"""simple docstring"""


import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
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 (
        MODEL_FOR_PRETRAINING_MAPPING,
        AlbertForMaskedLM,
        AlbertForMultipleChoice,
        AlbertForPreTraining,
        AlbertForQuestionAnswering,
        AlbertForSequenceClassification,
        AlbertForTokenClassification,
        AlbertModel,
    )
    from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST


class 							a					:


    def __init__(       self						:	List[str] ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Optional[int]=13 ,					__lowerCAmelCase						:	Dict=7 ,					__lowerCAmelCase						:	Any=True ,					__lowerCAmelCase						:	Union[str, Any]=True ,					__lowerCAmelCase						:	Tuple=True ,					__lowerCAmelCase						:	Optional[int]=True ,					__lowerCAmelCase						:	List[str]=99 ,					__lowerCAmelCase						:	List[str]=16 ,					__lowerCAmelCase						:	Optional[int]=36 ,					__lowerCAmelCase						:	Tuple=6 ,					__lowerCAmelCase						:	List[Any]=6 ,					__lowerCAmelCase						:	List[Any]=6 ,					__lowerCAmelCase						:	List[str]=37 ,					__lowerCAmelCase						:	Union[str, Any]="gelu" ,					__lowerCAmelCase						:	Optional[Any]=0.1 ,					__lowerCAmelCase						:	Optional[int]=0.1 ,					__lowerCAmelCase						:	Optional[int]=512 ,					__lowerCAmelCase						:	Union[str, Any]=16 ,					__lowerCAmelCase						:	Union[str, Any]=2 ,					__lowerCAmelCase						:	Optional[int]=0.02 ,					__lowerCAmelCase						:	Dict=3 ,					__lowerCAmelCase						:	int=4 ,					__lowerCAmelCase						:	Optional[Any]=None ,					):
      _UpperCAmelCase    			=   parent
      _UpperCAmelCase    			=   batch_size
      _UpperCAmelCase    			=   seq_length
      _UpperCAmelCase    			=   is_training
      _UpperCAmelCase    			=   use_input_mask
      _UpperCAmelCase    			=   use_token_type_ids
      _UpperCAmelCase    			=   use_labels
      _UpperCAmelCase    			=   vocab_size
      _UpperCAmelCase    			=   embedding_size
      _UpperCAmelCase    			=   hidden_size
      _UpperCAmelCase    			=   num_hidden_layers
      _UpperCAmelCase    			=   num_hidden_groups
      _UpperCAmelCase    			=   num_attention_heads
      _UpperCAmelCase    			=   intermediate_size
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   hidden_dropout_prob
      _UpperCAmelCase    			=   attention_probs_dropout_prob
      _UpperCAmelCase    			=   max_position_embeddings
      _UpperCAmelCase    			=   type_vocab_size
      _UpperCAmelCase    			=   type_sequence_label_size
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   num_labels
      _UpperCAmelCase    			=   num_choices
      _UpperCAmelCase    			=   scope


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size       )

      _UpperCAmelCase    			=   None
      if self.use_input_mask:
        _UpperCAmelCase    			=   random_attention_mask([self.batch_size, self.seq_length]       )

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

      _UpperCAmelCase    			=   None
      _UpperCAmelCase    			=   None
      _UpperCAmelCase    			=   None
      if self.use_labels:
        _UpperCAmelCase    			=   ids_tensor([self.batch_size] ,					self.type_sequence_label_size       )
        _UpperCAmelCase    			=   ids_tensor([self.batch_size, self.seq_length] ,					self.num_labels       )
        _UpperCAmelCase    			=   ids_tensor([self.batch_size] ,					self.num_choices       )

      _UpperCAmelCase    			=   self.get_config()

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


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      return AlbertConfig(
          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 ,					num_hidden_groups=self.num_hidden_groups ,					)


