

'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
_a					:			int							=    logging.get_logger(__name__)


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[str]    ):
  # initialize config
  if "resnet-50" in model_name:
    UpperCAmelCase    				=						ResNetConfig.from_pretrained('microsoft/resnet-50'    )
  elif "resnet-101" in model_name:
    UpperCAmelCase    				=						ResNetConfig.from_pretrained('microsoft/resnet-101'    )
  else:
    raise ValueError('Model name should include either resnet50 or resnet101'    )

  UpperCAmelCase    				=						DetrConfig(use_timm_backbone=__A							, backbone_config=__A    )

  # set label attributes
  UpperCAmelCase    				=						'''panoptic''' in model_name
  if is_panoptic:
    UpperCAmelCase    				=						250
  else:
    UpperCAmelCase    				=						91
    UpperCAmelCase    				=						'''huggingface/label-files'''
    UpperCAmelCase    				=						'''coco-detection-id2label.json'''
    UpperCAmelCase    				=						json.load(open(hf_hub_download(__A							, __A							, repo_type='dataset'    )							, 'r'    )    )
    UpperCAmelCase    				=						{int(__A    ): v for k, v in idalabel.items()}
    UpperCAmelCase    				=						idalabel
    UpperCAmelCase    				=						{v: k for k, v in idalabel.items()}

  return config, is_panoptic


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Any    ):
  # here we list all keys to be renamed (original name on the left, our name on the right)
  UpperCAmelCase    				=						[]

  # stem
  # fmt: off
  rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')    )
  rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')    )
  rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')    )
  rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')    )
  rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')    )
  # stages
  for stage_idx in range(len(config.backbone_config.depths    )    ):
    for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
      # shortcut
      if layer_idx == 0:
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
            )    )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
            )    )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
            )    )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
            )    )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
            )    )
      # 3 convs
      for i in range(3    ):
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
            )    )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
            )    )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
            )    )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
            )    )
        rename_keys.append(
            (
                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
            )    )
    # fmt: on

  for i in range(config.encoder_layers    ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (
            f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
            f'''encoder.layers.{i}.self_attn.out_proj.weight''',
        )    )
    rename_keys.append(
        (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias''')    )
    rename_keys.append(
        (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')    )
    rename_keys.append(
        (f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias''')    )
    rename_keys.append(
        (f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight''')    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias''')    )
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (
            f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
            f'''decoder.layers.{i}.self_attn.out_proj.weight''',
        )    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')    )
    rename_keys.append(
        (
            f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
            f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
        )    )
    rename_keys.append(
        (
            f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
            f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
        )    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias''')    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias''')    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight''')    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias''')    )

  # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
  rename_keys.extend(
      [
          ('input_proj.weight', 'input_projection.weight'),
          ('input_proj.bias', 'input_projection.bias'),
          ('query_embed.weight', 'query_position_embeddings.weight'),
          ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
          ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
          ('class_embed.weight', 'class_labels_classifier.weight'),
          ('class_embed.bias', 'class_labels_classifier.bias'),
          ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
          ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
          ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
          ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
          ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
          ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
      ]    )

  return rename_keys


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : int    ):
  UpperCAmelCase    				=						state_dict.pop(__A    )
  UpperCAmelCase    				=						val


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : List[str]=False    ):
  UpperCAmelCase    				=						''''''
  if is_panoptic:
    UpperCAmelCase    				=						'''detr.'''

  # first: transformer encoder
  for i in range(6    ):
    # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
    UpperCAmelCase    				=						state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''    )
    UpperCAmelCase    				=						state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''    )
    # next, add query, keys and values (in that order) to the state dict
    UpperCAmelCase    				=						in_proj_weight[:256, :]
    UpperCAmelCase    				=						in_proj_bias[:256]
    UpperCAmelCase    				=						in_proj_weight[256:512, :]
    UpperCAmelCase    				=						in_proj_bias[256:512]
    UpperCAmelCase    				=						in_proj_weight[-256:, :]
    UpperCAmelCase    				=						in_proj_bias[-256:]
  # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  for i in range(6    ):
    # read in weights + bias of input projection layer of self-attention
    UpperCAmelCase    				=						state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''    )
    UpperCAmelCase    				=						state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''    )
    # next, add query, keys and values (in that order) to the state dict
    UpperCAmelCase    				=						in_proj_weight[:256, :]
    UpperCAmelCase    				=						in_proj_bias[:256]
    UpperCAmelCase    				=						in_proj_weight[256:512, :]
    UpperCAmelCase    				=						in_proj_bias[256:512]
    UpperCAmelCase    				=						in_proj_weight[-256:, :]
    UpperCAmelCase    				=						in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    UpperCAmelCase    				=						state_dict.pop(
        f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''    )
    UpperCAmelCase    				=						state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''    )
    # next, add query, keys and values (in that order) of cross-attention to the state dict
    UpperCAmelCase    				=						in_proj_weight_cross_attn[:256, :]
    UpperCAmelCase    				=						in_proj_bias_cross_attn[:256]
    UpperCAmelCase    				=						in_proj_weight_cross_attn[256:512, :]
    UpperCAmelCase    				=						in_proj_bias_cross_attn[256:512]
    UpperCAmelCase    				=						in_proj_weight_cross_attn[-256:, :]
    UpperCAmelCase    				=						in_proj_bias_cross_attn[-256:]


