowercase      (				self     :     Tuple     ):
       _snake_case   	=			self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         _snake_case   	=			type
         self.model_tester.create_and_check_model(*_lowercase     )







     def 	lowercase      (				self     :     Dict     ):
       _snake_case   	=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_lowercase     )







     def 	lowercase      (				self     :     Tuple     ):
       _snake_case   	=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_lowercase     )







     @slow
     def 	lowercase      (				self     :     Any     ):
       for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case   	=			LiltModel.from_pretrained(_lowercase     )
         self.assertIsNotNone(_lowercase     )






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







     def 	lowercase      (				self     :     List[str]     ):
       _snake_case   	=			LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''     ).to(_lowercase     )

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

       # forward pass
       with torch.no_grad():
         _snake_case   	=			model(input_ids=_lowercase       , bbox=_lowercase     )

       _snake_case   	=			torch.Size([1, 2, 768]     )
       _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=_lowercase       , )

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






def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    if exponent == 1:
        return base
    if exponent % 2 == 0:
        UpperCamelCase_			  =						_modexpt(SCREAMING_SNAKE_CASE_			,						exponent // 2			,						SCREAMING_SNAKE_CASE_			) % modulo_value
        return (x * x) % modulo_value
    else:
        return (base * _modexpt(SCREAMING_SNAKE_CASE_			,						exponent - 1			,						SCREAMING_SNAKE_CASE_			)) % modulo_value




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 1_7_7_7			,						SCREAMING_SNAKE_CASE_ = 1_8_5_5			,						SCREAMING_SNAKE_CASE_ = 8			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						base
    for _ in range(1			,						SCREAMING_SNAKE_CASE_			):
        UpperCamelCase_			  =						_modexpt(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						1_0**digits			)

    return result


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