
"""simple docstring"""




import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					assert isinstance(lowerCAmelCase__			,      lowerCAmelCase__		)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_1", "col_2", "col_3"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('''keep_in_memory'''			,      [False, True]		)
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						tmp_path / '''cache'''
					lowercase						     =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										lowercase						     =						JsonDatasetReader(lowerCAmelCase__			,      cache_dir=lowerCAmelCase__			,      keep_in_memory=lowerCAmelCase__		).read()
					_check_json_dataset(lowerCAmelCase__			,      lowerCAmelCase__		)



@pytest.mark.parametrize(
    '''features'''			,      [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]			,      )
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						tmp_path / '''cache'''
					lowercase						     =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					lowercase						     =						features.copy() if features else default_expected_features
					lowercase						     =						(
					    Features({feature: Value(lowerCAmelCase__		) for feature, dtype in features.items()}		) if features is not None else None
					)
					lowercase						     =						JsonDatasetReader(lowerCAmelCase__			,      features=lowerCAmelCase__			,      cache_dir=lowerCAmelCase__		).read()
					_check_json_dataset(lowerCAmelCase__			,      lowerCAmelCase__		)



@pytest.mark.parametrize(
    '''features'''			,      [
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ]			,      )
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						tmp_path / '''cache'''
					lowercase						     =						{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
					lowercase						     =						features.copy() if features else default_expected_features
					lowercase						     =						(
					    Features({feature: Value(lowerCAmelCase__		) for feature, dtype in features.items()}		) if features is not None else None
					)
					lowercase						     =						JsonDatasetReader(lowerCAmelCase__			,      features=lowerCAmelCase__			,      cache_dir=lowerCAmelCase__		).read()
					assert isinstance(lowerCAmelCase__			,      lowerCAmelCase__		)
					assert dataset.num_rows == 2
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_3", "col_1", "col_2"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						{'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
					lowercase						     =						features.copy()
					lowercase						     =						(
					    Features({feature: Value(lowerCAmelCase__		) for feature, dtype in features.items()}		) if features is not None else None
					)
					lowercase						     =						tmp_path / '''cache'''
					lowercase						     =						JsonDatasetReader(lowerCAmelCase__			,      features=lowerCAmelCase__			,      cache_dir=lowerCAmelCase__		).read()
					assert isinstance(lowerCAmelCase__			,      lowerCAmelCase__		)
					assert dataset.num_rows == 2
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_2", "col_3", "col_1"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('''split'''			,      [None, NamedSplit('''train'''		), '''train''', '''test''']		)
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						tmp_path / '''cache'''
					lowercase						     =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					lowercase						     =						JsonDatasetReader(lowerCAmelCase__			,      cache_dir=lowerCAmelCase__			,      split=lowerCAmelCase__		).read()
					_check_json_dataset(lowerCAmelCase__			,      lowerCAmelCase__		)
					assert dataset.split == split if split else "train"



@pytest.mark.parametrize('''path_type'''			,      [str, list]		)
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					if issubclass(lowerCAmelCase__			,      lowerCAmelCase__		):
										lowercase						     =						jsonl_path
					elif issubclass(lowerCAmelCase__			,      lowerCAmelCase__		):
										lowercase						     =						[jsonl_path]
					lowercase						     =						tmp_path / '''cache'''
					lowercase						     =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					lowercase						     =						JsonDatasetReader(lowerCAmelCase__			,      cache_dir=lowerCAmelCase__		).read()
					_check_json_dataset(lowerCAmelCase__			,      lowerCAmelCase__		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__=("train",)		):
					'''simple docstring'''
					assert isinstance(lowerCAmelCase__			,      lowerCAmelCase__		)
					for split in splits:
										lowercase						     =						dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 3
										assert dataset.column_names == ["col_1", "col_2", "col_3"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('''keep_in_memory'''			,      [False, True]		)
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						tmp_path / '''cache'''
					lowercase						     =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										lowercase						     =						JsonDatasetReader({'''train''': jsonl_path}			,      cache_dir=lowerCAmelCase__			,      keep_in_memory=lowerCAmelCase__		).read()
					_check_json_datasetdict(lowerCAmelCase__			,      lowerCAmelCase__		)



@pytest.mark.parametrize(
    '''features'''			,      [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]			,      )
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						tmp_path / '''cache'''
					lowercase						     =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					lowercase						     =						features.copy() if features else default_expected_features
					lowercase						     =						(
					    Features({feature: Value(lowerCAmelCase__		) for feature, dtype in features.items()}		) if features is not None else None
					)
					lowercase						     =						JsonDatasetReader({'''train''': jsonl_path}			,      features=lowerCAmelCase__			,      cache_dir=lowerCAmelCase__		).read()
					_check_json_datasetdict(lowerCAmelCase__			,      lowerCAmelCase__		)



