



'''simple docstring'''







import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


SCREAMING_SNAKE_CASE__     : List[str]				=   namedtuple("""ExampleNamedTuple""", """a b c""")




class 				__lowerCAmelCase(							unittest.TestCase      ):






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.randn(5      ,       2	)
					SCREAMING_SNAKE_CASE_						:int										=      torch.device('cuda'	) if torch.cuda.is_available() else torch.device('cpu'	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      send_to_device(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					self.assertTrue(torch.equal(resulta.cpu()      ,       SCREAMING_SNAKE_CASE	)	)

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      send_to_device((tensor, [tensor, tensor], 1)      ,       SCREAMING_SNAKE_CASE	)
					self.assertIsInstance(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					self.assertTrue(torch.equal(resulta[0].cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertIsInstance(resulta[1]      ,       SCREAMING_SNAKE_CASE	)
					self.assertTrue(torch.equal(resulta[1][0].cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertTrue(torch.equal(resulta[1][1].cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertEqual(resulta[2]      ,       1	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}      ,       SCREAMING_SNAKE_CASE	)
					self.assertIsInstance(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					self.assertTrue(torch.equal(resulta['a'].cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertIsInstance(resulta['b']      ,       SCREAMING_SNAKE_CASE	)
					self.assertTrue(torch.equal(resulta['b'][0].cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertTrue(torch.equal(resulta['b'][1].cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertEqual(resulta['c']      ,       1	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      send_to_device(ExampleNamedTuple(a=SCREAMING_SNAKE_CASE      ,       b=[tensor, tensor]      ,       c=1	)      ,       SCREAMING_SNAKE_CASE	)
					self.assertIsInstance(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					self.assertTrue(torch.equal(resulta.a.cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertIsInstance(resulta.b      ,       SCREAMING_SNAKE_CASE	)
					self.assertTrue(torch.equal(resulta.b[0].cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertTrue(torch.equal(resulta.b[1].cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertEqual(resulta.c      ,       1	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}	)      ,       SCREAMING_SNAKE_CASE	)
					self.assertIsInstance(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					self.assertTrue(torch.equal(resulta['a'].cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertIsInstance(resulta['b']      ,       SCREAMING_SNAKE_CASE	)
					self.assertTrue(torch.equal(resulta['b'][0].cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertTrue(torch.equal(resulta['b'][1].cpu()      ,       SCREAMING_SNAKE_CASE	)	)
					self.assertEqual(resulta['c']      ,       1	)






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE	) as cm:
							SCREAMING_SNAKE_CASE_						:Tuple										=      recursively_apply(torch.tensor      ,       (torch.tensor(1	), 1)      ,       error_on_other_type=SCREAMING_SNAKE_CASE	)
					self.assertEqual(
					    str(cm.exception	)      ,       'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'      ,       )






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      torch.tensor([1, 2, 3, 4, 5]	)
					self.assertEqual(listify(SCREAMING_SNAKE_CASE	)      ,       [1, 2, 3, 4, 5]	)

					SCREAMING_SNAKE_CASE_						:Any										=      torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]	)
					self.assertEqual(listify(SCREAMING_SNAKE_CASE	)      ,       [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]	)
					self.assertEqual(
					    listify(SCREAMING_SNAKE_CASE	)      ,       [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					with patch_environment(aa=1      ,       BB=2	):
							self.assertEqual(os.environ.get('AA'	)      ,       '1'	)
							self.assertEqual(os.environ.get('BB'	)      ,       '2'	)

					self.assertNotIn('AA'      ,       os.environ	)
					self.assertNotIn('BB'      ,       os.environ	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      RegressionModel()
					SCREAMING_SNAKE_CASE_						:Tuple										=      model.forward
					SCREAMING_SNAKE_CASE_						:Any										=      convert_outputs_to_fpaa(model.forward	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      extract_model_from_parallel(SCREAMING_SNAKE_CASE      ,       keep_fpaa_wrapper=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      pickle.dumps(SCREAMING_SNAKE_CASE	)






			@require_cuda
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      RegressionModel()
					SCREAMING_SNAKE_CASE_						:Tuple										=      model.forward
					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.cuda.amp.autocast(dtype=torch.floataa	)(model.forward	)
					SCREAMING_SNAKE_CASE_						:Any										=      convert_outputs_to_fpaa(model.forward	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      extract_model_from_parallel(SCREAMING_SNAKE_CASE      ,       keep_fpaa_wrapper=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      pickle.dumps(SCREAMING_SNAKE_CASE	)






