




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import OwlViTImageProcessor, OwlViTProcessor





@require_vision
class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						tempfile.mkdtemp()

             # fmt: off
             __lowerCamelCase   =						["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
             # fmt: on
             __lowerCamelCase   =						dict(zip(UpperCamelCase_    ,       range(len(UpperCamelCase_   )   )   )   )
             __lowerCamelCase   =						["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
             __lowerCamelCase   =						{"""unk_token""": """<unk>"""}

             __lowerCamelCase   =						os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES["""vocab_file"""]   )
             __lowerCamelCase   =						os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES["""merges_file"""]   )
             with open(self.vocab_file    ,       """w"""    ,       encoding="""utf-8"""   ) as fp:
                    fp.write(json.dumps(UpperCamelCase_   ) + """\n"""   )
             with open(self.merges_file    ,       """w"""    ,       encoding="""utf-8"""   ) as fp:
                    fp.write("""\n""".join(UpperCamelCase_   )   )

             __lowerCamelCase   =						{
                 """do_resize""": True,
                 """size""": 20,
                 """do_center_crop""": True,
                 """crop_size""": 18,
                 """do_normalize""": True,
                 """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
                 """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
             }
             __lowerCamelCase   =						os.path.join(self.tmpdirname    ,       UpperCamelCase_   )
             with open(self.image_processor_file    ,       """w"""    ,       encoding="""utf-8"""   ) as fp:
                    json.dump(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]    ,       **UpperCamelCase_: str   ):
             return CLIPTokenizer.from_pretrained(self.tmpdirname    ,       pad_token="""!"""    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       **UpperCamelCase_: str   ):
             return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,       pad_token="""!"""    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       **UpperCamelCase_: List[str]   ):
             return OwlViTImageProcessor.from_pretrained(self.tmpdirname    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int   ):
             shutil.rmtree(self.tmpdirname   )
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						[np.random.randint(2_55    ,       size=(3, 30, 4_00)    ,       dtype=np.uinta   )]

             __lowerCamelCase   =						[Image.fromarray(np.moveaxis(UpperCamelCase_    ,       0    ,       -1   )   ) for x in image_inputs]

             return image_inputs
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						self.get_tokenizer()
             __lowerCamelCase   =						self.get_rust_tokenizer()
             __lowerCamelCase   =						self.get_image_processor()

             __lowerCamelCase   =						OwlViTProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )
             processor_slow.save_pretrained(self.tmpdirname   )
             __lowerCamelCase   =						OwlViTProcessor.from_pretrained(self.tmpdirname    ,       use_fast=UpperCamelCase_   )

             __lowerCamelCase   =						OwlViTProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )
             processor_fast.save_pretrained(self.tmpdirname   )
             __lowerCamelCase   =						OwlViTProcessor.from_pretrained(self.tmpdirname   )

             self.assertEqual(processor_slow.tokenizer.get_vocab()    ,       tokenizer_slow.get_vocab()   )
             self.assertEqual(processor_fast.tokenizer.get_vocab()    ,       tokenizer_fast.get_vocab()   )
             self.assertEqual(tokenizer_slow.get_vocab()    ,       tokenizer_fast.get_vocab()   )
             self.assertIsInstance(processor_slow.tokenizer    ,       UpperCamelCase_   )
             self.assertIsInstance(processor_fast.tokenizer    ,       UpperCamelCase_   )

             self.assertEqual(processor_slow.image_processor.to_json_string()    ,       image_processor.to_json_string()   )
             self.assertEqual(processor_fast.image_processor.to_json_string()    ,       image_processor.to_json_string()   )
             self.assertIsInstance(processor_slow.image_processor    ,       UpperCamelCase_   )
             self.assertIsInstance(processor_fast.image_processor    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						OwlViTProcessor(tokenizer=self.get_tokenizer()    ,       image_processor=self.get_image_processor()   )
             processor.save_pretrained(self.tmpdirname   )

             __lowerCamelCase   =						self.get_tokenizer(bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""   )
             __lowerCamelCase   =						self.get_image_processor(do_normalize=UpperCamelCase_   )

             __lowerCamelCase   =						OwlViTProcessor.from_pretrained(
                 self.tmpdirname    ,       bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    ,       do_normalize=UpperCamelCase_   )

             self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer_add_kwargs.get_vocab()   )
             self.assertIsInstance(processor.tokenizer    ,       UpperCamelCase_   )

             self.assertEqual(processor.image_processor.to_json_string()    ,       image_processor_add_kwargs.to_json_string()   )
             self.assertIsInstance(processor.image_processor    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						OwlViTProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

             __lowerCamelCase   =						self.prepare_image_inputs()

             __lowerCamelCase   =						image_processor(UpperCamelCase_    ,       return_tensors="""np"""   )
             __lowerCamelCase   =						processor(images=UpperCamelCase_    ,       return_tensors="""np"""   )

             for key in input_image_proc.keys():
                    self.assertAlmostEqual(input_image_proc[key].sum()    ,       input_processor[key].sum()    ,       delta=1E-2   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						OwlViTProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

             __lowerCamelCase   =						"""lower newer"""

             __lowerCamelCase   =						processor(text=UpperCamelCase_    ,       return_tensors="""np"""   )

             __lowerCamelCase   =						tokenizer(UpperCamelCase_    ,       return_tensors="""np"""   )

             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key][0].tolist()    ,       encoded_processor[key][0].tolist()   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						OwlViTProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

             __lowerCamelCase   =						"""lower newer"""
             __lowerCamelCase   =						self.prepare_image_inputs()

             __lowerCamelCase   =						processor(text=UpperCamelCase_    ,       images=UpperCamelCase_   )

             self.assertListEqual(list(inputs.keys()   )    ,       ["""input_ids""", """attention_mask""", """pixel_values"""]   )

