






import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE   =  {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
_SCREAMING_SNAKE_CASE   =  {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
_SCREAMING_SNAKE_CASE   =  {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

_SCREAMING_SNAKE_CASE   =  {
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
_SCREAMING_SNAKE_CASE   =  {
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
_SCREAMING_SNAKE_CASE   =  {
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


_SCREAMING_SNAKE_CASE   =  {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_SCREAMING_SNAKE_CASE   =  {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_SCREAMING_SNAKE_CASE   =  {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[str]					=VOCAB_FILES_NAMES
       __lowerCAmelCase    :		Union[str, Any]					=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       __lowerCAmelCase    :		Any					=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowerCAmelCase    :		Union[str, Any]					=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[str]					=VOCAB_FILES_NAMES
       __lowerCAmelCase    :		Optional[int]					=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       __lowerCAmelCase    :		Any					=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowerCAmelCase    :		Dict					=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

_SCREAMING_SNAKE_CASE   =  collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

_SCREAMING_SNAKE_CASE   =  collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


_SCREAMING_SNAKE_CASE   =  R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "






@add_start_docstrings(_a )
class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __call__(				self	:Optional[Any],      snake_case	:List[Any],      snake_case	:Optional[str] = None,      snake_case	:Optional[str] = None,      snake_case	:Union[bool, str] = False,      snake_case	:Union[bool, str] = False,      snake_case	:Optional[int] = None,      snake_case	:Optional[Union[str, TensorType]] = None,      snake_case	:Optional[bool] = None,      **snake_case	:Any,      ):



          """simple docstring"""

          if titles is None and texts is None:
             return super().__call__(
                 snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      return_tensors=snake_case,      return_attention_mask=snake_case,      **snake_case,      )
          elif titles is None or texts is None:
             _lowercase              =titles if texts is None else texts
             return super().__call__(
                 snake_case,      snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      return_tensors=snake_case,      return_attention_mask=snake_case,      **snake_case,      )
          _lowercase              =titles if not isinstance(snake_case,      snake_case) else [titles]
          _lowercase              =texts if not isinstance(snake_case,      snake_case) else [texts]
          _lowercase              =len(snake_case)
          _lowercase              =questions if not isinstance(snake_case,      snake_case) else [questions] * n_passages
          if len(snake_case) != len(snake_case):
             raise ValueError(
                 f'''There should be as many titles than texts but got {len(snake_case)} titles and {len(snake_case)} texts.''')
          _lowercase              =super().__call__(snake_case,      snake_case,      padding=snake_case,      truncation=snake_case)['input_ids']
          _lowercase              =super().__call__(snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case)['input_ids']
          _lowercase              ={
              'input_ids': [
                  (encoded_question_and_title + encoded_text)[:max_length]
                  if max_length is not None and truncation
                  else encoded_question_and_title + encoded_text
                  for encoded_question_and_title, encoded_text in zip(snake_case,      snake_case)
              ]
          }
          if return_attention_mask is not False:
             _lowercase              =[]
             for input_ids in encoded_inputs["input_ids"]:
                attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
             _lowercase              =attention_mask
          return self.pad(snake_case,      padding=snake_case,      max_length=snake_case,      return_tensors=snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:BatchEncoding,      snake_case	:DPRReaderOutput,      snake_case	:int = 16,      snake_case	:int = 64,      snake_case	:int = 4,      ):



          """simple docstring"""

          _lowercase              =reader_input['input_ids']
          _lowercase       ,       _lowercase       ,       _lowercase              =reader_output[:3]
          _lowercase              =len(snake_case)
          _lowercase              =sorted(range(snake_case),      reverse=snake_case,      key=relevance_logits.__getitem__)
          _lowercase              =[]
          for doc_id in sorted_docs:
             _lowercase              =list(input_ids[doc_id])
             # assuming question & title information is at the beginning of the sequence
             _lowercase              =sequence_ids.index(self.sep_token_id,      2) + 1  # second sep id
             if sequence_ids[-1] == self.pad_token_id:
                _lowercase              =sequence_ids.index(self.pad_token_id)
             else:
                _lowercase              =len(snake_case)

             _lowercase              =self._get_best_spans(
                 start_logits=start_logits[doc_id][passage_offset:sequence_len],      end_logits=end_logits[doc_id][passage_offset:sequence_len],      max_answer_length=snake_case,      top_spans=snake_case,      )
             for start_index, end_index in best_spans:
                start_index += passage_offset
                end_index += passage_offset
                nbest_spans_predictions.append(
                    DPRSpanPrediction(
                        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index],      relevance_score=relevance_logits[doc_id],      doc_id=snake_case,      start_index=snake_case,      end_index=snake_case,      text=self.decode(sequence_ids[start_index : end_index + 1]),      ))
             if len(snake_case) >= num_spans:
                break
          return nbest_spans_predictions[:num_spans]





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:List[int],      snake_case	:List[int],      snake_case	:int,      snake_case	:int,      ):



          """simple docstring"""

          _lowercase              =[]
          for start_index, start_score in enumerate(snake_case):
             for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
                scores.append(((start_index, start_index + answer_length), start_score + end_score))
          _lowercase              =sorted(snake_case,      key=lambda snake_case: x[1],      reverse=snake_case)
          _lowercase              =[]
          for (start_index, end_index), score in scores:
             if start_index > end_index:
                raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]''')
             _lowercase              =end_index - start_index + 1
             if length > max_answer_length:
                raise ValueError(f'''Span is too long: {length} > {max_answer_length}''')
             if any(
                 start_index <= prev_start_index <= prev_end_index <= end_index
                 or prev_start_index <= start_index <= end_index <= prev_end_index
                 for (prev_start_index, prev_end_index) in chosen_span_intervals):
                continue
             chosen_span_intervals.append((start_index, end_index))

             if len(snake_case) == top_spans:
                break
          return chosen_span_intervals






