


from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def 			a_   (					SCREAMING_SNAKE_CASE__      :   torch.Tensor	,    SCREAMING_SNAKE_CASE__      :   torch.Tensor	,    SCREAMING_SNAKE_CASE__      :   bool  ):
       '''simple docstring'''





       if not inplace:
              _lowerCamelCase :	Any						=ma + ma
       else:
              ma += ma

       return ma




def 			a_   (					SCREAMING_SNAKE_CASE__      :   torch.Tensor	,    SCREAMING_SNAKE_CASE__      :   List[int]  ):
       '''simple docstring'''





       _lowerCamelCase :	List[Any]						=-1 * len(SCREAMING_SNAKE_CASE__  )
       _lowerCamelCase :	Optional[int]						=list(range(len(tensor.shape[:zero_index]  )  )  )
       return tensor.permute(first_inds + [zero_index + i for i in inds]  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   torch.Tensor	,    SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       return t.reshape(t.shape[:-no_dims] + (-1,)  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   torch.Tensor	,    SCREAMING_SNAKE_CASE__      :   torch.Tensor	,    SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   float = 1e-4  ):
       '''simple docstring'''





       _lowerCamelCase :	Tuple						=mask.expand(*value.shape  )
       return torch.sum(mask * value	,    dim=SCREAMING_SNAKE_CASE__  ) / (eps + torch.sum(SCREAMING_SNAKE_CASE__	,    dim=SCREAMING_SNAKE_CASE__  ))




def 			a_   (					SCREAMING_SNAKE_CASE__      :   torch.Tensor	,    SCREAMING_SNAKE_CASE__      :   torch.types.Number = 2.31_25	,    SCREAMING_SNAKE_CASE__      :   torch.types.Number = 21.68_75	,    SCREAMING_SNAKE_CASE__      :   int = 64  ):
       '''simple docstring'''





       _lowerCamelCase :	Tuple						=torch.linspace(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    no_bins - 1	,    device=pts.device  )
       _lowerCamelCase :	Union[str, Any]						=torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2	,    dim=-1  )  )
       return torch.bucketize(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Callable[[list], Any]	,    SCREAMING_SNAKE_CASE__      :   List[dict]  ):
       '''simple docstring'''





       _lowerCamelCase :	Optional[Any]						=dicts[0]
       _lowerCamelCase :	List[str]						={}
       for k, v in first.items():
              _lowerCamelCase :	Dict						=[d[k] for d in dicts]
              if isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ):
                     _lowerCamelCase :	int						=dict_multimap(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )
              else:
                     _lowerCamelCase :	Dict						=fn(SCREAMING_SNAKE_CASE__  )

       return new_dict




def 			a_   (					SCREAMING_SNAKE_CASE__      :   torch.Tensor	,    SCREAMING_SNAKE_CASE__      :   torch.Tensor  ):
       '''simple docstring'''





       _lowerCamelCase :	Optional[Any]						=v_bins.view(((1,) * len(x.shape  )) + (len(SCREAMING_SNAKE_CASE__  ),)  )
       _lowerCamelCase :	Dict						=x[..., None] - reshaped_bins
       _lowerCamelCase :	Union[str, Any]						=torch.argmin(torch.abs(SCREAMING_SNAKE_CASE__  )	,    dim=-1  )
       return nn.functional.one_hot(SCREAMING_SNAKE_CASE__	,    num_classes=len(SCREAMING_SNAKE_CASE__  )  ).float()




def 			a_   (					SCREAMING_SNAKE_CASE__      :   torch.Tensor	,    SCREAMING_SNAKE_CASE__      :   torch.Tensor	,    SCREAMING_SNAKE_CASE__      :   int = 0	,    SCREAMING_SNAKE_CASE__      :   int = 0  ):
       '''simple docstring'''





       _lowerCamelCase :	List[Union[slice, torch.Tensor]]						=[]
       for i, s in enumerate(data.shape[:no_batch_dims]  ):
              _lowerCamelCase :	List[str]						=torch.arange(SCREAMING_SNAKE_CASE__  )
              _lowerCamelCase :	str						=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape  ) - i - 1)))  )
              ranges.append(SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	List[Union[slice, torch.Tensor]]						=[slice(SCREAMING_SNAKE_CASE__  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
       _lowerCamelCase :	List[Any]						=inds
       ranges.extend(SCREAMING_SNAKE_CASE__  )
       # Matt note: Editing this to get around the behaviour of using a list as an array index changing
       # in recent Numpy versions
       return data[tuple(SCREAMING_SNAKE_CASE__  )]


lowerCamelCase		      = TypeVar('T')




