
'''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  __A				:
 def __init__(   self			:    str				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    Any=13				,   lowerCamelCase			:    Dict=7				,   lowerCamelCase			:    Union[str, Any]=True				,   lowerCamelCase			:    str=True				,   lowerCamelCase			:    Tuple=True				,   lowerCamelCase			:    Optional[int]=True				,   lowerCamelCase			:    Tuple=99				,   lowerCamelCase			:    Union[str, Any]=24				,   lowerCamelCase			:    Tuple=2				,   lowerCamelCase			:    Union[str, Any]=6				,   lowerCamelCase			:    List[Any]=37				,   lowerCamelCase			:    str="gelu"				,   lowerCamelCase			:    Optional[int]=0.1				,   lowerCamelCase			:    Tuple=0.1				,   lowerCamelCase			:    Optional[Any]=5_12				,   lowerCamelCase			:    Any=16				,   lowerCamelCase			:    Tuple=2				,   lowerCamelCase			:    Any=0.02				,   lowerCamelCase			:    Union[str, Any]=3				,   lowerCamelCase			:    Union[str, Any]=None				,   lowerCamelCase			:    Dict=10_00				,   ):



   """simple docstring"""





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



   """simple docstring"""





   __A		:     Tuple												=		ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size       )

   __A		:     Tuple												=		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		:     Any												=		bbox[i, j, 3]
         __A		:     List[Any]												=		bbox[i, j, 1]
         __A		:     Dict												=		t
       if bbox[i, j, 2] < bbox[i, j, 0]:
         __A		:     int												=		bbox[i, j, 2]
         __A		:     List[Any]												=		bbox[i, j, 0]
         __A		:     str												=		t

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

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

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

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

   return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
 def 		lowercase_(   self			:    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 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    str				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    str				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    str				,   lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





   __A		:     Any												=		LiltModel(config=lowerCamelCase       )
   model.to(lowerCamelCase       )
   model.eval()
   __A		:     Tuple												=		model(lowerCamelCase				,   bbox=lowerCamelCase				,   attention_mask=lowerCamelCase				,   token_type_ids=lowerCamelCase       )
   __A		:     List[Any]												=		model(lowerCamelCase				,   bbox=lowerCamelCase				,   token_type_ids=lowerCamelCase       )
   __A		:     Any												=		model(lowerCamelCase				,   bbox=lowerCamelCase       )

   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			:    int				,   lowerCamelCase			:    Tuple				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    int				,   lowerCamelCase			:    Tuple				,   lowerCamelCase			:    Union[str, Any]				,   ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		self.num_labels
   __A		:     Union[str, Any]												=		LiltForTokenClassification(config=lowerCamelCase       )
   model.to(lowerCamelCase       )
   model.eval()
   __A		:     Optional[int]												=		model(
       lowerCamelCase				,   bbox=lowerCamelCase				,   attention_mask=lowerCamelCase				,   token_type_ids=lowerCamelCase				,   labels=lowerCamelCase       )
   self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.num_labels)       )
 def 		lowercase_(   self			:    List[Any]				,   lowerCamelCase			:    Any				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    Optional[int]				,   lowerCamelCase			:    Optional[int]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    Any				,   lowerCamelCase			:    str				,   ):



   """simple docstring"""





   __A		:     Any												=		LiltForQuestionAnswering(config=lowerCamelCase       )
   model.to(lowerCamelCase       )
   model.eval()
   __A		:     Union[str, Any]												=		model(
       lowerCamelCase				,   bbox=lowerCamelCase				,   attention_mask=lowerCamelCase				,   token_type_ids=lowerCamelCase				,   start_positions=lowerCamelCase				,   end_positions=lowerCamelCase				,   )
   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			:    int       ):



   """simple docstring"""





   __A		:     List[str]												=		self.prepare_config_and_inputs()
   (
       __A					
   )		:     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  __A				( _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						unittest.TestCase			):
 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 		lowercase_(   self			:    Any				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    Any				,   lowerCamelCase			:    Optional[Any]       ):



   """simple docstring"""





   return True
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		LiltModelTester(self       )
   __A		:     Union[str, Any]												=		ConfigTester(self				,   config_class=lowerCamelCase				,   hidden_size=37       )
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   self.config_tester.run_common_tests()
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   __A		:     str												=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   __A		:     Dict												=		self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
     __A		:     List[Any]												=		type
     self.model_tester.create_and_check_model(*lowerCamelCase       )
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   __A		:     Dict												=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowerCamelCase       )
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   __A		:     Tuple												=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*lowerCamelCase       )






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



   """simple docstring"""





   for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __A		:     Any												=		LiltModel.from_pretrained(lowerCamelCase       )
     self.assertIsNotNone(lowerCamelCase       )







@require_torch
@slow
class  __A				( unittest.TestCase			):
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     Tuple												=		LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""       ).to(lowerCamelCase       )

   __A		:     str												=		torch.tensor([[1, 2]]				,   device=lowerCamelCase       )
   __A		:     int												=		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,   device=lowerCamelCase       )

   # forward pass
   with torch.no_grad():
     __A		:     int												=		model(input_ids=lowerCamelCase				,   bbox=lowerCamelCase       )

   __A		:     Optional[Any]												=		torch.Size([1, 2, 7_68]       )
   __A		:     Tuple												=		torch.tensor(
       [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]]				,   device=lowerCamelCase				,   )

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

'''simple docstring'''
A__   :		List[Any] 		=[
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages



