

'''simple docstring'''




import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

  if is_vision_available():
    from transformers import OneFormerImageProcessor
    from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
    from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
  from PIL import Image


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : Tuple="shi-labs/oneformer_demo"    ):
  with open(hf_hub_download(__A							, __A							, repo_type='dataset'    )							, 'r'    ) as f:
    UpperCAmelCase    				=						json.load(__A    )
  UpperCAmelCase    				=						{}
  UpperCAmelCase    				=						[]
  UpperCAmelCase    				=						[]
  for key, info in class_info.items():
    UpperCAmelCase    				=						info['''name''']
    class_names.append(info['name']    )
    if info["isthing"]:
      thing_ids.append(int(__A    )    )
  UpperCAmelCase    				=						thing_ids
  UpperCAmelCase    				=						class_names
  return metadata



class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_=7 ,			a_=3 ,			a_=3_0 ,			a_=4_0_0 ,			a_=None ,			a_=True ,			a_=True ,			a_=[0.5, 0.5, 0.5] ,			a_=[0.5, 0.5, 0.5] ,			a_=1_0 ,			a_=False ,			a_=2_5_5 ,			a_="shi-labs/oneformer_demo" ,			a_="ade20k_panoptic.json" ,			a_=1_0 ,			)				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						parent
    UpperCAmelCase    				=						batch_size
    UpperCAmelCase    				=						num_channels
    UpperCAmelCase    				=						min_resolution
    UpperCAmelCase    				=						max_resolution
    UpperCAmelCase    				=						do_resize
    UpperCAmelCase    				=						{'''shortest_edge''': 3_2, '''longest_edge''': 1_3_3_3} if size is None else size
    UpperCAmelCase    				=						do_normalize
    UpperCAmelCase    				=						image_mean
    UpperCAmelCase    				=						image_std
    UpperCAmelCase    				=						class_info_file
    UpperCAmelCase    				=						prepare_metadata(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE  )
    UpperCAmelCase    				=						num_text
    UpperCAmelCase    				=						repo_path

    # for the post_process_functions
    UpperCAmelCase    				=						2
    UpperCAmelCase    				=						1_0
    UpperCAmelCase    				=						1_0
    UpperCAmelCase    				=						3
    UpperCAmelCase    				=						4
    UpperCAmelCase    				=						num_labels
    UpperCAmelCase    				=						do_reduce_labels
    UpperCAmelCase    				=						ignore_index
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "num_labels": self.num_labels,
        "do_reduce_labels": self.do_reduce_labels,
        "ignore_index": self.ignore_index,
        "class_info_file": self.class_info_file,
        "metadata": self.metadata,
        "num_text": self.num_text,
    }
  def     snake_case_      (  self ,			a_ ,			a_=False  )				->					Optional[Any]:


    """simple docstring"""
    if not batched:
      UpperCAmelCase    				=						image_inputs[0]
      if isinstance(__SCREAMING_SNAKE_CASE ,			Image.Image  ):
        UpperCAmelCase    				=						image.size
      else:
        UpperCAmelCase    				=						image.shape[1], image.shape[2]
      if w < h:
        UpperCAmelCase    				=						int(self.size['shortest_edge'] * h / w  )
        UpperCAmelCase    				=						self.size['''shortest_edge''']
      elif w > h:
        UpperCAmelCase    				=						self.size['''shortest_edge''']
        UpperCAmelCase    				=						int(self.size['shortest_edge'] * w / h  )
      else:
        UpperCAmelCase    				=						self.size['''shortest_edge''']
        UpperCAmelCase    				=						self.size['''shortest_edge''']

    else:
      UpperCAmelCase    				=						[]
      for image in image_inputs:
        UpperCAmelCase    				=						self.get_expected_values([image]  )
        expected_values.append((expected_height, expected_width)  )
      UpperCAmelCase    				=						max(__SCREAMING_SNAKE_CASE ,			key=lambda a_  : item[0]  )[0]
      UpperCAmelCase    				=						max(__SCREAMING_SNAKE_CASE ,			key=lambda a_  : item[1]  )[1]

    return expected_height, expected_width





  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    return OneFormerForUniversalSegmentationOutput(
        # +1 for null class
        class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)  ) ,			masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)  ) ,			)