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  UpperCAmelCase    				=						Image.open(requests.get(__A							, stream=__A    ).raw    )

  return im


@torch.no_grad()
def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Any							, SCREAMING_SNAKE_CASE   : List[str]=None							, SCREAMING_SNAKE_CASE   : Dict=False    ):
  UpperCAmelCase    				=						get_detr_config(__A    )

  # load original model from torch hub
  UpperCAmelCase    				=						{
      '''detr-resnet-50''': '''detr_resnet50''',
      '''detr-resnet-101''': '''detr_resnet101''',
  }
  logger.info(f'''Converting model {model_name}...'''    )
  UpperCAmelCase    				=						torch.hub.load('facebookresearch/detr'							, model_name_to_original_name[model_name]							, pretrained=__A    ).eval()
  UpperCAmelCase    				=						detr.state_dict()
  # rename keys
  for src, dest in create_rename_keys(__A    ):
    if is_panoptic:
      UpperCAmelCase    				=						'''detr.''' + src
    rename_key(__A							, __A							, __A    )
  # query, key and value matrices need special treatment
  read_in_q_k_v(__A							, is_panoptic=__A    )
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  UpperCAmelCase    				=						'''detr.model.''' if is_panoptic else '''model.'''
  for key in state_dict.copy().keys():
    if is_panoptic:
      if (
          key.startswith('detr'    )
          and not key.startswith('class_labels_classifier'    )
          and not key.startswith('bbox_predictor'    )
      ):
        UpperCAmelCase    				=						state_dict.pop(__A    )
        UpperCAmelCase    				=						val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        UpperCAmelCase    				=						state_dict.pop(__A    )
        UpperCAmelCase    				=						val
      elif key.startswith('bbox_attention'    ) or key.startswith('mask_head'    ):
        continue
      else:
        UpperCAmelCase    				=						state_dict.pop(__A    )
        UpperCAmelCase    				=						val
    else:
      if not key.startswith('class_labels_classifier'    ) and not key.startswith('bbox_predictor'    ):
        UpperCAmelCase    				=						state_dict.pop(__A    )
        UpperCAmelCase    				=						val

    # finally, create HuggingFace model and load state dict
  UpperCAmelCase    				=						DetrForSegmentation(__A    ) if is_panoptic else DetrForObjectDetection(__A    )
  model.load_state_dict(__A    )
  model.eval()

  # verify our conversion on an image
  UpperCAmelCase    				=						'''coco_panoptic''' if is_panoptic else '''coco_detection'''
  UpperCAmelCase    				=						DetrImageProcessor(format=__A    )

  UpperCAmelCase    				=						processor(images=prepare_img()							, return_tensors='pt'    )
  UpperCAmelCase    				=						encoding['''pixel_values''']

  UpperCAmelCase    				=						detr(__A    )
  UpperCAmelCase    				=						model(__A    )

  assert torch.allclose(outputs.logits							, original_outputs['pred_logits']							, atol=1E-3    )
  assert torch.allclose(outputs.pred_boxes							, original_outputs['pred_boxes']							, atol=1E-3    )
  if is_panoptic:
    assert torch.allclose(outputs.pred_masks							, original_outputs['pred_masks']							, atol=1E-4    )
  print('Looks ok!'    )

  if pytorch_dump_folder_path is not None:
    # Save model and image processor
    logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''    )
    Path(__A    ).mkdir(exist_ok=__A    )
    model.save_pretrained(__A    )
    processor.save_pretrained(__A    )

  if push_to_hub:
    # Upload model and image processor to the hub
    logger.info('Uploading PyTorch model and image processor to the hub...'    )
    model.push_to_hub(f'''nielsr/{model_name}'''    )
    processor.push_to_hub(f'''nielsr/{model_name}'''    )


if __name__ == "__main__":
  _a					:			int							=    argparse.ArgumentParser()

  parser.add_argument(
      '--model_name',
      default='detr-resnet-50',
      type=str,
      choices=['detr-resnet-50', 'detr-resnet-101'],
      help='Name of the DETR model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
  _a					:			Optional[int]							=    parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''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()