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Callable[[T], Any]	,    SCREAMING_SNAKE_CASE__      :   Dict[Any, Union[dict, list, tuple, T]]	,    SCREAMING_SNAKE_CASE__      :   Type[T]  ):
       '''simple docstring'''





       _lowerCamelCase :	Dict[Any, Union[dict, list, tuple, Any]]						={}
       for k, v in dic.items():
              if isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ):
                     _lowerCamelCase :	Any						=dict_map(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )
              else:
                     _lowerCamelCase :	Any						=tree_map(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )

       return new_dict




@overload
def 			a_   (					SCREAMING_SNAKE_CASE__      :   Callable[[T], Any]	,    SCREAMING_SNAKE_CASE__      :   T	,    SCREAMING_SNAKE_CASE__      :   Type[T]  ):
       '''simple docstring'''





       ...




@overload
def 			a_   (					SCREAMING_SNAKE_CASE__      :   Callable[[T], Any]	,    SCREAMING_SNAKE_CASE__      :   dict	,    SCREAMING_SNAKE_CASE__      :   Type[T]  ):
       '''simple docstring'''





       ...




@overload
def 			a_   (					SCREAMING_SNAKE_CASE__      :   Callable[[T], Any]	,    SCREAMING_SNAKE_CASE__      :   list	,    SCREAMING_SNAKE_CASE__      :   Type[T]  ):
       '''simple docstring'''





       ...




@overload
def 			a_   (					SCREAMING_SNAKE_CASE__      :   Callable[[T], Any]	,    SCREAMING_SNAKE_CASE__      :   tuple	,    SCREAMING_SNAKE_CASE__      :   Type[T]  ):
       '''simple docstring'''





       ...




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Any	,    SCREAMING_SNAKE_CASE__      :   Dict	,    SCREAMING_SNAKE_CASE__      :   Union[str, Any]  ):
       '''simple docstring'''





       if isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ):
              return dict_map(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )
       elif isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ):
              return [tree_map(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) for x in tree]
       elif isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ):
              return tuple(tree_map(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) for x in tree  )
       elif isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ):
              return fn(SCREAMING_SNAKE_CASE__  )
       else:
              print(type(SCREAMING_SNAKE_CASE__  )  )
              raise ValueError('Not supported'  )


lowerCamelCase		      = partial(tree_map, leaf_type=torch.Tensor)






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		:  int    ,     lowercase_		:  List[Any]    ,     lowercase_		:  Dict=13    ,     lowercase_		:  Optional[Any]=7    ,     lowercase_		:  List[str]=True    ,     lowercase_		:  List[str]=True    ,     lowercase_		:  Tuple=True    ,     lowercase_		:  Union[str, Any]=99    ,     lowercase_		:  Union[str, Any]=32    ,     lowercase_		:  Tuple=5    ,     lowercase_		:  List[Any]=4    ,     lowercase_		:  Any=37    ,     lowercase_		:  Union[str, Any]="gelu"    ,     lowercase_		:  Any=0.1    ,     lowercase_		:  Union[str, Any]=0.1    ,     lowercase_		:  List[str]=512    ,     lowercase_		:  Optional[int]=16    ,     lowercase_		:  int=2    ,     lowercase_		:  Optional[Any]=0.02    ,     lowercase_		:  Dict=3    ,     lowercase_		:  Any=4    ,     lowercase_		:  List[str]=None    ,     )      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	Dict						=parent
             _lowerCamelCase :	Optional[int]						=batch_size
             _lowerCamelCase :	int						=seq_length
             _lowerCamelCase :	List[str]						=is_training
             _lowerCamelCase :	Optional[int]						=use_input_mask
             _lowerCamelCase :	Dict						=use_labels
             _lowerCamelCase :	Any						=vocab_size
             _lowerCamelCase :	Any						=hidden_size
             _lowerCamelCase :	Any						=num_hidden_layers
             _lowerCamelCase :	Optional[int]						=num_attention_heads
             _lowerCamelCase :	Optional[Any]						=intermediate_size
             _lowerCamelCase :	Union[str, Any]						=hidden_act
             _lowerCamelCase :	str						=hidden_dropout_prob
             _lowerCamelCase :	Tuple						=attention_probs_dropout_prob
             _lowerCamelCase :	Tuple						=max_position_embeddings
             _lowerCamelCase :	Optional[int]						=type_vocab_size
             _lowerCamelCase :	Optional[Any]						=type_sequence_label_size
             _lowerCamelCase :	List[str]						=initializer_range
             _lowerCamelCase :	Dict						=num_labels
             _lowerCamelCase :	List[str]						=num_choices
             _lowerCamelCase :	Optional[Any]						=scope






