
'''simple docstring'''





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     a :




				def __init__(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Any=13,     SCREAMING_SNAKE_CASE_						:					Any=7,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=True,     SCREAMING_SNAKE_CASE_						:					List[str]=True,     SCREAMING_SNAKE_CASE_						:					Optional[int]=True,     SCREAMING_SNAKE_CASE_						:					List[Any]=True,     SCREAMING_SNAKE_CASE_						:					str=99,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=32,     SCREAMING_SNAKE_CASE_						:					Optional[int]=5,     SCREAMING_SNAKE_CASE_						:					str=4,     SCREAMING_SNAKE_CASE_						:					Dict=37,     SCREAMING_SNAKE_CASE_						:					Optional[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					List[str]=0.1,     SCREAMING_SNAKE_CASE_						:					Tuple=0.1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					Dict=16,     SCREAMING_SNAKE_CASE_						:					Any=2,     SCREAMING_SNAKE_CASE_						:					Tuple=0.02,     SCREAMING_SNAKE_CASE_						:					int=3,     SCREAMING_SNAKE_CASE_						:					Dict=None,     SCREAMING_SNAKE_CASE_						:					List[str]=20,     SCREAMING_SNAKE_CASE_						:					List[Any]=30,     SCREAMING_SNAKE_CASE_						:					Optional[int]=2,     SCREAMING_SNAKE_CASE_						:					Any=2,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=10,     ):
										snake_case							:  Union[str, Any]			=     parent
										snake_case							:  List[str]			=     batch_size
										snake_case							:  List[str]			=     seq_length
										snake_case							:  Optional[int]			=     is_training
										snake_case							:  Optional[Any]			=     use_input_mask
										snake_case							:  List[Any]			=     use_token_type_ids
										snake_case							:  int			=     use_labels
										snake_case							:  List[Any]			=     vocab_size
										snake_case							:  Tuple			=     hidden_size
										snake_case							:  List[str]			=     num_hidden_layers
										snake_case							:  List[Any]			=     num_attention_heads
										snake_case							:  int			=     intermediate_size
										snake_case							:  List[str]			=     hidden_act
										snake_case							:  int			=     hidden_dropout_prob
										snake_case							:  Dict			=     attention_probs_dropout_prob
										snake_case							:  Tuple			=     max_position_embeddings
										snake_case							:  List[str]			=     type_vocab_size
										snake_case							:  str			=     type_sequence_label_size
										snake_case							:  Optional[Any]			=     initializer_range
										snake_case							:  List[str]			=     num_labels
										snake_case							:  Union[str, Any]			=     scope
										snake_case							:  int			=     max_xpath_tag_unit_embeddings
										snake_case							:  Optional[Any]			=     max_xpath_subs_unit_embeddings
										snake_case							:  str			=     tag_pad_id
										snake_case							:  Dict			=     subs_pad_id
										snake_case							:  List[Any]			=     max_depth




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  Tuple			=     ids_tensor([self.batch_size, self.seq_length],     self.vocab_size   )

										snake_case							:  Tuple			=     ids_tensor(
										    [self.batch_size, self.seq_length, self.max_depth],     self.max_xpath_tag_unit_embeddings   )

										snake_case							:  Tuple			=     ids_tensor(
										    [self.batch_size, self.seq_length, self.max_depth],     self.max_xpath_subs_unit_embeddings   )

										snake_case							:  Dict			=     None
										if self.use_input_mask:
																snake_case							:  List[str]			=     ids_tensor([self.batch_size, self.seq_length],     vocab_size=2   )

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

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

										snake_case							:  Union[str, Any]			=     self.get_config()

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




				def 						__snake_case				(				self						:					Tuple   ):
										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 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  Optional[Any]			=     MarkupLMModel(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										print('''Configs:''',     model.config.tag_pad_id,     model.config.subs_pad_id   )
										snake_case							:  Optional[Any]			=     model(SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     model(SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Dict			=     model(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 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     ):
										snake_case							:  int			=     self.num_labels
										snake_case							:  Union[str, Any]			=     MarkupLMForSequenceClassification(SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  str			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=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.num_labels)   )




