






"""simple docstring"""
from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
			from transformers import (
			    TFAutoModel,
			    TFAutoModelForCausalLM,
			    TFAutoModelForMaskedLM,
			    TFAutoModelForPreTraining,
			    TFAutoModelForQuestionAnswering,
			    TFAutoModelForSeqaSeqLM,
			    TFAutoModelForSequenceClassification,
			    TFAutoModelForTableQuestionAnswering,
			    TFAutoModelForTokenClassification,
			    TFAutoModelWithLMHead,
			    TFBertForMaskedLM,
			    TFBertForPreTraining,
			    TFBertForQuestionAnswering,
			    TFBertForSequenceClassification,
			    TFBertModel,
			    TFFunnelBaseModel,
			    TFFunnelModel,
			    TFGPTaLMHeadModel,
			    TFRobertaForMaskedLM,
			    TFTaForConditionalGeneration,
			    TFTapasForQuestionAnswering,
			)
			from transformers.models.auto.modeling_tf_auto import (
			    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
			    TF_MODEL_FOR_MASKED_LM_MAPPING,
			    TF_MODEL_FOR_PRETRAINING_MAPPING,
			    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
			    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
			    TF_MODEL_MAPPING,
			)
			from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
			from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
			from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
			from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST







class     UpperCamelCase_ (a__  ):
	__magic_name__			   =       "new-model"


if is_tf_available():







			class     UpperCamelCase_ (a__  ):
				__magic_name__			   =       NewModelConfig







@require_tf
class     UpperCamelCase_ (unittest.TestCase  ):





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    str:
				UpperCAmelCase_  :				Optional[int]      =      "bert-base-cased"
				UpperCAmelCase_  :				Optional[int]      =      AutoConfig.from_pretrained(lowerCamelCase_		)
				self.assertIsNotNone(lowerCamelCase_		)
				self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

				UpperCAmelCase_  :				List[str]      =      TFAutoModel.from_pretrained(lowerCamelCase_		)
				self.assertIsNotNone(lowerCamelCase_		)
				self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    str:
				UpperCAmelCase_  :				Optional[int]      =      "bert-base-cased"
				UpperCAmelCase_  :				str      =      AutoConfig.from_pretrained(lowerCamelCase_		)
				self.assertIsNotNone(lowerCamelCase_		)
				self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

				UpperCAmelCase_  :				Any      =      TFAutoModelForPreTraining.from_pretrained(lowerCamelCase_		)
				self.assertIsNotNone(lowerCamelCase_		)
				self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[int]:
				for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_  :				str      =      AutoConfig.from_pretrained(lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

							UpperCAmelCase_  :				Any      =      TFAutoModelForCausalLM.from_pretrained(lowerCamelCase_		)
							UpperCAmelCase_  ,			UpperCAmelCase_  :				Any      =      TFAutoModelForCausalLM.from_pretrained(lowerCamelCase_			,			output_loading_info=lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    str:
				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_  :				Dict      =      AutoConfig.from_pretrained(lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

							UpperCAmelCase_  :				Union[str, Any]      =      TFAutoModelWithLMHead.from_pretrained(lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    str:
				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_  :				int      =      AutoConfig.from_pretrained(lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

							UpperCAmelCase_  :				Optional[int]      =      TFAutoModelForMaskedLM.from_pretrained(lowerCamelCase_		)
							UpperCAmelCase_  ,			UpperCAmelCase_  :				Dict      =      TFAutoModelForMaskedLM.from_pretrained(lowerCamelCase_			,			output_loading_info=lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Union[str, Any]:
				for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_  :				List[Any]      =      AutoConfig.from_pretrained(lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

							UpperCAmelCase_  :				Union[str, Any]      =      TFAutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_		)
							UpperCAmelCase_  ,			UpperCAmelCase_  :				Optional[int]      =      TFAutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_			,			output_loading_info=lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    List[Any]:
				# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				for model_name in ["bert-base-uncased"]:
							UpperCAmelCase_  :				Dict      =      AutoConfig.from_pretrained(lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

							UpperCAmelCase_  :				Any      =      TFAutoModelForSequenceClassification.from_pretrained(lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    int:
				# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				for model_name in ["bert-base-uncased"]:
							UpperCAmelCase_  :				Union[str, Any]      =      AutoConfig.from_pretrained(lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