      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						=ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size				)

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

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

             _lowerCamelCase :	Any						=self.get_config()

             return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






      def  lowerCamelCase (      self		:  Dict				)      ->		Dict:
             """simple docstring"""




             _lowerCamelCase :	List[str]						=ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size				)

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

             return config, input_ids, input_mask






      def  lowerCamelCase (      self		:  Any				)      ->		Any:
             """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  lowerCamelCase (      self		:  Optional[int]    ,     lowercase_		:  Tuple    ,     lowercase_		:  Tuple    ,     lowercase_		:  Any    ,     lowercase_		:  Dict    ,     lowercase_		:  int    ,     lowercase_		:  Tuple				)      ->		List[str]:
             """simple docstring"""




             _lowerCamelCase :	Tuple						=ErnieMModel(config=lowercase_				)
             model.to(lowercase_				)
             model.eval()
             _lowerCamelCase :	str						=model(lowercase_    ,     return_dict=lowercase_				)
             self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)				)






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  Union[str, Any]    ,     lowercase_		:  Union[str, Any]    ,     lowercase_		:  List[str]    ,     lowercase_		:  str    ,     lowercase_		:  List[str]    ,     lowercase_		:  str				)      ->		str:
             """simple docstring"""




             _lowerCamelCase :	Any						=ErnieMForQuestionAnswering(config=lowercase_				)
             model.to(lowercase_				)
             model.eval()
             _lowerCamelCase :	List[str]						=model(
                 lowercase_    ,     attention_mask=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  lowerCamelCase (      self		:  int    ,     lowercase_		:  List[Any]    ,     lowercase_		:  int    ,     lowercase_		:  Optional[int]    ,     lowercase_		:  Optional[Any]    ,     lowercase_		:  Any    ,     lowercase_		:  Optional[int]				)      ->		Any:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						=ErnieMForInformationExtraction(config=lowercase_				)
             model.to(lowercase_				)
             model.eval()
             _lowerCamelCase :	Optional[int]						=torch.ones_like(lowercase_    ,     dtype=torch.floataa				)
             _lowerCamelCase :	str						=model(
                 lowercase_    ,     attention_mask=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  lowerCamelCase (      self		:  Dict    ,     lowercase_		:  Dict    ,     lowercase_		:  Tuple    ,     lowercase_		:  List[str]    ,     lowercase_		:  str    ,     lowercase_		:  Any    ,     lowercase_		:  Tuple				)      ->		int:
             """simple docstring"""




             _lowerCamelCase :	Dict						=self.num_labels
             _lowerCamelCase :	Any						=ErnieMForSequenceClassification(lowercase_				)
             model.to(lowercase_				)
             model.eval()
             _lowerCamelCase :	Optional[int]						=model(lowercase_    ,     attention_mask=lowercase_    ,     labels=lowercase_				)
             self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)				)






      def  lowerCamelCase (      self		:  Dict    ,     lowercase_		:  List[str]    ,     lowercase_		:  Optional[Any]    ,     lowercase_		:  Optional[int]    ,     lowercase_		:  List[str]    ,     lowercase_		:  Optional[int]    ,     lowercase_		:  Dict				)      ->		int:
             """simple docstring"""




             _lowerCamelCase :	int						=self.num_labels
             _lowerCamelCase :	List[str]						=ErnieMForTokenClassification(config=lowercase_				)
             model.to(lowercase_				)
             model.eval()
             input_ids.to(lowercase_				)
             input_mask.to(lowercase_				)
             token_labels.to(lowercase_				)

