


from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.ctrl.modeling_tf_ctrl import (
     TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFCTRLForSequenceClassification,
     TFCTRLLMHeadModel,
     TFCTRLModel,
 )




class A						(     UpperCamelCase_  ):






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




             _lowerCamelCase :	Union[str, Any]						=parent
             _lowerCamelCase :	Dict						=13
             _lowerCamelCase :	List[Any]						=7
             _lowerCamelCase :	Optional[Any]						=True
             _lowerCamelCase :	Tuple						=True
             _lowerCamelCase :	Any						=True
             _lowerCamelCase :	Any						=True
             _lowerCamelCase :	int						=True
             _lowerCamelCase :	Dict						=99
             _lowerCamelCase :	Union[str, Any]						=32
             _lowerCamelCase :	Any						=2
             _lowerCamelCase :	Union[str, Any]						=4
             _lowerCamelCase :	int						=37
             _lowerCamelCase :	Optional[int]						='gelu'
             _lowerCamelCase :	Optional[int]						=0.1
             _lowerCamelCase :	Dict						=0.1
             _lowerCamelCase :	Any						=512
             _lowerCamelCase :	Dict						=16
             _lowerCamelCase :	Dict						=2
             _lowerCamelCase :	int						=0.02
             _lowerCamelCase :	Optional[Any]						=3
             _lowerCamelCase :	List[Any]						=4
             _lowerCamelCase :	Optional[int]						=None
             _lowerCamelCase :	str						=self.vocab_size - 1






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




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

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

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

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

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

             _lowerCamelCase :	str						=CTRLConfig(
                 vocab_size=self.vocab_size    ,     n_embd=self.hidden_size    ,     n_layer=self.num_hidden_layers    ,     n_head=self.num_attention_heads    ,     n_positions=self.max_position_embeddings    ,     pad_token_id=self.pad_token_id    ,     )

             _lowerCamelCase :	Optional[Any]						=ids_tensor([self.num_hidden_layers, self.num_attention_heads]    ,     2				)

             return (
                 config,
                 input_ids,
                 input_mask,
                 head_mask,
                 token_type_ids,
                 mc_token_ids,
                 sequence_labels,
                 token_labels,
                 choice_labels,
             )






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




             _lowerCamelCase :	Optional[Any]						=TFCTRLModel(config=lowercase_				)
             _lowerCamelCase :	Tuple						={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             _lowerCamelCase :	Tuple						=model(lowercase_				)

             _lowerCamelCase :	int						=[input_ids, None, input_mask]  # None is the input for 'past'
             _lowerCamelCase :	int						=model(lowercase_				)

             _lowerCamelCase :	Optional[Any]						=model(lowercase_				)

             self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)				)






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




             _lowerCamelCase :	Optional[int]						=TFCTRLLMHeadModel(config=lowercase_				)
             _lowerCamelCase :	Optional[Any]						={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             _lowerCamelCase :	List[Any]						=model(lowercase_				)
             self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size)				)






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




             _lowerCamelCase :	Dict						=self.num_labels
             _lowerCamelCase :	List[Any]						=ids_tensor([self.batch_size]    ,     self.type_sequence_label_size				)
             _lowerCamelCase :	List[str]						={
                 'input_ids': input_ids,
                 'token_type_ids': token_type_ids,
                 'labels': sequence_labels,
             }
             _lowerCamelCase :	Dict						=TFCTRLForSequenceClassification(lowercase_				)
             _lowerCamelCase :	List[str]						=model(lowercase_				)
             self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)				)






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




             _lowerCamelCase :	List[Any]						=self.prepare_config_and_inputs()

             (
                 (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  (
                 _lowerCamelCase
             )			,  
             ) :	Dict						=config_and_inputs

             _lowerCamelCase :	Union[str, Any]						={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
             return config, inputs_dict




@require_tf
class A						(     UpperCamelCase_					,							UpperCamelCase_					,							unittest.TestCase  ):
      UpperCamelCase__							:						List[str]   =(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
      UpperCamelCase__							:						Optional[Any]   =(TFCTRLLMHeadModel,) if is_tf_available() else ()
      UpperCamelCase__							:						Tuple   =(
          {
              'feature-extraction': TFCTRLModel,
              'text-classification': TFCTRLForSequenceClassification,
              'text-generation': TFCTRLLMHeadModel,
              'zero-shot': TFCTRLForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      UpperCamelCase__							:						Optional[int]   =False
      UpperCamelCase__							:						Optional[int]   =False






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




             if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                    # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                    # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
                    # config could not be created.
                    return True

             return False






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




             _lowerCamelCase :	List[Any]						=TFCTRLModelTester(self				)
             _lowerCamelCase :	Union[str, Any]						=ConfigTester(self    ,     config_class=lowercase_    ,     n_embd=37				)






      def  lowerCamelCase (      self		:  int				)      ->		List[Any]:
             """simple docstring"""




             self.config_tester.run_common_tests()






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




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






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




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






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




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






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




             _lowerCamelCase			,  _lowerCamelCase :	int						=self.model_tester.prepare_config_and_inputs_for_common()
             _lowerCamelCase :	Optional[Any]						=[TFCTRLLMHeadModel]
             _lowerCamelCase :	Tuple						=[TFCTRLForSequenceClassification]

             for model_class in self.all_model_classes:
                    _lowerCamelCase :	Optional[Any]						=model_class(lowercase_				)
                    model.build()  # may be needed for the get_bias() call below
                    assert isinstance(model.get_input_embeddings()    ,     tf.keras.layers.Layer				)

                    if model_class in list_lm_models:
                           _lowerCamelCase :	List[Any]						=model.get_output_embeddings()
                           assert isinstance(lowercase_    ,     tf.keras.layers.Layer				)
                           _lowerCamelCase :	Any						=model.get_bias()
                           assert isinstance(lowercase_    ,     lowercase_				)
                           for k, v in name.items():
                                  assert isinstance(lowercase_    ,     tf.Variable				)
                    elif model_class in list_other_models_with_output_ebd:
                           _lowerCamelCase :	Optional[int]						=model.get_output_embeddings()
                           assert isinstance(lowercase_    ,     tf.keras.layers.Layer				)
                           _lowerCamelCase :	Any						=model.get_bias()
                           assert name is None
                    else:
                           _lowerCamelCase :	Optional[int]						=model.get_output_embeddings()
                           assert x is None
                           _lowerCamelCase :	Optional[int]						=model.get_bias()
                           assert name is None






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




             for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase :	List[Any]						=TFCTRLModel.from_pretrained(lowercase_				)
                    self.assertIsNotNone(lowercase_				)




@require_tf
class A						(     unittest.TestCase  ):






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




             _lowerCamelCase :	Dict						=TFCTRLLMHeadModel.from_pretrained('ctrl'				)
             _lowerCamelCase :	str						=tf.convert_to_tensor([[1_1859, 0, 1611, 8]]    ,     dtype=tf.intaa				)  # Legal the president is
             _lowerCamelCase :	Dict						=[
                 1_1859,
                 0,
                 1611,
                 8,
                 5,
                 150,
                 2_6449,
                 2,
                 19,
                 348,
                 469,
                 3,
                 2595,
                 48,
                 2_0740,
                 24_6533,
                 24_6533,
                 19,
                 30,
                 5,
             ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

             _lowerCamelCase :	Union[str, Any]						=model.generate(lowercase_    ,     do_sample=lowercase_				)
             self.assertListEqual(output_ids[0].numpy().tolist()    ,     lowercase_				)






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				)				)


