"""simple docstring"""







import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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 (
           LayoutLMForMaskedLM,
           LayoutLMForQuestionAnswering,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMModel,
       )






class 							__snake_case:


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase					,       __lowerCamelCase=13					,       __lowerCamelCase=7					,       __lowerCamelCase=True					,       __lowerCamelCase=True					,       __lowerCamelCase=True					,       __lowerCamelCase=True					,       __lowerCamelCase=99					,       __lowerCamelCase=32					,       __lowerCamelCase=5					,       __lowerCamelCase=4					,       __lowerCamelCase=37					,       __lowerCamelCase="gelu"					,       __lowerCamelCase=0.1					,       __lowerCamelCase=0.1					,       __lowerCamelCase=512					,       __lowerCamelCase=16					,       __lowerCamelCase=2					,       __lowerCamelCase=0.02					,       __lowerCamelCase=3					,       __lowerCamelCase=4					,       __lowerCamelCase=None					,       __lowerCamelCase=1000					,       ):




         '''simple docstring'''



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




         '''simple docstring'''



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

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

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

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

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

         __A					:					List[Any]								=		self.get_config()

         return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    def 						_a							(  self	):




         '''simple docstring'''



         return LayoutLMConfig(
             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					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		LayoutLMModel(config=lowercase__	)
         model.to(lowercase__	)
         model.eval()
         __A					:					Tuple								=		model(lowercase__					,       lowercase__					,       attention_mask=lowercase__					,       token_type_ids=lowercase__	)
         __A					:					Dict								=		model(lowercase__					,       lowercase__					,       token_type_ids=lowercase__	)
         __A					:					Any								=		model(lowercase__					,       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 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Any								=		LayoutLMForMaskedLM(config=lowercase__	)
         model.to(lowercase__	)
         model.eval()
         __A					:					int								=		model(lowercase__					,       lowercase__					,       attention_mask=lowercase__					,       token_type_ids=lowercase__					,       labels=lowercase__	)
         self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.seq_length, self.vocab_size)	)
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Optional[int]								=		self.num_labels
         __A					:					Tuple								=		LayoutLMForSequenceClassification(lowercase__	)
         model.to(lowercase__	)
         model.eval()
         __A					:					str								=		model(
             lowercase__					,       lowercase__					,       attention_mask=lowercase__					,       token_type_ids=lowercase__					,       labels=lowercase__	)
         self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.num_labels)	)
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Union[str, Any]								=		self.num_labels
         __A					:					Union[str, Any]								=		LayoutLMForTokenClassification(config=lowercase__	)
         model.to(lowercase__	)
         model.eval()
         __A					:					Any								=		model(lowercase__					,       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 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[str]								=		LayoutLMForQuestionAnswering(config=lowercase__	)
         model.to(lowercase__	)
         model.eval()
         __A					:					int								=		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 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.prepare_config_and_inputs()
         (
             __A				
         )					:					List[str]								=		config_and_inputs
         __A					:					Dict								=		{
             '''input_ids''': input_ids,
             '''bbox''': bbox,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict







@require_torch
class 							__snake_case(					a__				,	a__				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			(
        (
            LayoutLMModel,
            LayoutLMForMaskedLM,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    _UpperCAmelCase       =			(
        {
            """document-question-answering""": LayoutLMForQuestionAnswering,
            """feature-extraction""": LayoutLMModel,
            """fill-mask""": LayoutLMForMaskedLM,
            """text-classification""": LayoutLMForSequenceClassification,
            """token-classification""": LayoutLMForTokenClassification,
            """zero-shot""": LayoutLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _UpperCAmelCase       =			True
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		LayoutLMModelTester(self	)
         __A					:					Union[str, Any]								=		ConfigTester(self					,       config_class=lowercase__					,       hidden_size=37	)
    def 						_a							(  self	):




         '''simple docstring'''



         self.config_tester.run_common_tests()
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase__	)
    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



         __A					:					int								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowercase__	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowercase__	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowercase__	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowercase__	)

    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
    def 						_a							(  self	):