             _lowerCamelCase :	List[Any]						=model(lowercase_    ,     attention_mask=lowercase_    ,     labels=lowercase_				)

             self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels)				)






      def  lowerCamelCase (      self		:  Union[str, Any]    ,     lowercase_		:  int    ,     lowercase_		:  Union[str, Any]    ,     lowercase_		:  int    ,     lowercase_		:  str    ,     lowercase_		:  Optional[int]    ,     lowercase_		:  str				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	Any						=self.num_choices
             _lowerCamelCase :	Any						=ErnieMForMultipleChoice(config=lowercase_				)
             model.to(lowercase_				)
             model.eval()
             _lowerCamelCase :	str						=input_ids.unsqueeze(1				).expand(-1    ,     self.num_choices    ,     -1				).contiguous()
             _lowerCamelCase :	Optional[int]						=input_mask.unsqueeze(1				).expand(-1    ,     self.num_choices    ,     -1				).contiguous()
             _lowerCamelCase :	List[str]						=model(
                 lowercase_    ,     attention_mask=lowercase_    ,     labels=lowercase_    ,     )
             self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_choices)				)






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		List[str]:
             """simple docstring"""




             _lowerCamelCase :	Tuple						=self.prepare_config_and_inputs()
             (
                 (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  
             ) :	List[Any]						=config_and_inputs
             _lowerCamelCase :	Optional[Any]						={'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict




@require_torch
class A						(     UpperCamelCase_					,							UpperCamelCase_					,							unittest.TestCase  ):
      UpperCamelCase__							:						str   =(
          (
              ErnieMModel,
              ErnieMForMultipleChoice,
              ErnieMForQuestionAnswering,
              ErnieMForSequenceClassification,
              ErnieMForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase__							:						Dict   =()
      UpperCamelCase__							:						List[str]   =(
          {
              'feature-extraction': ErnieMModel,
              'question-answering': ErnieMForQuestionAnswering,
              'text-classification': ErnieMForSequenceClassification,
              'token-classification': ErnieMForTokenClassification,
              'zero-shot': ErnieMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      UpperCamelCase__							:						List[str]   =False






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  List[Any]    ,     lowercase_		:  Any    ,     lowercase_		:  str    ,     lowercase_		:  Tuple    ,     lowercase_		:  Tuple				)      ->		Dict:
             """simple docstring"""




             if pipeline_test_casse_name == "QAPipelineTests":
                    return True

             return False






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		int:
             """simple docstring"""




             _lowerCamelCase :	int						=ErnieMModelTester(self				)
             _lowerCamelCase :	Optional[int]						=ConfigTester(self    ,     config_class=lowercase_    ,     hidden_size=37				)






      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		Union[str, Any]:
             """simple docstring"""




             self.config_tester.run_common_tests()






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		Dict:
             """simple docstring"""




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






      def  lowerCamelCase (      self		:  Dict				)      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	List[Any]						=self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    _lowerCamelCase :	Optional[Any]						=type
                    self.model_tester.create_and_check_model(*lowercase_				)






      def  lowerCamelCase (      self		:  Tuple				)      ->		Any:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*lowercase_				)






      def  lowerCamelCase (      self		:  str				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	Dict						=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*lowercase_				)






      def  lowerCamelCase (      self		:  List[str]				)      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	Any						=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*lowercase_				)






      def  lowerCamelCase (      self		:  List[str]				)      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	List[str]						=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_information_extraction(*lowercase_				)






      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	str						=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*lowercase_				)






      @slow
      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		List[str]:
             """simple docstring"""




             for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase :	Union[str, Any]						=ErnieMModel.from_pretrained(lowercase_				)
                    self.assertIsNotNone(lowercase_				)




@require_torch
class A						(     unittest.TestCase  ):






      @slow
      def  lowerCamelCase (      self		:  List[str]				)      ->		Dict:
             """simple docstring"""




             _lowerCamelCase :	Dict						=ErnieMModel.from_pretrained('susnato/ernie-m-base_pytorch'				)
             model.eval()
             _lowerCamelCase :	Optional[int]						=torch.tensor([[0, 1, 2, 3, 4, 5]]				)
             _lowerCamelCase :	Optional[int]						=model(lowercase_				)[0]

             # TODO Replace vocab size
             _lowerCamelCase :	Optional[Any]						=768

             _lowerCamelCase :	str						=torch.Size((1, 6, hidden_size)				)
             self.assertEqual(output.shape    ,     lowercase_				)

             _lowerCamelCase :	str						=torch.tensor(
                 [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]]				)

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