							UpperCAmelCase_  :				Optional[Any]      =      TFAutoModelForQuestionAnswering.from_pretrained(lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)





	@slow
	@require_tensorflow_probability
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    List[str]:
				for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
							UpperCAmelCase_  :				List[Any]      =      AutoConfig.from_pretrained(lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

							UpperCAmelCase_  :				Optional[Any]      =      TFAutoModelForTableQuestionAnswering.from_pretrained(lowerCamelCase_		)
							UpperCAmelCase_  ,			UpperCAmelCase_  :				str      =      TFAutoModelForTableQuestionAnswering.from_pretrained(
							    lowerCamelCase_			,			output_loading_info=lowerCamelCase_		)
							self.assertIsNotNone(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Tuple:
				UpperCAmelCase_  :				Dict      =      TFAutoModelWithLMHead.from_pretrained(lowerCamelCase_		)
				self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)
				self.assertEqual(model.num_parameters()			,			14_410		)
				self.assertEqual(model.num_parameters(only_trainable=lowerCamelCase_		)			,			14_410		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Any      =      TFAutoModelWithLMHead.from_pretrained(lowerCamelCase_		)
				self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)
				self.assertEqual(model.num_parameters()			,			14_410		)
				self.assertEqual(model.num_parameters(only_trainable=lowerCamelCase_		)			,			14_410		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    List[Any]:
				# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
				UpperCAmelCase_  :				List[str]      =      TFAutoModel.from_pretrained("sgugger/funnel-random-tiny"		)
				self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

				UpperCAmelCase_  :				Optional[Any]      =      copy.deepcopy(model.config		)
				UpperCAmelCase_  :				Optional[int]      =      ["FunnelBaseModel"]
				UpperCAmelCase_  :				Tuple      =      TFAutoModel.from_config(lowerCamelCase_		)
				self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

				with tempfile.TemporaryDirectory() as tmp_dir:
							model.save_pretrained(lowerCamelCase_		)
							UpperCAmelCase_  :				List[Any]      =      TFAutoModel.from_pretrained(lowerCamelCase_		)
							self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[Any]:
				try:
							AutoConfig.register("new-model"			,			lowerCamelCase_		)

							UpperCAmelCase_  :				str      =      [
							    TFAutoModel,
							    TFAutoModelForCausalLM,
							    TFAutoModelForMaskedLM,
							    TFAutoModelForPreTraining,
							    TFAutoModelForQuestionAnswering,
							    TFAutoModelForSequenceClassification,
							    TFAutoModelForTokenClassification,
							]

							for auto_class in auto_classes:
										with self.subTest(auto_class.__name__		):
													# Wrong config class will raise an error
													with self.assertRaises(lowerCamelCase_		):
																auto_class.register(lowerCamelCase_			,			lowerCamelCase_		)
													auto_class.register(lowerCamelCase_			,			lowerCamelCase_		)
													# Trying to register something existing in the Transformers library will raise an error
													with self.assertRaises(lowerCamelCase_		):
																auto_class.register(lowerCamelCase_			,			lowerCamelCase_		)

													# Now that the config is registered, it can be used as any other config with the auto-API
													UpperCAmelCase_  :				List[Any]      =      BertModelTester(self		).get_config()
													UpperCAmelCase_  :				Optional[Any]      =      NewModelConfig(**tiny_config.to_dict()		)
													UpperCAmelCase_  :				List[Any]      =      auto_class.from_config(lowerCamelCase_		)
													self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

													with tempfile.TemporaryDirectory() as tmp_dir:
																model.save_pretrained(lowerCamelCase_		)
																UpperCAmelCase_  :				Dict      =      auto_class.from_pretrained(lowerCamelCase_		)
																self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)

				finally:
							if "new-model" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["new-model"]
							for mapping in (
							    TF_MODEL_MAPPING,
							    TF_MODEL_FOR_PRETRAINING_MAPPING,
							    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
							    TF_MODEL_FOR_MASKED_LM_MAPPING,
							):
										if NewModelConfig in mapping._extra_content:
													del mapping._extra_content[NewModelConfig]