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   AlbertModel(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      _UpperCAmelCase    			=   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						:	Dict ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Any       ):
      _UpperCAmelCase    			=   AlbertForPreTraining(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase ,					sentence_order_label=__lowerCAmelCase ,					)
      self.parent.assertEqual(result.prediction_logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)       )
      self.parent.assertEqual(result.sop_logits.shape ,					(self.batch_size, config.num_labels)       )


    def   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   AlbertForMaskedLM(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase       )
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)       )


    def   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	str       ):
      _UpperCAmelCase    			=   AlbertForQuestionAnswering(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(
          __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						:	Any ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   self.num_labels
      _UpperCAmelCase    			=   AlbertForSequenceClassification(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__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						:	List[str] ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   self.num_labels
      _UpperCAmelCase    			=   AlbertForTokenClassification(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__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						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   self.num_choices
      _UpperCAmelCase    			=   AlbertForMultipleChoice(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   input_ids.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   token_type_ids.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   input_mask.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					labels=__lowerCAmelCase ,					)
      self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_choices)       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   self.prepare_config_and_inputs()
      (
          (
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					
      )    			=   config_and_inputs
      _UpperCAmelCase    			=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict



@require_torch
class 							a					(		lowerCAmelCase_						,				lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	Optional[Any]  				=				(
        (
            AlbertModel,
            AlbertForPreTraining,
            AlbertForMaskedLM,
            AlbertForMultipleChoice,
            AlbertForSequenceClassification,
            AlbertForTokenClassification,
            AlbertForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    _snake_case  :	List[str]  				=				(
        {
            'feature-extraction': AlbertModel,
            'fill-mask': AlbertForMaskedLM,
            'question-answering': AlbertForQuestionAnswering,
            'text-classification': AlbertForSequenceClassification,
            'token-classification': AlbertForTokenClassification,
            'zero-shot': AlbertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _snake_case  :	List[str]  				=				True


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Tuple=False       ):
      _UpperCAmelCase    			=   super()._prepare_for_class(__lowerCAmelCase ,					__lowerCAmelCase ,					return_labels=__lowerCAmelCase       )

      if return_labels:
        if model_class in get_values(__lowerCAmelCase       ):
          _UpperCAmelCase    			=   torch.zeros(
              (self.model_tester.batch_size, self.model_tester.seq_length) ,					dtype=torch.long ,					device=__lowerCAmelCase       )
          _UpperCAmelCase    			=   torch.zeros(
              self.model_tester.batch_size ,					dtype=torch.long ,					device=__lowerCAmelCase       )
      return inputs_dict


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   AlbertModelTester(self       )
      _UpperCAmelCase    			=   ConfigTester(self ,					config_class=__lowerCAmelCase ,					hidden_size=37       )


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      self.config_tester.run_common_tests()


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        _UpperCAmelCase    			=   type
        self.model_tester.create_and_check_model(*__lowerCAmelCase       )


    @slow
    def   lowerCAmelCase_	(       self						:	Dict       ):
      for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _UpperCAmelCase    			=   AlbertModel.from_pretrained(__lowerCAmelCase       )
        self.assertIsNotNone(__lowerCAmelCase       )



@require_torch
class 							a					(		unittest.TestCase ):


    @slow
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   AlbertModel.from_pretrained("""albert-base-v2"""       )
      _UpperCAmelCase    			=   torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
      _UpperCAmelCase    			=   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase       )[0]
      _UpperCAmelCase    			=   torch.Size((1, 11, 768)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.tensor(
          [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]       )

      self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,					__lowerCAmelCase ,					atol=1e-4       )       )

"""simple docstring"""


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs


class 							a					(		lowerCAmelCase_ ):


    def __init__(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	str=None ,					__lowerCAmelCase						:	List[Any]=True ,					__lowerCAmelCase						:	Union[str, Any]=None ,					**__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   parent
      _UpperCAmelCase    			=   config_class
      _UpperCAmelCase    			=   has_text_modality
      _UpperCAmelCase    			=   kwargs
      _UpperCAmelCase    			=   common_properties


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict       )
      _UpperCAmelCase    			=   (
          ["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
          if self.common_properties is None
          else self.common_properties
      )

      # Add common fields for text models
      if self.has_text_modality:
        common_properties.extend(["""vocab_size"""]       )

      # Test that config has the common properties as getters
      for prop in common_properties:
        self.parent.assertTrue(hasattr(__lowerCAmelCase ,					__lowerCAmelCase       ) ,					msg=f'''`{prop}` does not exist'''       )

