

'''simple docstring'''
import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




def 	UpperCamelCase   (       lowercase_						:    List[str]	)		->	int:




  '''simple docstring'''



  return 1.0 / (1.0 + np.exp(-_outputs	))




def 	UpperCamelCase   (       lowercase_						:    Any	)		->	List[Any]:




  '''simple docstring'''



  lowercase									=np.max(_outputs     ,					axis=-1     ,					keepdims=lowercase_	)
  lowercase									=np.exp(_outputs - maxes	)
  return shifted_exp / shifted_exp.sum(axis=-1     ,					keepdims=lowercase_	)

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       'sigmoid'
 UpperCamelCase__     =       'softmax'
 UpperCamelCase__     =       'none'

@add_end_docstrings(
    __SCREAMING_SNAKE_CASE ,				r'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    ' ,				)
class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       False
 UpperCamelCase__     =       ClassificationFunction.NONE







 def __init__(       self  , **snake_case_   ):
   super().__init__(**snake_case_   )

   self.check_model_type(
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       if self.framework == '''tf'''
       else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING   )







 def 						_A(       self  , snake_case_=None  , snake_case_=None  , snake_case_=""  , **snake_case_   ):
   # Using "" as default argument because we're going to use `top_k=None` in user code to declare
   # "No top_k"
   lowercase									=tokenizer_kwargs

   lowercase									={}
   if hasattr(self.model.config  , '''return_all_scores'''   ) and return_all_scores is None:
     lowercase									=self.model.config.return_all_scores

   if isinstance(snake_case_  , snake_case_   ) or top_k is None:
     lowercase									=top_k
     lowercase									=False
   elif return_all_scores is not None:
     warnings.warn(
         '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
         ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''  , snake_case_  , )
     if return_all_scores:
       lowercase									=None
     else:
       lowercase									=1

   if isinstance(snake_case_  , snake_case_   ):
     lowercase									=ClassificationFunction[function_to_apply.upper()]

   if function_to_apply is not None:
     lowercase									=function_to_apply
   return preprocess_params, {}, postprocess_params







 def __call__(       self  , *snake_case_  , **snake_case_   ):
   lowercase									=super().__call__(*snake_case_  , **snake_case_   )
   # TODO try and retrieve it in a nicer way from _sanitize_parameters.
   lowercase									='''top_k''' not in kwargs
   if isinstance(args[0]  , snake_case_   ) and _legacy:
     # This pipeline is odd, and return a list when single item is run
     return [result]
   else:
     return result







 def 						_A(       self  , snake_case_  , **snake_case_   ):
   lowercase									=self.framework
   if isinstance(snake_case_  , snake_case_   ):
     return self.tokenizer(**snake_case_  , return_tensors=snake_case_  , **snake_case_   )
   elif isinstance(snake_case_  , snake_case_   ) and len(snake_case_   ) == 1 and isinstance(inputs[0]  , snake_case_   ) and len(inputs[0]   ) == 2:
     # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
     return self.tokenizer(
         text=inputs[0][0]  , text_pair=inputs[0][1]  , return_tensors=snake_case_  , **snake_case_   )
   elif isinstance(snake_case_  , snake_case_   ):
     # This is likely an invalid usage of the pipeline attempting to pass text pairs.
     raise ValueError(
         '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
         ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''   )
   return self.tokenizer(snake_case_  , return_tensors=snake_case_  , **snake_case_   )







 def 						_A(       self  , snake_case_   ):
   return self.model(**snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_=None  , snake_case_=1  , snake_case_=True   ):
   # `_legacy` is used to determine if we're running the naked pipeline and in backward
   # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
   # the more natural result containing the list.
   # Default value before `set_parameters`
   if function_to_apply is None:
     if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
       lowercase									=ClassificationFunction.SIGMOID
     elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
       lowercase									=ClassificationFunction.SOFTMAX
     elif hasattr(self.model.config  , '''function_to_apply'''   ) and function_to_apply is None:
       lowercase									=self.model.config.function_to_apply
     else:
       lowercase									=ClassificationFunction.NONE

   lowercase									=model_outputs['''logits'''][0]
   lowercase									=outputs.numpy()

   if function_to_apply == ClassificationFunction.SIGMOID:
     lowercase									=sigmoid(snake_case_   )
   elif function_to_apply == ClassificationFunction.SOFTMAX:
     lowercase									=softmax(snake_case_   )
   elif function_to_apply == ClassificationFunction.NONE:
     lowercase									=outputs
   else:
     raise ValueError(f'Unrecognized `function_to_apply` argument: {function_to_apply}'   )

   if top_k == 1 and _legacy:
     return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

   lowercase									=[
       {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(snake_case_   )
   ]
   if not _legacy:
     dict_scores.sort(key=lambda snake_case_   : x["score"]  , reverse=snake_case_   )
     if top_k is not None:
       lowercase									=dict_scores[:top_k]
   return dict_scores