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    str:
				with self.assertRaisesRegex(
				    lowerCamelCase_			,			"bert-base is not a local folder and is not a valid model identifier"		):
							UpperCAmelCase_  :				int      =      TFAutoModel.from_pretrained("bert-base"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Union[str, Any]:
				with self.assertRaisesRegex(
				    lowerCamelCase_			,			R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"		):
							UpperCAmelCase_  :				Optional[Any]      =      TFAutoModel.from_pretrained(lowerCamelCase_			,			revision="aaaaaa"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Union[str, Any]:
				with self.assertRaisesRegex(
				    lowerCamelCase_			,			"hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"			,			):
							UpperCAmelCase_  :				int      =      TFAutoModel.from_pretrained("hf-internal-testing/config-no-model"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    List[str]:
				with self.assertRaisesRegex(lowerCamelCase_			,			"Use `from_pt=True` to load this model"		):
							UpperCAmelCase_  :				str      =      TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    str:
				# Make sure we have cached the model.
				UpperCAmelCase_  :				Union[str, Any]      =      TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"		)
				with RequestCounter() as counter:
							UpperCAmelCase_  :				List[str]      =      TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"		)
							self.assertEqual(counter.get_request_count			,			0		)
							self.assertEqual(counter.head_request_count			,			1		)
							self.assertEqual(counter.other_request_count			,			0		)

				# With a sharded checkpoint
				UpperCAmelCase_  :				Union[str, Any]      =      TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"		)
				with RequestCounter() as counter:
							UpperCAmelCase_  :				Any      =      TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"		)
							self.assertEqual(counter.get_request_count			,			0		)
							self.assertEqual(counter.head_request_count			,			1		)
							self.assertEqual(counter.other_request_count			,			0		)











"""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     UpperCamelCase_ (__A  ):





	def __init__(   self      :				List[Any]			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				List[Any]=None			,			lowerCAmelCase_      :				List[str]=True			,			lowerCAmelCase_      :				List[Any]=None			,			**lowerCAmelCase_      :				Tuple		)		->    Dict:
				UpperCAmelCase_  :				Tuple      =      parent
				UpperCAmelCase_  :				Optional[int]      =      config_class
				UpperCAmelCase_  :				List[str]      =      has_text_modality
				UpperCAmelCase_  :				Tuple      =      kwargs
				UpperCAmelCase_  :				int      =      common_properties





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Optional[int]      =      self.config_class(**self.inputs_dict		)
				UpperCAmelCase_  :				int      =      (
				    ["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_  :				Optional[Any]      =      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   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Tuple:
				UpperCAmelCase_  :				str      =      self.config_class(**self.inputs_dict		)
				UpperCAmelCase_  :				List[str]      =      json.loads(config.to_json_string()		)
				for key, value in self.inputs_dict.items():
							self.parent.assertEqual(obj[key]			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Any:
				UpperCAmelCase_  :				Optional[Any]      =      self.config_class(**self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				List[str]      =      os.path.join(lowerCAmelCase_			,			"config.json"		)
							config_first.to_json_file(lowerCAmelCase_		)
							UpperCAmelCase_  :				Dict      =      self.config_class.from_json_file(lowerCAmelCase_		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Dict:
				UpperCAmelCase_  :				Any      =      self.config_class(**self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							config_first.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_  :				Tuple      =      self.config_class.from_pretrained(lowerCAmelCase_		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Optional[Any]:
				UpperCAmelCase_  :				Any      =      self.config_class(**self.inputs_dict		)

				UpperCAmelCase_  :				int      =      "test"
				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				Optional[int]      =      os.path.join(lowerCAmelCase_			,			lowerCAmelCase_		)
							config_first.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_  :				Union[str, Any]      =      self.config_class.from_pretrained(lowerCAmelCase_			,			subfolder=lowerCAmelCase_		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Tuple:
				UpperCAmelCase_  :				List[str]      =      self.config_class(**self.inputs_dict			,			num_labels=5		)
				self.parent.assertEqual(len(config.idalabel		)			,			5		)
				self.parent.assertEqual(len(config.labelaid		)			,			5		)

				UpperCAmelCase_  :				List[Any]      =      3
				self.parent.assertEqual(len(config.idalabel		)			,			3		)
				self.parent.assertEqual(len(config.labelaid		)			,			3		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Optional[Any]:
				if self.config_class.is_composition:
							return
				UpperCAmelCase_  :				str      =      self.config_class()
				self.parent.assertIsNotNone(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    List[str]:
				UpperCAmelCase_  :				Optional[int]      =      copy.deepcopy(lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      self.config_class(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      []
				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_  :				Any      =      "\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   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    str:
				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()