      # Test that config has the common properties as setter
      for idx, name in enumerate(__lowerCAmelCase       ):
        try:
          setattr(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )
          self.parent.assertEqual(
              getattr(__lowerCAmelCase ,					__lowerCAmelCase       ) ,					__lowerCAmelCase ,					msg=f'''`{name} value {idx} expected, but was {getattr(__lowerCAmelCase ,					__lowerCAmelCase       )}'''       )
        except NotImplementedError:
          # Some models might not be able to implement setters for common_properties
          # In that case, a NotImplementedError is raised
          pass

        # Test if config class can be called with Config(prop_name=..)
      for idx, name in enumerate(__lowerCAmelCase       ):
        try:
          _UpperCAmelCase    			=   self.config_class(**{name: idx}       )
          self.parent.assertEqual(
              getattr(__lowerCAmelCase ,					__lowerCAmelCase       ) ,					__lowerCAmelCase ,					msg=f'''`{name} value {idx} expected, but was {getattr(__lowerCAmelCase ,					__lowerCAmelCase       )}'''       )
        except NotImplementedError:
          # Some models might not be able to implement setters for common_properties
          # In that case, a NotImplementedError is raised
          pass


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict       )
      _UpperCAmelCase    			=   json.loads(config.to_json_string()       )
      for key, value in self.inputs_dict.items():
        self.parent.assertEqual(obj[key] ,					__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict       )

      with tempfile.TemporaryDirectory() as tmpdirname:
        _UpperCAmelCase    			=   os.path.join(__lowerCAmelCase ,					"""config.json"""       )
        config_first.to_json_file(__lowerCAmelCase       )
        _UpperCAmelCase    			=   self.config_class.from_json_file(__lowerCAmelCase       )

      self.parent.assertEqual(config_second.to_dict() ,					config_first.to_dict()       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict       )

      with tempfile.TemporaryDirectory() as tmpdirname:
        config_first.save_pretrained(__lowerCAmelCase       )
        _UpperCAmelCase    			=   self.config_class.from_pretrained(__lowerCAmelCase       )

      self.parent.assertEqual(config_second.to_dict() ,					config_first.to_dict()       )


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict       )

      _UpperCAmelCase    			=   """test"""
      with tempfile.TemporaryDirectory() as tmpdirname:
        _UpperCAmelCase    			=   os.path.join(__lowerCAmelCase ,					__lowerCAmelCase       )
        config_first.save_pretrained(__lowerCAmelCase       )
        _UpperCAmelCase    			=   self.config_class.from_pretrained(__lowerCAmelCase ,					subfolder=__lowerCAmelCase       )

      self.parent.assertEqual(config_second.to_dict() ,					config_first.to_dict()       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.config_class(**self.inputs_dict ,					num_labels=5       )
      self.parent.assertEqual(len(config.idalabel       ) ,					5       )
      self.parent.assertEqual(len(config.labelaid       ) ,					5       )

      _UpperCAmelCase    			=   3
      self.parent.assertEqual(len(config.idalabel       ) ,					3       )
      self.parent.assertEqual(len(config.labelaid       ) ,					3       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      if self.config_class.is_composition:
        return
      _UpperCAmelCase    			=   self.config_class()
      self.parent.assertIsNotNone(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   copy.deepcopy(__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.config_class(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   []
      for key, value in config_common_kwargs.items():
        if key == "torch_dtype":
          if not is_torch_available():
            continue
          else:
            import torch

            if config.torch_dtype != torch.floataa:
              wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa)       )
        elif getattr(__lowerCAmelCase ,					__lowerCAmelCase       ) != value:
          wrong_values.append((key, getattr(__lowerCAmelCase ,					__lowerCAmelCase       ), value)       )

      if len(__lowerCAmelCase       ) > 0:
        _UpperCAmelCase    			=   """\n""".join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]       )
        raise ValueError(f'''The following keys were not properly set in the config:\n{errors}'''       )


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      self.create_and_test_config_common_properties()
      self.create_and_test_config_to_json_string()
      self.create_and_test_config_to_json_file()
      self.create_and_test_config_from_and_save_pretrained()
      self.create_and_test_config_from_and_save_pretrained_subfolder()
      self.create_and_test_config_with_num_labels()
      self.check_config_can_be_init_without_params()
      self.check_config_arguments_init()