@require_torch
@require_vision
class 							lowercase_						(		SCREAMING_SNAKE_CASE_			,							unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			List[Any]     	= OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  __lowerCAmelCase       :			Union[str, Any]     	= image_processing_class
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase    				=						OneFormerImageProcessorTester(self  )
  @property
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    return self.image_processing_tester.prepare_image_processor_dict()
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,			'image_mean'  )  )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,			'image_std'  )  )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,			'do_normalize'  )  )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,			'do_resize'  )  )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,			'size'  )  )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,			'ignore_index'  )  )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,			'class_info_file'  )  )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,			'num_text'  )  )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,			'repo_path'  )  )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,			'metadata'  )  )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,			'do_reduce_labels'  )  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    pass
  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    # Initialize image_processor
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    UpperCAmelCase    				=						prepare_image_inputs(self.image_processing_tester ,			equal_resolution=__SCREAMING_SNAKE_CASE  )
    for image in image_inputs:
      self.assertIsInstance(__SCREAMING_SNAKE_CASE ,			Image.Image  )

    # Test not batched input
    UpperCAmelCase    				=						image_processor(image_inputs[0] ,			['semantic'] ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase    				=						self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE  )

    self.assertEqual(
        encoded_images.shape ,			(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,			)

    # Test batched
    UpperCAmelCase    				=						self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE ,			batched=__SCREAMING_SNAKE_CASE  )

    UpperCAmelCase    				=						image_processor(
        __SCREAMING_SNAKE_CASE ,			['semantic'] * len(__SCREAMING_SNAKE_CASE  ) ,			return_tensors='pt'  ).pixel_values
    self.assertEqual(
        encoded_images.shape ,			(
            self.image_processing_tester.batch_size,
            self.image_processing_tester.num_channels,
            expected_height,
            expected_width,
        ) ,			)
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    # Initialize image_processor
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    UpperCAmelCase    				=						prepare_image_inputs(self.image_processing_tester ,			equal_resolution=__SCREAMING_SNAKE_CASE ,			numpify=__SCREAMING_SNAKE_CASE  )
    for image in image_inputs:
      self.assertIsInstance(__SCREAMING_SNAKE_CASE ,			np.ndarray  )

    # Test not batched input
    UpperCAmelCase    				=						image_processor(image_inputs[0] ,			['semantic'] ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase    				=						self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE  )

    self.assertEqual(
        encoded_images.shape ,			(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,			)

    # Test batched
    UpperCAmelCase    				=						self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE ,			batched=__SCREAMING_SNAKE_CASE  )

    UpperCAmelCase    				=						image_processor(
        __SCREAMING_SNAKE_CASE ,			['semantic'] * len(__SCREAMING_SNAKE_CASE  ) ,			return_tensors='pt'  ).pixel_values
    self.assertEqual(
        encoded_images.shape ,			(
            self.image_processing_tester.batch_size,
            self.image_processing_tester.num_channels,
            expected_height,
            expected_width,
        ) ,			)
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    # Initialize image_processor
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    UpperCAmelCase    				=						prepare_image_inputs(self.image_processing_tester ,			equal_resolution=__SCREAMING_SNAKE_CASE ,			torchify=__SCREAMING_SNAKE_CASE  )
    for image in image_inputs:
      self.assertIsInstance(__SCREAMING_SNAKE_CASE ,			torch.Tensor  )

    # Test not batched input
    UpperCAmelCase    				=						image_processor(image_inputs[0] ,			['semantic'] ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase    				=						self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE  )

    self.assertEqual(
        encoded_images.shape ,			(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,			)

    # Test batched
    UpperCAmelCase    				=						self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE ,			batched=__SCREAMING_SNAKE_CASE  )