             # test if it raises when no input is passed
             with pytest.raises(UpperCamelCase_   ):
                    processor()
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __lowerCamelCase   =						"""google/owlvit-base-patch32"""
             __lowerCamelCase   =						OwlViTProcessor.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						["""cat""", """nasa badge"""]
             __lowerCamelCase   =						processor(text=UpperCamelCase_   )

             __lowerCamelCase   =						16
             self.assertListEqual(list(inputs.keys()   )    ,       ["""input_ids""", """attention_mask"""]   )
             self.assertEqual(inputs["""input_ids"""].shape    ,       (2, seq_length)   )

             # test if it raises when no input is passed
             with pytest.raises(UpperCamelCase_   ):
                    processor()
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						"""google/owlvit-base-patch32"""
             __lowerCamelCase   =						OwlViTProcessor.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						[["""cat""", """nasa badge"""], ["""person"""]]
             __lowerCamelCase   =						processor(text=UpperCamelCase_   )

             __lowerCamelCase   =						16
             __lowerCamelCase   =						len(UpperCamelCase_   )
             __lowerCamelCase   =						max([len(UpperCamelCase_   ) for texts in input_texts]   )

             self.assertListEqual(list(inputs.keys()   )    ,       ["""input_ids""", """attention_mask"""]   )
             self.assertEqual(inputs["""input_ids"""].shape    ,       (batch_size * num_max_text_queries, seq_length)   )

             # test if it raises when no input is passed
             with pytest.raises(UpperCamelCase_   ):
                    processor()
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						"""google/owlvit-base-patch32"""
             __lowerCamelCase   =						OwlViTProcessor.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						["""cat""", """nasa badge"""]
             __lowerCamelCase   =						processor(text=UpperCamelCase_   )

             __lowerCamelCase   =						16
             __lowerCamelCase   =						inputs["""input_ids"""]
             __lowerCamelCase   =						[
                 [4_94_06, 23_68, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                 [4_94_06, 68_41, 1_13_01, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
             ]

             self.assertListEqual(list(inputs.keys()   )    ,       ["""input_ids""", """attention_mask"""]   )
             self.assertEqual(inputs["""input_ids"""].shape    ,       (2, seq_length)   )
             self.assertListEqual(list(input_ids[0]   )    ,       predicted_ids[0]   )
             self.assertListEqual(list(input_ids[1]   )    ,       predicted_ids[1]   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						OwlViTProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

             __lowerCamelCase   =						self.prepare_image_inputs()
             __lowerCamelCase   =						self.prepare_image_inputs()

             __lowerCamelCase   =						processor(images=UpperCamelCase_    ,       query_images=UpperCamelCase_   )

             self.assertListEqual(list(inputs.keys()   )    ,       ["""query_pixel_values""", """pixel_values"""]   )

             # test if it raises when no input is passed
             with pytest.raises(UpperCamelCase_   ):
                    processor()




      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						OwlViTProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

             __lowerCamelCase   =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             __lowerCamelCase   =						processor.batch_decode(UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.batch_decode(UpperCamelCase_   )

             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )






'''simple docstring'''






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
A_							: List[str]						 =	"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
A_							: Optional[Any]						 =	direct_transformers_import(PATH_TO_TRANSFORMERS)

A_							: Union[str, Any]						 =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

A_							: Dict						 =	{
    # 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     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Dict:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = 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
                     ):
                            _UpperCAmelCase  :						Tuple			     = 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
                     ):
                            _UpperCAmelCase  :						Any			     = 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:
                                   _UpperCAmelCase  :						List[str]			     = True
                     if attribute_used:
                            break
              if attribute_used:
                     break

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

       # Special cases to be allowed
       _UpperCAmelCase  :						Optional[Any]			     = True
       if not attribute_used:
              _UpperCAmelCase  :						List[Any]			     = 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:
                            _UpperCAmelCase  :						Tuple			     = True
                     elif attribute in ["tie_word_embeddings"] and default_value is False:
                            _UpperCAmelCase  :						Any			     = True

                     # Allow cases without checking the default value in the configuration class
                     elif attribute in attributes_to_allow + attributes_used_in_generation:
                            _UpperCAmelCase  :						Dict			     = True
                     elif attribute.endswith("""_token_id"""		):
                            _UpperCAmelCase  :						Optional[int]			     = True

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

       return attribute_used or case_allowed




def     snake_case_					(     lowerCAmelCase_		)->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[int]			     = dict(inspect.signature(config_class.__init__		).parameters		)
       _UpperCAmelCase  :						Optional[int]			     = [x for x in list(signature.keys()		) if x not in ["""self""", """kwargs"""]]
       _UpperCAmelCase  :						Optional[int]			     = [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
       _UpperCAmelCase  :						List[Any]			     = {}
       if len(config_class.attribute_map		) > 0:
              _UpperCAmelCase  :						Optional[int]			     = {v: k for k, v in config_class.attribute_map.items()}

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

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

       _UpperCAmelCase  :						Any			     = []
       for config_param, default_value in zip(lowerCAmelCase_       ,   lowerCAmelCase_		):
              # `attributes` here is all the variant names for `config_param`
              _UpperCAmelCase  :						List[str]			     = [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     snake_case_					(     )->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = {}
       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.)
              _UpperCAmelCase  :						List[Any]			     = [
                  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:
                     _UpperCAmelCase  :						Optional[int]			     = check_config_attributes_being_used(lowerCAmelCase_		)
                     if len(lowerCAmelCase_		) > 0:
                            _UpperCAmelCase  :						Tuple			     = unused_attributes

       if len(lowerCAmelCase_		) > 0:
              _UpperCAmelCase  :						Dict			     = """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()