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  Any			=     self.num_labels
										snake_case							:  List[str]			=     MarkupLMForTokenClassification(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  Dict			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=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 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  List[Any]			=     MarkupLMForQuestionAnswering(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  Union[str, Any]			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=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 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  int			=     self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       
										)							:  Any			=     config_and_inputs
										snake_case							:  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     a (					__magic_name__					,__magic_name__					,unittest.TestCase     ):
				_snake_case		     =			(
				    (
				        MarkupLMModel,
				        MarkupLMForSequenceClassification,
				        MarkupLMForTokenClassification,
				        MarkupLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				_snake_case		     =			(
				    {
				        '''feature-extraction''': MarkupLMModel,
				        '''question-answering''': MarkupLMForQuestionAnswering,
				        '''text-classification''': MarkupLMForSequenceClassification,
				        '''token-classification''': MarkupLMForTokenClassification,
				        '''zero-shot''': MarkupLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					List[Any]   ):
										# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
										# (batch of pretokenized examples).
										return True




				def 						__snake_case				(				self						:					str   ):
										snake_case							:  Tuple			=     MarkupLMModelTester(self   )
										snake_case							:  List[Any]			=     ConfigTester(self,     config_class=SCREAMING_SNAKE_CASE_,     hidden_size=37   )




				def 						__snake_case				(				self						:					Any   ):
										self.config_tester.run_common_tests()




				def 						__snake_case				(				self						:					Optional[int]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  List[Any]			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_   )




def   A			(							):
						snake_case							:  Optional[int]			=     '''
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    '''

						return html_string





@require_torch
class     a (					unittest.TestCase     ):




				@cached_property
				def 						__snake_case				(				self						:					Tuple   ):
										# TODO use from_pretrained here
										snake_case							:  Optional[int]			=     MarkupLMFeatureExtractor()
										snake_case							:  Tuple			=     MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''   )

										return MarkupLMProcessor(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )




				@slow
				def 						__snake_case				(				self						:					Dict   ):
										snake_case							:  Dict			=     MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''   ).to(SCREAMING_SNAKE_CASE_   )

										snake_case							:  List[str]			=     self.default_processor

										snake_case							:  str			=     processor(prepare_html_string(),     return_tensors='''pt'''   )
										snake_case							:  List[Any]			=     inputs.to(SCREAMING_SNAKE_CASE_   )

										# forward pass
										with torch.no_grad():
																snake_case							:  Any			=     model(**SCREAMING_SNAKE_CASE_   )

										# verify the last hidden states
										snake_case							:  Tuple			=     torch.Size([1, 14, 7_68]   )
										self.assertEqual(outputs.last_hidden_state.shape,     SCREAMING_SNAKE_CASE_   )

										snake_case							:  Union[str, Any]			=     torch.tensor(
										    [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]   ).to(SCREAMING_SNAKE_CASE_   )

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





import unittest

from transformers import ErnieMConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     ErnieMForInformationExtraction,
     ErnieMForMultipleChoice,
     ErnieMForQuestionAnswering,
     ErnieMForSequenceClassification,
     ErnieMForTokenClassification,
     ErnieMModel,
 )
 from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST





class     __a     :
 def __init__(  self							,					lowerCAmelCase__							,					lowerCAmelCase__=13							,					lowerCAmelCase__=7							,					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__=0.0_2							,					lowerCAmelCase__=3							,					lowerCAmelCase__=4							,					lowerCAmelCase__=None							,					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Tuple         =  parent
        lowercase__:  Optional[int]         =  batch_size
        lowercase__:  Optional[Any]         =  seq_length
        lowercase__:  Dict         =  is_training
        lowercase__:  List[str]         =  use_input_mask
        lowercase__:  Tuple         =  use_labels
        lowercase__:  List[str]         =  vocab_size
        lowercase__:  Any         =  hidden_size
        lowercase__:  Optional[int]         =  num_hidden_layers
        lowercase__:  str         =  num_attention_heads
        lowercase__:  Any         =  intermediate_size
        lowercase__:  List[str]         =  hidden_act
        lowercase__:  str         =  hidden_dropout_prob
        lowercase__:  Optional[Any]         =  attention_probs_dropout_prob
        lowercase__:  Optional[Any]         =  max_position_embeddings
        lowercase__:  List[str]         =  type_vocab_size
        lowercase__:  List[Any]         =  type_sequence_label_size
        lowercase__:  Any         =  initializer_range
        lowercase__:  str         =  num_labels
        lowercase__:  Tuple         =  num_choices
        lowercase__:  str         =  scope
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  int         =  ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size					)

        lowercase__:  str         =  None
        if self.use_input_mask:
               lowercase__:  Dict         =  random_attention_mask([self.batch_size, self.seq_length]					)

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

        lowercase__:  int         =  self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        lowercase__:  Tuple         =  ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size					)

        lowercase__:  List[str]         =  None
        if self.use_input_mask:
               lowercase__:  Union[str, Any]         =  random_attention_mask([self.batch_size, self.seq_length]					)
        lowercase__:  Any         =  self.get_config()

        return config, input_ids, input_mask
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        return ErnieMConfig(
            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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							str:




        '''simple docstring'''
        lowercase__:  str         =  ErnieMModel(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  List[Any]         =  model(lowerCAmelCase__							,					return_dict=lowerCAmelCase__					)
        self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ErnieMForQuestionAnswering(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  List[str]         =  model(
            lowerCAmelCase__							,					attention_mask=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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Dict         =  ErnieMForInformationExtraction(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  Any         =  torch.ones_like(lowerCAmelCase__							,					dtype=torch.floataa					)
        lowercase__:  Tuple         =  model(
            lowerCAmelCase__							,					attention_mask=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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  Tuple         =  self.num_labels
        lowercase__:  str         =  ErnieMForSequenceClassification(lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  Optional[int]         =  model(lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					labels=lowerCAmelCase__					)
        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  self.num_labels
        lowercase__:  Dict         =  ErnieMForTokenClassification(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        input_ids.to(lowerCAmelCase__					)
        input_mask.to(lowerCAmelCase__					)
        token_labels.to(lowerCAmelCase__					)

        lowercase__:  Optional[Any]         =  model(lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					labels=lowerCAmelCase__					)

        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels)					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  Dict         =  self.num_choices
        lowercase__:  Any         =  ErnieMForMultipleChoice(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  Optional[int]         =  input_ids.unsqueeze(1					).expand(-1							,					self.num_choices							,					-1					).contiguous()
        lowercase__:  Any         =  input_mask.unsqueeze(1					).expand(-1							,					self.num_choices							,					-1					).contiguous()
        lowercase__:  Union[str, Any]         =  model(
            lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					labels=lowerCAmelCase__							,					)
        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_choices)					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  int         =  self.prepare_config_and_inputs()
        (
            (
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		
        ):  Optional[Any]         =  config_and_inputs
        lowercase__:  int         =  {'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict





@require_torch
class     __a     (       __UpperCamelCase			,			__UpperCamelCase			,			unittest.TestCase ):
 __lowercase					:				List[str]  	=					(
     (
         ErnieMModel,
         ErnieMForMultipleChoice,
         ErnieMForQuestionAnswering,
         ErnieMForSequenceClassification,
         ErnieMForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowercase					:				List[str]  	=					()
 __lowercase					:				Optional[int]  	=					(
     {
         'feature-extraction': ErnieMModel,
         'question-answering': ErnieMForQuestionAnswering,
         'text-classification': ErnieMForSequenceClassification,
         'token-classification': ErnieMForTokenClassification,
         'zero-shot': ErnieMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowercase					:				Optional[Any]  	=					False
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							int:




        '''simple docstring'''
        if pipeline_test_casse_name == "QAPipelineTests":
               return True

        return False
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Any         =  ErnieMModelTester(self					)
        lowercase__:  List[str]         =  ConfigTester(self							,					config_class=lowerCAmelCase__							,					hidden_size=37					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        self.config_tester.run_common_tests()
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  int         =  self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
               lowercase__:  str         =  type
               self.model_tester.create_and_check_model(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        lowercase__:  Tuple         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Any         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_information_extraction(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__					)
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Any:




        '''simple docstring'''
        for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__:  Tuple         =  ErnieMModel.from_pretrained(lowerCAmelCase__					)
               self.assertIsNotNone(lowerCAmelCase__					)





@require_torch
class     __a     (       unittest.TestCase ):
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ErnieMModel.from_pretrained('susnato/ernie-m-base_pytorch'					)
        model.eval()
        lowercase__:  Union[str, Any]         =  torch.tensor([[0, 1, 2, 3, 4, 5]]					)
        lowercase__:  str         =  model(lowerCAmelCase__					)[0]

        # TODO Replace vocab size
        lowercase__:  Union[str, Any]         =  768

        lowercase__:  Any         =  torch.Size((1, 6, hidden_size)					)
        self.assertEqual(output.shape							,					lowerCAmelCase__					)

        lowercase__:  str         =  torch.tensor(
            [[[-0.0_0_1_2, 0.1_2_4_5, -0.0_2_1_4], [-0.0_7_4_2, 0.0_2_4_4, -0.0_7_7_1], [-0.0_3_3_3, 0.1_1_6_4, -0.1_5_5_4]]]					)

        self.assertTrue(torch.allclose(output[:, :3, :3]							,					lowerCAmelCase__							,					atol=1E-3					)					)