    UpperCAmelCase    				=						image_processor(
        __SCREAMING_SNAKE_CASE ,			['semantic'] * len(__SCREAMING_SNAKE_CASE  ) ,			return_tensors='pt'  ).pixel_values
    self.assertEqual(
        encoded_images.shape ,			(
            self.image_processing_tester.batch_size,
            self.image_processing_tester.num_channels,
            expected_height,
            expected_width,
        ) ,			)
  def     snake_case_      (  self ,			a_=False ,			a_=False ,			a_="np"  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    # prepare image and target
    UpperCAmelCase    				=						self.image_processing_tester.num_labels
    UpperCAmelCase    				=						None
    UpperCAmelCase    				=						None
    UpperCAmelCase    				=						prepare_image_inputs(self.image_processing_tester ,			equal_resolution=__SCREAMING_SNAKE_CASE  )
    if with_segmentation_maps:
      UpperCAmelCase    				=						num_labels
      if is_instance_map:
        UpperCAmelCase    				=						list(range(__SCREAMING_SNAKE_CASE  )  ) * 2
        UpperCAmelCase    				=						dict(enumerate(__SCREAMING_SNAKE_CASE  )  )
      UpperCAmelCase    				=						[
          np.random.randint(0 ,			high * 2 ,			(img.size[1], img.size[0])  ).astype(np.uinta  ) for img in image_inputs
      ]
      if segmentation_type == "pil":
        UpperCAmelCase    				=						[Image.fromarray(__SCREAMING_SNAKE_CASE  ) for annotation in annotations]

    UpperCAmelCase    				=						image_processor(
        __SCREAMING_SNAKE_CASE ,			['semantic'] * len(__SCREAMING_SNAKE_CASE  ) ,			__SCREAMING_SNAKE_CASE ,			return_tensors='pt' ,			instance_id_to_semantic_id=__SCREAMING_SNAKE_CASE ,			pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE ,			)

    return inputs
  def     snake_case_      (  self  )				->					Optional[Any]:


    """simple docstring"""
    pass
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    def common(a_=False ,			a_=None  ):
      UpperCAmelCase    				=						self.comm_get_image_processor_inputs(
          with_segmentation_maps=__SCREAMING_SNAKE_CASE ,			is_instance_map=__SCREAMING_SNAKE_CASE ,			segmentation_type=__SCREAMING_SNAKE_CASE  )

      UpperCAmelCase    				=						inputs['''mask_labels''']
      UpperCAmelCase    				=						inputs['''class_labels''']
      UpperCAmelCase    				=						inputs['''pixel_values''']
      UpperCAmelCase    				=						inputs['''text_inputs''']

      # check the batch_size
      for mask_label, class_label, text_input in zip(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE  ):
        self.assertEqual(mask_label.shape[0] ,			class_label.shape[0]  )
        # this ensure padding has happened
        self.assertEqual(mask_label.shape[1:] ,			pixel_values.shape[2:]  )
        self.assertEqual(len(__SCREAMING_SNAKE_CASE  ) ,			self.image_processing_tester.num_text  )

    common()
    common(is_instance_map=__SCREAMING_SNAKE_CASE  )
    common(is_instance_map=__SCREAMING_SNAKE_CASE ,			segmentation_type='pil'  )
    common(is_instance_map=__SCREAMING_SNAKE_CASE ,			segmentation_type='pil'  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						np.zeros((2_0, 5_0)  )
    UpperCAmelCase    				=						1
    UpperCAmelCase    				=						1
    UpperCAmelCase    				=						1

    UpperCAmelCase    				=						binary_mask_to_rle(__SCREAMING_SNAKE_CASE  )
    self.assertEqual(len(__SCREAMING_SNAKE_CASE  ) ,			4  )
    self.assertEqual(rle[0] ,			2_1  )
    self.assertEqual(rle[1] ,			4_5  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes ,			max_seq_length=7_7 ,			task_seq_length=7_7 ,			class_info_file='ade20k_panoptic.json' ,			num_text=self.image_processing_tester.num_text ,			repo_path='shi-labs/oneformer_demo' ,			)
    UpperCAmelCase    				=						self.image_processing_tester.get_fake_oneformer_outputs()

    UpperCAmelCase    				=						fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE  )

    self.assertEqual(len(__SCREAMING_SNAKE_CASE  ) ,			self.image_processing_tester.batch_size  )
    self.assertEqual(
        segmentation[0].shape ,			(
            self.image_processing_tester.height,
            self.image_processing_tester.width,
        ) ,			)