@pytest.mark.parametrize('''split'''			,      [None, NamedSplit('''train'''		), '''train''', '''test''']		)
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					if split:
										lowercase						     =						{split: jsonl_path}
					else:
										lowercase						     =						'''train'''
										lowercase						     =						{'''train''': jsonl_path, '''test''': jsonl_path}
					lowercase						     =						tmp_path / '''cache'''
					lowercase						     =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					lowercase						     =						JsonDatasetReader(lowerCAmelCase__			,      cache_dir=lowerCAmelCase__		).read()
					_check_json_datasetdict(lowerCAmelCase__			,      lowerCAmelCase__			,      splits=list(path.keys()		)		)
					assert all(dataset[split].split == split for split in path.keys()		)



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					return json.load(lowerCAmelCase__		)



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					return [json.loads(lowerCAmelCase__		) for line in buffer]







class     lowercase		:




							@pytest.mark.parametrize('''lines, load_json_function'''       ,[(True, load_json_lines), (False, load_json)])
							def 			A__				(    self       ,A__       ,A__       ,A__):
												with io.BytesIO() as buffer:
																	JsonDatasetWriter(A__       ,A__       ,lines=A__).write()
																	buffer.seek(0)
																	lowercase						     =						load_json_function(A__)
												assert isinstance(A__       ,A__)
												assert isinstance(exported_content[0]       ,A__)
												assert len(A__) == 1_0




							@pytest.mark.parametrize(
							    '''orient, container, keys, len_at'''       ,[
							        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
							        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
							        ('''index''', dict, set('''0123456789'''), None),
							        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
							        ('''values''', list, None, None),
							        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
							    ]       ,)
							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__):
												with io.BytesIO() as buffer:
																	JsonDatasetWriter(A__       ,A__       ,lines=A__       ,orient=A__).write()
																	buffer.seek(0)
																	lowercase						     =						load_json(A__)
												assert isinstance(A__       ,A__)
												if keys:
																	if container is dict:
																						assert exported_content.keys() == keys
																	else:
																						assert exported_content[0].keys() == keys
												else:
																	assert not hasattr(A__       ,'''keys''') and not hasattr(exported_content[0]       ,'''keys''')
												if len_at:
																	assert len(exported_content[len_at]) == 1_0
												else:
																	assert len(A__) == 1_0




							@pytest.mark.parametrize('''lines, load_json_function'''       ,[(True, load_json_lines), (False, load_json)])
							def 			A__				(    self       ,A__       ,A__       ,A__):
												with io.BytesIO() as buffer:
																	JsonDatasetWriter(A__       ,A__       ,lines=A__       ,num_proc=2).write()
																	buffer.seek(0)
																	lowercase						     =						load_json_function(A__)
												assert isinstance(A__       ,A__)
												assert isinstance(exported_content[0]       ,A__)
												assert len(A__) == 1_0




							@pytest.mark.parametrize(
							    '''orient, container, keys, len_at'''       ,[
							        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
							        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
							        ('''index''', dict, set('''0123456789'''), None),
							        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
							        ('''values''', list, None, None),
							        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
							    ]       ,)
							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__):
												with io.BytesIO() as buffer:
																	JsonDatasetWriter(A__       ,A__       ,lines=A__       ,orient=A__       ,num_proc=2).write()
																	buffer.seek(0)
																	lowercase						     =						load_json(A__)
												assert isinstance(A__       ,A__)
												if keys:
																	if container is dict:
																						assert exported_content.keys() == keys
																	else:
																						assert exported_content[0].keys() == keys
												else:
																	assert not hasattr(A__       ,'''keys''') and not hasattr(exported_content[0]       ,'''keys''')
												if len_at:
																	assert len(exported_content[len_at]) == 1_0
												else:
																	assert len(A__) == 1_0




							def 			A__				(    self       ,A__):
												with pytest.raises(A__):
																	with io.BytesIO() as buffer:
																						JsonDatasetWriter(A__       ,A__       ,num_proc=0)





							@pytest.mark.parametrize('''compression, extension'''       ,[('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')])
							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						tmp_path_factory.mktemp('''data''') / f'test.json.{extension}'
												lowercase						     =						str(shared_datadir / f'test_file.json.{extension}')
												JsonDatasetWriter(A__       ,A__       ,compression=A__).write()

												with fsspec.open(A__       ,'''rb'''       ,compression='''infer''') as f:
																	lowercase						     =						f.read()
												with fsspec.open(A__       ,'''rb'''       ,compression='''infer''') as f:
																	lowercase						     =						f.read()
												assert exported_content == original_content






import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase__		:Optional[Any]			  =   "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
lowercase__		:int			  =   direct_transformers_import(PATH_TO_TRANSFORMERS)