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


__a							     =					logging.get_logger(__name__)

__a							     =					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__a							     =					{
    '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'
        ),
    },
}
__a							     =					{
    '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'
        ),
    },
}
__a							     =					{
    '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'
        ),
    },
}

__a							     =					{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
__a							     =					{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
__a							     =					{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


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

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


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

__a							     =					collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__a							     =					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     __a:






    """simple docstring"""





    def __call__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						BatchEncoding:
         if titles is None and texts is None:
              return super().__call__(
                  _SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE  ,return_attention_mask=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)
         elif titles is None or texts is None:
              UpperCAmelCase_     :				List[str]  =     titles if texts is None else texts
              return super().__call__(
                  _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE  ,return_attention_mask=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				List[Any]  =     titles if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) else [titles]
         UpperCAmelCase_     :				List[str]  =     texts if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) else [texts]
         UpperCAmelCase_     :				Any  =     len(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[Any]  =     questions if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) else [questions] * n_passages
         if len(_SCREAMING_SNAKE_CASE    ) != len(_SCREAMING_SNAKE_CASE    ):
              raise ValueError(
                  f'''There should be as many titles than texts but got {len(_SCREAMING_SNAKE_CASE    )} titles and {len(_SCREAMING_SNAKE_CASE    )} texts.'''    )
         UpperCAmelCase_     :				Tuple  =     super().__call__(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE    )['''input_ids''']
         UpperCAmelCase_     :				int  =     super().__call__(_SCREAMING_SNAKE_CASE  ,add_special_tokens=_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE    )['''input_ids''']
         UpperCAmelCase_     :				Optional[int]  =     {
             '''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(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
             ]
         }
         if return_attention_mask is not False:
              UpperCAmelCase_     :				List[str]  =     []
              for input_ids in encoded_inputs["input_ids"]:
                   attention_mask.append([int(input_id != self.pad_token_id    ) for input_id in input_ids]    )
              UpperCAmelCase_     :				Dict  =     attention_mask
         return self.pad(_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = 16  ,_SCREAMING_SNAKE_CASE = 64  ,_SCREAMING_SNAKE_CASE = 4  ,)					->						List[DPRSpanPrediction]:
         UpperCAmelCase_     :				Tuple  =     reader_input['''input_ids''']
         UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Optional[Any]  =     reader_output[:3]
         UpperCAmelCase_     :				Optional[Any]  =     len(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				int  =     sorted(range(_SCREAMING_SNAKE_CASE    )  ,reverse=_SCREAMING_SNAKE_CASE  ,key=relevance_logits.__getitem__    )
         UpperCAmelCase_     :				List[DPRReaderOutput]  =     []
         for doc_id in sorted_docs:
              UpperCAmelCase_     :				List[Any]  =     list(input_ids[doc_id]    )
              # assuming question & title information is at the beginning of the sequence
              UpperCAmelCase_     :				str  =     sequence_ids.index(self.sep_token_id  ,2    ) + 1  # second sep id
              if sequence_ids[-1] == self.pad_token_id:
                   UpperCAmelCase_     :				List[Any]  =     sequence_ids.index(self.pad_token_id    )
              else:
                   UpperCAmelCase_     :				int  =     len(_SCREAMING_SNAKE_CASE    )

              UpperCAmelCase_     :				Tuple  =     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=_SCREAMING_SNAKE_CASE  ,top_spans=_SCREAMING_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=_SCREAMING_SNAKE_CASE  ,start_index=_SCREAMING_SNAKE_CASE  ,end_index=_SCREAMING_SNAKE_CASE  ,text=self.decode(sequence_ids[start_index : end_index + 1]    )  ,)    )
              if len(_SCREAMING_SNAKE_CASE    ) >= num_spans:
                   break
         return nbest_spans_predictions[:num_spans]





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,)					->						List[DPRSpanPrediction]:
         UpperCAmelCase_     :				Tuple  =     []
         for start_index, start_score in enumerate(_SCREAMING_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)    )
         UpperCAmelCase_     :				int  =     sorted(_SCREAMING_SNAKE_CASE  ,key=lambda _SCREAMING_SNAKE_CASE    : x[1]  ,reverse=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Union[str, Any]  =     []
         for (start_index, end_index), score in scores:
              if start_index > end_index:
                   raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''    )
              UpperCAmelCase_     :				str  =     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(_SCREAMING_SNAKE_CASE    ) == top_spans:
                   break
         return chosen_span_intervals


@add_end_docstrings(_a  )
class     __a(	_a							,		_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = READER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = READER_PRETRAINED_INIT_CONFIGURATION
    lowerCAmelCase         = ['''input_ids''', '''attention_mask''']