

'''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							,					_a      :	Any							,					_a      :	List[str]=13							,					_a      :	Union[str, Any]=7							,					_a      :	List[Any]=True							,					_a      :	Optional[int]=True							,					_a      :	Tuple=True							,					_a      :	Optional[Any]=True							,					_a      :	Optional[int]=99							,					_a      :	str=24							,					_a      :	str=2							,					_a      :	Union[str, Any]=6							,					_a      :	Union[str, Any]=37							,					_a      :	Any="gelu"							,					_a      :	Any=0.1							,					_a      :	List[Any]=0.1							,					_a      :	List[str]=512							,					_a      :	Tuple=16							,					_a      :	List[str]=2							,					_a      :	int=0.02							,					_a      :	Optional[int]=3							,					_a      :	Any=None							,					_a      :	int=1000							,					)	->    Optional[int]:





       '''simple docstring'''






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





  def 						A  (		self      :	int )	->    Dict:





       '''simple docstring'''






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

       _SCREAMING_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]:
                      _SCREAMING_SNAKE_CASE							    =bbox[i, j, 3]
                      _SCREAMING_SNAKE_CASE							    =bbox[i, j, 1]
                      _SCREAMING_SNAKE_CASE							    =t
                 if bbox[i, j, 2] < bbox[i, j, 0]:
                      _SCREAMING_SNAKE_CASE							    =bbox[i, j, 2]
                      _SCREAMING_SNAKE_CASE							    =bbox[i, j, 0]
                      _SCREAMING_SNAKE_CASE							    =t

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

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

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

       _SCREAMING_SNAKE_CASE							    =self.get_config()

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





  def 						A  (		self      :	Optional[int] )	->    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 						A  (		self      :	int							,					_a      :	Any							,					_a      :	str							,					_a      :	Union[str, Any]							,					_a      :	Any							,					_a      :	Tuple							,					_a      :	List[Any]							,					_a      :	Dict							,					)	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =LiltModel(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(_a							,					bbox=_a							,					attention_mask=_a							,					token_type_ids=_a )
       _SCREAMING_SNAKE_CASE							    =model(_a							,					bbox=_a							,					token_type_ids=_a )
       _SCREAMING_SNAKE_CASE							    =model(_a							,					bbox=_a )

       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 						A  (		self      :	Optional[int]							,					_a      :	Tuple							,					_a      :	List[str]							,					_a      :	Optional[Any]							,					_a      :	Any							,					_a      :	Tuple							,					_a      :	Any							,					_a      :	Optional[int]							,					)	->    Union[str, Any]:





       '''simple docstring'''






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





  def 						A  (		self      :	Tuple							,					_a      :	str							,					_a      :	List[str]							,					_a      :	Optional[Any]							,					_a      :	Union[str, Any]							,					_a      :	Dict							,					_a      :	List[Any]							,					_a      :	str							,					)	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =LiltForQuestionAnswering(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(
           _a							,					bbox=_a							,					attention_mask=_a							,					token_type_ids=_a							,					start_positions=_a							,					end_positions=_a							,					)
       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 						A  (		self      :	Dict )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.prepare_config_and_inputs()
       (
           (
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						
       )							    =config_and_inputs
       _SCREAMING_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 							A__    (       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 						A  (		self      :	Tuple							,					_a      :	Union[str, Any]							,					_a      :	int							,					_a      :	List[Any]							,					_a      :	Optional[int]							,					_a      :	int )	->    Optional[Any]:





       '''simple docstring'''






       return True





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =LiltModelTester(self )
       _SCREAMING_SNAKE_CASE							    =ConfigTester(self							,					config_class=_a							,					hidden_size=37 )





  def 						A  (		self      :	Dict )	->    Optional[int]:





       '''simple docstring'''






       self.config_tester.run_common_tests()





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a )





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





       '''simple docstring'''






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





  def 						A  (		self      :	int )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_a )





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_a )





  @slow
  def 						A  (		self      :	Optional[int] )	->    Dict:





       '''simple docstring'''






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







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





  def 						A  (		self      :	Dict )	->    str:





       '''simple docstring'''






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

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

       # forward pass
       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =model(input_ids=_a							,					bbox=_a )

       _SCREAMING_SNAKE_CASE							    =torch.Size([1, 2, 768] )
       _SCREAMING_SNAKE_CASE							    =torch.tensor(
           [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]							,					device=_a							,					)

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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	Dict    = {
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Union[str, Any]    = ['XLMRobertaTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = ['XLMRobertaTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	List[Any]    = [
					    'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'XLMRobertaForCausalLM',
					    'XLMRobertaForMaskedLM',
					    'XLMRobertaForMultipleChoice',
					    'XLMRobertaForQuestionAnswering',
					    'XLMRobertaForSequenceClassification',
					    'XLMRobertaForTokenClassification',
					    'XLMRobertaModel',
					    'XLMRobertaPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFXLMRobertaForCausalLM',
					    'TFXLMRobertaForMaskedLM',
					    'TFXLMRobertaForMultipleChoice',
					    'TFXLMRobertaForQuestionAnswering',
					    'TFXLMRobertaForSequenceClassification',
					    'TFXLMRobertaForTokenClassification',
					    'TFXLMRobertaModel',
					    'TFXLMRobertaPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Tuple    = [
					    'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaxXLMRobertaForMaskedLM',
					    'FlaxXLMRobertaForCausalLM',
					    'FlaxXLMRobertaForMultipleChoice',
					    'FlaxXLMRobertaForQuestionAnswering',
					    'FlaxXLMRobertaForSequenceClassification',
					    'FlaxXLMRobertaForTokenClassification',
					    'FlaxXLMRobertaModel',
					    'FlaxXLMRobertaPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta import (
					    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaConfig,
					    XLMRobertaOnnxConfig,
					)

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta import XLMRobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta import (
										    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaForCausalLM,
										    XLMRobertaForMaskedLM,
										    XLMRobertaForMultipleChoice,
										    XLMRobertaForQuestionAnswering,
										    XLMRobertaForSequenceClassification,
										    XLMRobertaForTokenClassification,
										    XLMRobertaModel,
										    XLMRobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm_roberta import (
										    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMRobertaForCausalLM,
										    TFXLMRobertaForMaskedLM,
										    TFXLMRobertaForMultipleChoice,
										    TFXLMRobertaForQuestionAnswering,
										    TFXLMRobertaForSequenceClassification,
										    TFXLMRobertaForTokenClassification,
										    TFXLMRobertaModel,
										    TFXLMRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xlm_roberta import (
										    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxXLMRobertaForCausalLM,
										    FlaxXLMRobertaForMaskedLM,
										    FlaxXLMRobertaForMultipleChoice,
										    FlaxXLMRobertaForQuestionAnswering,
										    FlaxXLMRobertaForSequenceClassification,
										    FlaxXLMRobertaForTokenClassification,
										    FlaxXLMRobertaModel,
										    FlaxXLMRobertaPreTrainedModel,
										)

else:
					import sys

					A     :	Tuple    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)