lowercase__		:List[Any]			  =   transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowercase__		:List[str]			  =   {
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
    }
)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						False
					for attribute in attributes:
										for modeling_source in source_strings:
															# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
															if (
															    f'config.{attribute}' in modeling_source
															    or f'getattr(config, "{attribute}"' in modeling_source
															    or f'getattr(self.config, "{attribute}"' in modeling_source
															):
																				lowercase						     =						True
															# Deal with multi-line cases
															elif (
															    re.search(
															        Rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'			,      lowerCAmelCase__			,      )
															    is not None
															):
																				lowercase						     =						True
															# `SequenceSummary` is called with `SequenceSummary(config)`
															elif attribute in [
															    "summary_type",
															    "summary_use_proj",
															    "summary_activation",
															    "summary_last_dropout",
															    "summary_proj_to_labels",
															    "summary_first_dropout",
															]:
																				if "SequenceSummary" in modeling_source:
																									lowercase						     =						True
															if attribute_used:
																				break
										if attribute_used:
															break

    # common and important attributes, even if they do not always appear in the modeling files
					lowercase						     =						[
					    '''bos_index''',
					    '''eos_index''',
					    '''pad_index''',
					    '''unk_index''',
					    '''mask_index''',
					    '''image_size''',
					    '''use_cache''',
					    '''out_features''',
					    '''out_indices''',
					]
					lowercase						     =						['''encoder_no_repeat_ngram_size''']

					# Special cases to be allowed
					lowercase						     =						True
					if not attribute_used:
										lowercase						     =						False
										for attribute in attributes:
															# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
															if attribute in ["is_encoder_decoder"] and default_value is True:
																				lowercase						     =						True
															elif attribute in ["tie_word_embeddings"] and default_value is False:
																				lowercase						     =						True

															# Allow cases without checking the default value in the configuration class
															elif attribute in attributes_to_allow + attributes_used_in_generation:
																				lowercase						     =						True
															elif attribute.endswith('''_token_id'''		):
																				lowercase						     =						True

															# configuration class specific cases
															if not case_allowed:
																				lowercase						     =						SPECIAL_CASES_TO_ALLOW.get(config_class.__name__			,      []		)
																				lowercase						     =						allowed_cases is True or attribute in allowed_cases

					return attribute_used or case_allowed



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						dict(inspect.signature(config_class.__init__		).parameters		)
					lowercase						     =						[x for x in list(signature.keys()		) if x not in ['''self''', '''kwargs''']]
					lowercase						     =						[signature[param].default for param in parameter_names]

					# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
					# as one variant is used, the test should pass
					lowercase						     =						{}
					if len(config_class.attribute_map		) > 0:
										lowercase						     =						{v: k for k, v in config_class.attribute_map.items()}

					# Get the path to modeling source files
					lowercase						     =						inspect.getsourcefile(lowerCAmelCase__		)
					lowercase						     =						os.path.dirname(lowerCAmelCase__		)
					# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
					lowercase						     =						[os.path.join(lowerCAmelCase__			,      lowerCAmelCase__		) for fn in os.listdir(lowerCAmelCase__		) if fn.startswith('''modeling_'''		)]

					# Get the source code strings
					lowercase						     =						[]
					for path in modeling_paths:
										if os.path.isfile(lowerCAmelCase__		):
															with open(lowerCAmelCase__		) as fp:
																				modeling_sources.append(fp.read()		)

					lowercase						     =						[]
					for config_param, default_value in zip(lowerCAmelCase__			,      lowerCAmelCase__		):
										# `attributes` here is all the variant names for `config_param`
										lowercase						     =						[config_param]
										# some configuration classes have non-empty `attribute_map`, and both names could be used in the
										# corresponding modeling files. As long as one of them appears, it is fine.
										if config_param in reversed_attribute_map:
															attributes.append(reversed_attribute_map[config_param]		)

										if not check_attribute_being_used(lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
															unused_attributes.append(attributes[0]		)

					return sorted(lowerCAmelCase__		)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						{}
					for _config_class in list(CONFIG_MAPPING.values()		):
										# Skip deprecated models
										if "models.deprecated" in _config_class.__module__:
															continue
										# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
										lowercase						     =						[
										    cls
										    for name, cls in inspect.getmembers(
										        inspect.getmodule(_config_class		)			,      lambda lowerCAmelCase__		: inspect.isclass(lowerCAmelCase__		)
										        and issubclass(lowerCAmelCase__			,      lowerCAmelCase__		)
										        and inspect.getmodule(lowerCAmelCase__		) == inspect.getmodule(_config_class		)			,      )
										]
										for config_class in config_classes_in_module:
															lowercase						     =						check_config_attributes_being_used(lowerCAmelCase__		)
															if len(lowerCAmelCase__		) > 0:
																				lowercase						     =						unused_attributes

					if len(lowerCAmelCase__		) > 0:
										lowercase						     =						'''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
										for name, attributes in configs_with_unused_attributes.items():
															error += f'{name}: {attributes}\n'

										raise ValueError(lowerCAmelCase__		)


if __name__ == "__main__":
							check_config_attributes()




