




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  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=13  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=99  ,_SCREAMING_SNAKE_CASE=24  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=6  ,_SCREAMING_SNAKE_CASE=37  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=512  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=1_000  ,)					->						Union[str, Any]:
         UpperCAmelCase_     :				Any  =     parent
         UpperCAmelCase_     :				List[Any]  =     batch_size
         UpperCAmelCase_     :				Union[str, Any]  =     seq_length
         UpperCAmelCase_     :				int  =     is_training
         UpperCAmelCase_     :				List[str]  =     use_input_mask
         UpperCAmelCase_     :				int  =     use_token_type_ids
         UpperCAmelCase_     :				Any  =     use_labels
         UpperCAmelCase_     :				int  =     vocab_size
         UpperCAmelCase_     :				List[Any]  =     hidden_size
         UpperCAmelCase_     :				List[str]  =     num_hidden_layers
         UpperCAmelCase_     :				List[Any]  =     num_attention_heads
         UpperCAmelCase_     :				str  =     intermediate_size
         UpperCAmelCase_     :				Dict  =     hidden_act
         UpperCAmelCase_     :				Optional[Any]  =     hidden_dropout_prob
         UpperCAmelCase_     :				Optional[Any]  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				Optional[int]  =     max_position_embeddings
         UpperCAmelCase_     :				Optional[int]  =     type_vocab_size
         UpperCAmelCase_     :				Union[str, Any]  =     type_sequence_label_size
         UpperCAmelCase_     :				Any  =     initializer_range
         UpperCAmelCase_     :				List[str]  =     num_labels
         UpperCAmelCase_     :				List[str]  =     scope
         UpperCAmelCase_     :				int  =     range_bbox





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				List[str]  =     ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size    )

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

         UpperCAmelCase_     :				int  =     None
         if self.use_input_mask:
              UpperCAmelCase_     :				Union[str, Any]  =     ids_tensor([self.batch_size, self.seq_length]  ,vocab_size=2    )

         UpperCAmelCase_     :				Union[str, Any]  =     None
         if self.use_token_type_ids:
              UpperCAmelCase_     :				List[str]  =     ids_tensor([self.batch_size, self.seq_length]  ,self.type_vocab_size    )

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

         UpperCAmelCase_     :				Optional[Any]  =     self.get_config()

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





    def 				a__ ( self    )					->						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 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,)					->						Tuple:
         UpperCAmelCase_     :				Dict  =     LiltModel(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Dict  =     model(_SCREAMING_SNAKE_CASE  ,bbox=_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,token_type_ids=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     model(_SCREAMING_SNAKE_CASE  ,bbox=_SCREAMING_SNAKE_CASE  ,token_type_ids=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[str]  =     model(_SCREAMING_SNAKE_CASE  ,bbox=_SCREAMING_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 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,)					->						str:
         UpperCAmelCase_     :				Tuple  =     self.num_labels
         UpperCAmelCase_     :				Dict  =     LiltForTokenClassification(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Any  =     model(
             _SCREAMING_SNAKE_CASE  ,bbox=_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,token_type_ids=_SCREAMING_SNAKE_CASE  ,labels=_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,)					->						Tuple:
         UpperCAmelCase_     :				List[Any]  =     LiltForQuestionAnswering(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				List[str]  =     model(
             _SCREAMING_SNAKE_CASE  ,bbox=_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,token_type_ids=_SCREAMING_SNAKE_CASE  ,start_positions=_SCREAMING_SNAKE_CASE  ,end_positions=_SCREAMING_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 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				List[str]  =     self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       
         )     :				Tuple  =     config_and_inputs
         UpperCAmelCase_     :				Dict  =     {
             '''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  ):






    """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 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						int:
         return True





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Any  =     LiltModelTester(self    )
         UpperCAmelCase_     :				Tuple  =     ConfigTester(self  ,config_class=_SCREAMING_SNAKE_CASE  ,hidden_size=37    )





    def 				a__ ( self    )					->						Optional[Any]:
         self.config_tester.run_common_tests()





    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				Optional[Any]  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Union[str, Any]  =     self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCAmelCase_     :				Any  =     type
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				Optional[Any]  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				Optional[Any]  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE    )





    @slow
    def 				a__ ( self    )					->						Dict:
         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_     :				Any  =     LiltModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )


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






    """simple docstring"""





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Optional[Any]  =     LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''    ).to(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[Any]  =     torch.tensor([[1, 2]]  ,device=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				str  =     torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]  ,device=_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Optional[int]  =     model(input_ids=_SCREAMING_SNAKE_CASE  ,bbox=_SCREAMING_SNAKE_CASE    )

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

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a							     =					{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					[
         'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
         'EncodecModel',
         'EncodecPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_encodec import (
         ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EncodecConfig,
     )
     from .feature_extraction_encodec import EncodecFeatureExtractor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_encodec import (
              ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
              EncodecModel,
              EncodecPreTrainedModel,
          )

else:
     import sys

     __a							     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)