    UpperCAmelCase    				=						[(1, 4) for i in range(self.image_processing_tester.batch_size  )]
    UpperCAmelCase    				=						fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE ,			target_sizes=__SCREAMING_SNAKE_CASE  )

    self.assertEqual(segmentation[0].shape ,			target_sizes[0]  )
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes ,			max_seq_length=7_7 ,			task_seq_length=7_7 ,			class_info_file='ade20k_panoptic.json' ,			num_text=self.image_processing_tester.num_text ,			repo_path='shi-labs/oneformer_demo' ,			)
    UpperCAmelCase    				=						self.image_processing_tester.get_fake_oneformer_outputs()
    UpperCAmelCase    				=						image_processor.post_process_instance_segmentation(__SCREAMING_SNAKE_CASE ,			threshold=0  )

    self.assertTrue(len(__SCREAMING_SNAKE_CASE  ) == self.image_processing_tester.batch_size  )
    for el in segmentation:
      self.assertTrue('segmentation' in el  )
      self.assertTrue('segments_info' in el  )
      self.assertEqual(type(el['segments_info']  ) ,			__SCREAMING_SNAKE_CASE  )
      self.assertEqual(
          el['segmentation'].shape ,			(self.image_processing_tester.height, self.image_processing_tester.width)  )





  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes ,			max_seq_length=7_7 ,			task_seq_length=7_7 ,			class_info_file='ade20k_panoptic.json' ,			num_text=self.image_processing_tester.num_text ,			repo_path='shi-labs/oneformer_demo' ,			)
    UpperCAmelCase    				=						self.image_processing_tester.get_fake_oneformer_outputs()
    UpperCAmelCase    				=						image_processor.post_process_panoptic_segmentation(__SCREAMING_SNAKE_CASE ,			threshold=0  )

    self.assertTrue(len(__SCREAMING_SNAKE_CASE  ) == self.image_processing_tester.batch_size  )
    for el in segmentation:
      self.assertTrue('segmentation' in el  )
      self.assertTrue('segments_info' in el  )
      self.assertEqual(type(el['segments_info']  ) ,			__SCREAMING_SNAKE_CASE  )
      self.assertEqual(
          el['segmentation'].shape ,			(self.image_processing_tester.height, self.image_processing_tester.width)  )










'''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
__lowerCAmelCase									=   'src/transformers'


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

__lowerCAmelCase									=   transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowerCAmelCase									=   {
    # 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 (				__A				: Optional[Any]    ,		__A				: List[str]    ,		__A				: List[str]    ,		__A				: Union[str, Any]   ):
				a_		:	List[str]									=   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
												):
																a_		:	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}"'    ,		__A    ,		)
												    is not None
												):
																a_		:	Union[str, 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:
																				a_		:	Any									=   True
												if attribute_used:
																break
								if attribute_used:
												break

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

				# Special cases to be allowed
				a_		:	Optional[int]									=   True
				if not attribute_used:
								a_		:	Optional[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:
																a_		:	Dict									=   True
												elif attribute in ["tie_word_embeddings"] and default_value is False:
																a_		:	List[str]									=   True

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

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

				return attribute_used or case_allowed





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

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

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

				a_		:	List[Any]									=   []
				for config_param, default_value in zip(__A    ,		__A   ):
								# `attributes` here is all the variant names for `config_param`
								a_		:	int									=   [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(__A    ,		__A    ,		__A    ,		__A   ):
												unused_attributes.append(attributes[0]   )

				return sorted(__A   )





def 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   {}
				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.)
								a_		:	int									=   [
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class   )    ,		lambda __A   : inspect.isclass(__A   )
								        and issubclass(__A    ,		__A   )
								        and inspect.getmodule(__A   ) == inspect.getmodule(_config_class   )    ,		)
								]
								for config_class in config_classes_in_module:
												a_		:	int									=   check_config_attributes_being_used(__A   )
												if len(__A   ) > 0:
																a_		:	Dict									=   unused_attributes

				if len(__A   ) > 0:
								a_		:	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(__A   )


if __name__ == "__main__":
		check_config_attributes()