			@require_cuda
			@require_torch_min_version(version='2.0'	)
			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      RegressionModel()
					SCREAMING_SNAKE_CASE_						:Tuple										=      model.forward
					SCREAMING_SNAKE_CASE_						:int										=      torch.cuda.amp.autocast(dtype=torch.floataa	)(model.forward	)
					SCREAMING_SNAKE_CASE_						:int										=      convert_outputs_to_fpaa(model.forward	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      torch.compile(model.forward      ,       backend='inductor'	)
					SCREAMING_SNAKE_CASE_						:str										=      torch.randn(4      ,       10	).cuda()
					SCREAMING_SNAKE_CASE_						:int										=      model(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      RegressionModel()
					# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:int										=      extract_model_from_parallel(SCREAMING_SNAKE_CASE	)

					self.assertEqual(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)






			@require_torch_min_version(version='2.0'	)
			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      RegressionModel()
					SCREAMING_SNAKE_CASE_						:int										=      torch.compile(SCREAMING_SNAKE_CASE	)

					# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.compile(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      extract_model_from_parallel(SCREAMING_SNAKE_CASE	)

					self.assertEqual(compiled_model._orig_mod      ,       compiled_model_unwrapped._orig_mod	)






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]	)]	)      ,       torch.device('cpu'	)	)
					self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]	)}	)      ,       torch.device('cpu'	)	)
					self.assertIsNone(find_device([1, 'a']	)	)




import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

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 (
        MarkupLMForQuestionAnswering,
        MarkupLMForSequenceClassification,
        MarkupLMForTokenClassification,
        MarkupLMModel,
    )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer







class      _lowercase					:


       """simple docstring"""




       def __init__(  self				:     Dict   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Union[str, Any]=13   , __lowerCamelCase				:     Optional[Any]=7   , __lowerCamelCase				:     List[str]=True   , __lowerCamelCase				:     str=True   , __lowerCamelCase				:     List[str]=True   , __lowerCamelCase				:     List[str]=True   , __lowerCamelCase				:     Optional[int]=99   , __lowerCamelCase				:     Dict=32   , __lowerCamelCase				:     List[str]=5   , __lowerCamelCase				:     str=4   , __lowerCamelCase				:     str=37   , __lowerCamelCase				:     Any="gelu"   , __lowerCamelCase				:     Dict=0.1   , __lowerCamelCase				:     Optional[Any]=0.1   , __lowerCamelCase				:     List[str]=512   , __lowerCamelCase				:     str=16   , __lowerCamelCase				:     List[Any]=2   , __lowerCamelCase				:     int=0.0_2   , __lowerCamelCase				:     Any=3   , __lowerCamelCase				:     Optional[int]=None   , __lowerCamelCase				:     str=20   , __lowerCamelCase				:     Union[str, Any]=30   , __lowerCamelCase				:     str=2   , __lowerCamelCase				:     Dict=2   , __lowerCamelCase				:     str=10   , ):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    parent
             lowerCamelCase__						:							Optional[Any]       =    batch_size
             lowerCamelCase__						:							Union[str, Any]       =    seq_length
             lowerCamelCase__						:							Union[str, Any]       =    is_training
             lowerCamelCase__						:							Tuple       =    use_input_mask
             lowerCamelCase__						:							Tuple       =    use_token_type_ids
             lowerCamelCase__						:							Union[str, Any]       =    use_labels
             lowerCamelCase__						:							str       =    vocab_size
             lowerCamelCase__						:							int       =    hidden_size
             lowerCamelCase__						:							List[Any]       =    num_hidden_layers
             lowerCamelCase__						:							List[Any]       =    num_attention_heads
             lowerCamelCase__						:							Optional[Any]       =    intermediate_size
             lowerCamelCase__						:							int       =    hidden_act
             lowerCamelCase__						:							Optional[Any]       =    hidden_dropout_prob
             lowerCamelCase__						:							Optional[Any]       =    attention_probs_dropout_prob
             lowerCamelCase__						:							Optional[int]       =    max_position_embeddings
             lowerCamelCase__						:							Optional[Any]       =    type_vocab_size
             lowerCamelCase__						:							Tuple       =    type_sequence_label_size
             lowerCamelCase__						:							List[Any]       =    initializer_range
             lowerCamelCase__						:							str       =    num_labels
             lowerCamelCase__						:							Dict       =    scope
             lowerCamelCase__						:							List[Any]       =    max_xpath_tag_unit_embeddings
             lowerCamelCase__						:							str       =    max_xpath_subs_unit_embeddings
             lowerCamelCase__						:							Optional[Any]       =    tag_pad_id
             lowerCamelCase__						:							Optional[Any]       =    subs_pad_id
             lowerCamelCase__						:							Optional[int]       =    max_depth





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size			)

             lowerCamelCase__						:							List[Any]       =    ids_tensor(
                 [self.batch_size, self.seq_length, self.max_depth]   , self.max_xpath_tag_unit_embeddings			)

             lowerCamelCase__						:							int       =    ids_tensor(
                 [self.batch_size, self.seq_length, self.max_depth]   , self.max_xpath_subs_unit_embeddings			)

             lowerCamelCase__						:							List[Any]       =    None
             if self.use_input_mask:
                   lowerCamelCase__						:							Tuple       =    ids_tensor([self.batch_size, self.seq_length]   , vocab_size=2			)