         '''simple docstring'''



         pass







def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					str								=		torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]  ,       device=lowerCAmelCase_						)  # noqa: E231
     __A					:					str								=		torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]  ,       device=lowerCAmelCase_						)  # noqa: E231
     __A					:					Any								=		torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]  ,       device=lowerCAmelCase_						)  # noqa: E231
     __A					:					Optional[int]								=		torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]  ,       device=lowerCAmelCase_						)  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     __A					:					List[Any]								=		torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]  ,       device=lowerCAmelCase_						)  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class 							__snake_case(					unittest.TestCase	):


    '''simple docstring'''





    @slow
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					str								=		LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'	).to(lowercase__	)

         __A					:					Optional[Any]								=		prepare_layoutlm_batch_inputs()

         # forward pass
         __A					:					Any								=		model(input_ids=lowercase__					,       bbox=lowercase__					,       attention_mask=lowercase__					,       token_type_ids=lowercase__	)

         # test the sequence output on [0, :3, :3]
         __A					:					List[str]								=		torch.tensor(
             [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]					,       device=lowercase__					,       )

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

         # test the pooled output on [1, :3]
         __A					:					Optional[Any]								=		torch.tensor([-0.65_80, -0.02_14, 0.85_52]					,       device=lowercase__	)

         self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]					,       lowercase__					,       atol=1e-3	)	)
    @slow
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'					,       num_labels=2	).to(
             lowercase__	)

         __A					:					Optional[int]								=		prepare_layoutlm_batch_inputs()

         # forward pass
         __A					:					Optional[Any]								=		model(
             input_ids=lowercase__					,       bbox=lowercase__					,       attention_mask=lowercase__					,       token_type_ids=lowercase__					,       labels=torch.tensor([1, 1]					,       device=lowercase__	)					,       )

         # test whether we get a loss as a scalar
         __A					:					int								=		outputs.loss
         __A					:					Tuple								=		torch.Size([]	)
         self.assertEqual(loss.shape					,       lowercase__	)

         # test the shape of the logits
         __A					:					Optional[int]								=		outputs.logits
         __A					:					Optional[Any]								=		torch.Size((2, 2)	)
         self.assertEqual(logits.shape					,       lowercase__	)
    @slow
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'					,       num_labels=13	).to(
             lowercase__	)

         __A					:					Optional[int]								=		prepare_layoutlm_batch_inputs()

         # forward pass
         __A					:					int								=		model(
             input_ids=lowercase__					,       bbox=lowercase__					,       attention_mask=lowercase__					,       token_type_ids=lowercase__					,       labels=lowercase__	)

         # test the loss calculation to be around 2.65
         # expected_loss = torch.tensor(2.65, device=torch_device)

         # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
         # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

         # test the shape of the logits
         __A					:					List[str]								=		outputs.logits
         __A					:					str								=		torch.Size((2, 25, 13)	)
         self.assertEqual(logits.shape					,       lowercase__	)

    @slow
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					List[Any]								=		LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'	).to(lowercase__	)

         __A					:					Optional[int]								=		prepare_layoutlm_batch_inputs()

         # forward pass
         __A					:					Optional[int]								=		model(input_ids=lowercase__					,       bbox=lowercase__					,       attention_mask=lowercase__					,       token_type_ids=lowercase__	)

         # test the shape of the logits
         __A					:					Optional[int]								=		torch.Size((2, 25)	)
         self.assertEqual(outputs.start_logits.shape					,       lowercase__	)
         self.assertEqual(outputs.end_logits.shape					,       lowercase__	)










'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase       :		Dict		=				{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Union[str, Any]		=				[
       'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'HubertForCTC',
       'HubertForSequenceClassification',
       'HubertModel',
       'HubertPreTrainedModel',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Optional[int]		=				[
       'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFHubertForCTC',
       'TFHubertModel',
       'TFHubertPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_hubert import (
          HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          HubertForCTC,
          HubertForSequenceClassification,
          HubertModel,
          HubertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_hubert import (
          TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFHubertForCTC,
          TFHubertModel,
          TFHubertPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase       :		Optional[int]		=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