@add_end_docstrings(_a )
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						_a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[str]					=VOCAB_FILES_NAMES
       __lowerCAmelCase    :		str					=READER_PRETRAINED_VOCAB_FILES_MAP
       __lowerCAmelCase    :		int					=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowerCAmelCase    :		Tuple					=READER_PRETRAINED_INIT_CONFIGURATION
       __lowerCAmelCase    :		Optional[Any]					=['''input_ids''', '''attention_mask''']










import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =0





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
          self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
             _lowercase              =Path(snake_case) / 'preprocessor_config.json'
             _lowercase              =Path(snake_case) / 'config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'},      open(snake_case,      'w'),      )
             json.dump({'model_type': 'clip'},      open(snake_case,      'w'))

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
             self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
             _lowercase              =Path(snake_case) / 'preprocessor_config.json'
             _lowercase              =Path(snake_case) / 'config.json'
             json.dump(
                 {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'},      open(snake_case,      'w'),      )
             json.dump({'model_type': 'clip'},      open(snake_case,      'w'))

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
             self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
             _lowercase              =CLIPConfig()

             # Create a dummy config file with image_proceesor_type
             _lowercase              =Path(snake_case) / 'preprocessor_config.json'
             _lowercase              =Path(snake_case) / 'config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'},      open(snake_case,      'w'),      )
             json.dump({'model_type': 'clip'},      open(snake_case,      'w'))

             # remove image_processor_type to make sure config.json alone is enough to load image processor locally
             _lowercase              =AutoImageProcessor.from_pretrained(snake_case).to_dict()

             config_dict.pop('image_processor_type')
             _lowercase              =CLIPImageProcessor(**snake_case)

             # save in new folder
             model_config.save_pretrained(snake_case)
             config.save_pretrained(snake_case)

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)

             # make sure private variable is not incorrectly saved
             _lowercase              =json.loads(config.to_json_string())
             self.assertTrue('_processor_class' not in dict_as_saved)

          self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
             _lowercase              =Path(snake_case) / 'preprocessor_config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'},      open(snake_case,      'w'),      )

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
             self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          with self.assertRaisesRegex(
              snake_case,      'clip-base is not a local folder and is not a valid model identifier'):
             _lowercase              =AutoImageProcessor.from_pretrained('clip-base')





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          with self.assertRaisesRegex(
              snake_case,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
             _lowercase              =AutoImageProcessor.from_pretrained(snake_case,      revision='aaaaaa')





       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          with self.assertRaisesRegex(
              snake_case,      'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.',      ):
             _lowercase              =AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model')





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          with self.assertRaises(snake_case):
             _lowercase              =AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(snake_case):
             _lowercase              =AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor',      trust_remote_code=snake_case)

          _lowercase              =AutoImageProcessor.from_pretrained(
              'hf-internal-testing/test_dynamic_image_processor',      trust_remote_code=snake_case)
          self.assertEqual(image_processor.__class__.__name__,      'NewImageProcessor')

          # Test image processor can be reloaded.
          with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(snake_case)
             _lowercase              =AutoImageProcessor.from_pretrained(snake_case,      trust_remote_code=snake_case)
          self.assertEqual(reloaded_image_processor.__class__.__name__,      'NewImageProcessor')





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          try:
             AutoConfig.register('custom',      snake_case)
             AutoImageProcessor.register(snake_case,      snake_case)
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(snake_case):
                AutoImageProcessor.register(snake_case,      snake_case)

             with tempfile.TemporaryDirectory() as tmpdirname:
                _lowercase              =Path(snake_case) / 'preprocessor_config.json'
                _lowercase              =Path(snake_case) / 'config.json'
                json.dump(
                    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'},      open(snake_case,      'w'),      )
                json.dump({'model_type': 'clip'},      open(snake_case,      'w'))

                _lowercase              =CustomImageProcessor.from_pretrained(snake_case)

             # Now that the config is registered, it can be used as any other config with the auto-API
             with tempfile.TemporaryDirectory() as tmp_dir:
                image_processor.save_pretrained(snake_case)
                _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
                self.assertIsInstance(snake_case,      snake_case)

          finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          class 					SCREAMING_SNAKE_CASE_  (       _a ):
                 """simple docstring"""


                 __lowerCAmelCase    :		Union[str, Any]					=True

          try:
             AutoConfig.register('custom',      snake_case)
             AutoImageProcessor.register(snake_case,      snake_case)
             # If remote code is not set, the default is to use local
             _lowercase              =AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
             self.assertEqual(image_processor.__class__.__name__,      'NewImageProcessor')
             self.assertTrue(image_processor.is_local)

             # If remote code is disabled, we load the local one.
             _lowercase              =AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor',      trust_remote_code=snake_case)
             self.assertEqual(image_processor.__class__.__name__,      'NewImageProcessor')
             self.assertTrue(image_processor.is_local)

             # If remote is enabled, we load from the Hub
             _lowercase              =AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor',      trust_remote_code=snake_case)
             self.assertEqual(image_processor.__class__.__name__,      'NewImageProcessor')
             self.assertTrue(not hasattr(snake_case,      'is_local'))

          finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]