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

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

             lowerCamelCase__						:							int       =    self.get_config()

             return (
                 config,
                 input_ids,
                 xpath_tags_seq,
                 xpath_subs_seq,
                 token_type_ids,
                 input_mask,
                 sequence_labels,
                 token_labels,
             )





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             return MarkupLMConfig(
                 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   , max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings   , max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings   , tag_pad_id=self.tag_pad_id   , subs_pad_id=self.subs_pad_id   , max_depth=self.max_depth   , )





       def 		lowerCAmelCase	(  self				:     Optional[Any]   , __lowerCamelCase				:     Union[str, Any]   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     str   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     str   , ):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    MarkupLMModel(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             print("Configs:"   , model.config.tag_pad_id   , model.config.subs_pad_id			)
             lowerCamelCase__						:							Optional[int]       =    model(__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase			)
             lowerCamelCase__						:							List[str]       =    model(__lowerCamelCase   , token_type_ids=__lowerCamelCase			)
             lowerCamelCase__						:							str       =    model(__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 		lowerCAmelCase	(  self				:     Dict   , __lowerCamelCase				:     Union[str, Any]   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     int   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     List[Any]   , ):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    self.num_labels
             lowerCamelCase__						:							str       =    MarkupLMForSequenceClassification(__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Optional[int]       =    model(
                 __lowerCamelCase   , xpath_tags_seq=__lowerCamelCase   , xpath_subs_seq=__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase   , labels=__lowerCamelCase   , )
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)			)





       def 		lowerCAmelCase	(  self				:     Any   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     int   , __lowerCamelCase				:     str   , ):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    self.num_labels
             lowerCamelCase__						:							Tuple       =    MarkupLMForTokenClassification(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							List[Any]       =    model(
                 __lowerCamelCase   , xpath_tags_seq=__lowerCamelCase   , xpath_subs_seq=__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 		lowerCAmelCase	(  self				:     Tuple   , __lowerCamelCase				:     int   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Any   , ):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    MarkupLMForQuestionAnswering(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Union[str, Any]       =    model(
                 __lowerCamelCase   , xpath_tags_seq=__lowerCamelCase   , xpath_subs_seq=__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 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    self.prepare_config_and_inputs()
             (
                 (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  
             )						:							Optional[int]       =    config_and_inputs
             lowerCamelCase__						:							List[str]       =    {
                 "input_ids": input_ids,
                 "xpath_tags_seq": xpath_tags_seq,
                 "xpath_subs_seq": xpath_subs_seq,
                 "token_type_ids": token_type_ids,
                 "attention_mask": input_mask,
             }
             return config, inputs_dict







@require_torch
class      _lowercase					(				lowercase__				,    lowercase__				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     (
           (
               MarkupLMModel,
               MarkupLMForSequenceClassification,
               MarkupLMForTokenClassification,
               MarkupLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       A__   		=     (
           {
               "feature-extraction": MarkupLMModel,
               "question-answering": MarkupLMForQuestionAnswering,
               "text-classification": MarkupLMForSequenceClassification,
               "token-classification": MarkupLMForTokenClassification,
               "zero-shot": MarkupLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )





       def 		lowerCAmelCase	(  self				:     int   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     int   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     List[str]			):


             '''simple docstring'''



             return True





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    MarkupLMModelTester(self			)
             lowerCamelCase__						:							Any       =    ConfigTester(self   , config_class=__lowerCamelCase   , hidden_size=37			)





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase			)



def  lowercase_						(						):

      """simple docstring"""




      lowerCamelCase__						:							int       =    "\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    "

      return html_string







@require_torch
class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       @cached_property
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    MarkupLMFeatureExtractor()
             lowerCamelCase__						:							Union[str, Any]       =    MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"			)

             return MarkupLMProcessor(__lowerCamelCase   , __lowerCamelCase			)





       @slow
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    MarkupLMModel.from_pretrained("microsoft/markuplm-base"			).to(__lowerCamelCase			)

             lowerCamelCase__						:							Dict       =    self.default_processor

             lowerCamelCase__						:							Dict       =    processor(prepare_html_string()   , return_tensors="pt"			)
             lowerCamelCase__						:							Optional[Any]       =    inputs.to(__lowerCamelCase			)

             # forward pass
             with torch.no_grad():
                   lowerCamelCase__						:							Union[str, Any]       =    model(**__lowerCamelCase			)

             # verify the last hidden states
             lowerCamelCase__						:							Dict       =    torch.Size([1, 14, 768]			)
             self.assertEqual(outputs.last_hidden_state.shape   , __lowerCamelCase			)

             lowerCamelCase__						:							Dict       =    torch.tensor(
                 [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]]			).to(__lowerCamelCase			)

             self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]   , __lowerCamelCase   , atol=1E-4			)			)


