






'''simple docstring'''





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							:							Any  							=		logging.get_logger(__name__)

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

__A							:							Dict  							=		{
    '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							:							Dict  							=		{
    '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							:							Tuple  							=		{
    '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							:							Any  							=		{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_12,
    'facebook/dpr-ctx_encoder-multiset-base': 5_12,
}
__A							:							Optional[Any]  							=		{
    'facebook/dpr-question_encoder-single-nq-base': 5_12,
    'facebook/dpr-question_encoder-multiset-base': 5_12,
}
__A							:							Tuple  							=		{
    'facebook/dpr-reader-single-nq-base': 5_12,
    'facebook/dpr-reader-multiset-base': 5_12,
}


__A							:							Any  							=		{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__A							:							Tuple  							=		{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__A							:							List[Any]  							=		{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class 				lowercase       (							_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 				lowercase       (							_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							:							Tuple  							=		collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

__A							:							Tuple  							=		collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


__A							:							Tuple  							=		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 				lowercase       :


				'''simple docstring'''

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

					'''simple docstring'''




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

				def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     BatchEncoding				,     __lowerCamelCase     :     DPRReaderOutput				,     __lowerCamelCase     :     int = 16				,     __lowerCamelCase     :     int = 64				,     __lowerCamelCase     :     int = 4				,     )							->							List[DPRSpanPrediction]:

					'''simple docstring'''




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

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




				def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     List[int]				,     __lowerCamelCase     :     List[int]				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int				,     )							->							List[DPRSpanPrediction]:

					'''simple docstring'''




					lowerCamelCase__  =     []
					for start_index, start_score in enumerate(UpperCamelCase__     ):
						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)     )
					lowerCamelCase__  =     sorted(UpperCamelCase__				,     key=lambda __lowerCamelCase     : x[1]				,     reverse=UpperCamelCase__     )
					lowerCamelCase__  =     []
					for (start_index, end_index), score in scores:
						if start_index > end_index:
							raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''     )
						lowerCamelCase__  =     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(UpperCamelCase__     ) == top_spans:
							break
					return chosen_span_intervals
@add_end_docstrings(_A      )
class 				lowercase       (							_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"]









'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__A							:							Union[str, Any]  							=		logging.get_logger(__name__)

__A							:							Tuple  							=		{
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"gpt_neo"
    lowerCAmelCase__      				=							["past_key_values"]
    lowerCAmelCase__      				=							{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

    def __init__(					self     :     str				,     __lowerCamelCase     :     Optional[int]=50257				,     __lowerCamelCase     :     Optional[int]=2048				,     __lowerCamelCase     :     Optional[int]=2048				,     __lowerCamelCase     :     Any=24				,     __lowerCamelCase     :     List[Any]=[[["global", "local"], 12]]				,     __lowerCamelCase     :     Any=16				,     __lowerCamelCase     :     Any=None				,     __lowerCamelCase     :     Optional[Any]=256				,     __lowerCamelCase     :     List[Any]="gelu_new"				,     __lowerCamelCase     :     Any=0.0				,     __lowerCamelCase     :     List[Any]=0.0				,     __lowerCamelCase     :     Union[str, Any]=0.0				,     __lowerCamelCase     :     Any=0.1				,     __lowerCamelCase     :     Dict=1E-5				,     __lowerCamelCase     :     List[Any]=0.0_2				,     __lowerCamelCase     :     List[Any]=True				,     __lowerCamelCase     :     Optional[int]=50256				,     __lowerCamelCase     :     Tuple=50256				,     **__lowerCamelCase     :     Optional[int]				,     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     vocab_size
     lowerCamelCase__  =     max_position_embeddings
     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_layers
     lowerCamelCase__  =     num_heads
     lowerCamelCase__  =     intermediate_size
     lowerCamelCase__  =     window_size
     lowerCamelCase__  =     activation_function
     lowerCamelCase__  =     resid_dropout
     lowerCamelCase__  =     embed_dropout
     lowerCamelCase__  =     attention_dropout
     lowerCamelCase__  =     classifier_dropout
     lowerCamelCase__  =     layer_norm_epsilon
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     use_cache

     lowerCamelCase__  =     bos_token_id
     lowerCamelCase__  =     eos_token_id

     lowerCamelCase__  =     attention_types
     lowerCamelCase__  =     self.expand_attention_types_params(__lowerCamelCase     )

     if len(self.attention_layers     ) != self.num_layers:
      raise ValueError(
          "Configuration for convolutional module is incorrect. "
          "It is required that `len(config.attention_layers)` == `config.num_layers` "
          f'''but is `len(config.attention_layers) = {len(self.attention_layers     )}`, '''
          f'''`config.num_layers = {self.num_layers}`. '''
          "`config.attention_layers` is prepared using `config.attention_types`. "
          "Please verify the value of `config.attention_types` argument."     )

     super().__init__(bos_token_id=__lowerCamelCase				,     eos_token_id=__lowerCamelCase				,     **__lowerCamelCase     )




    @staticmethod
    def    a__				(					__lowerCamelCase     :     int     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     []
     for item in attention_types:
      for _ in range(item[1]     ):
       attentions.extend(item[0]     )
     return attentions






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 import torch

 lowerCamelCase__  =     input.size()
 lowerCamelCase__  =     len(lowercase__)
 lowerCamelCase__  =     shape[dimension]

 lowerCamelCase__  =     torch.arange(0				,     lowercase__				,     lowercase__)
 lowerCamelCase__  =     torch.div(sizedim - size				,     lowercase__				,     rounding_mode="floor") + 1
 lowerCamelCase__  =     torch.arange(lowercase__) + low_indices[:min_length][:, None]

 lowerCamelCase__  =     [slice(lowercase__)] * rank
 lowerCamelCase__  =     indices
 lowerCamelCase__  =     input[s]

 lowerCamelCase__  =     list(range(0				,     rank + 1))
 perm.append(perm.pop(dimension + 1))

 return sliced.permute(lowercase__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 import torch

 lowerCamelCase__  =     torch.arange(1				,     lowercase__)
 lowerCamelCase__  =     torch.remainder(lowercase__				,     lowercase__)
 lowerCamelCase__  =     remainders == 0
 lowerCamelCase__  =     candidates[divisor_indices]
 lowerCamelCase__  =     torch.max(lowercase__)
 return largest_divisor, torch.div(lowercase__				,     lowercase__				,     rounding_mode="floor")
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    @property
    def    a__				(					self     :     Any     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     lowerCamelCase__  =     OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
     if self.use_past:
      self.fill_with_past_key_values_(__lowerCamelCase				,     direction="inputs"     )
      lowerCamelCase__  =     {0: "batch", 1: "past_sequence + sequence"}
     else:
      lowerCamelCase__  =     {0: "batch", 1: "sequence"}

     return common_inputs

    @property
    def    a__				(					self     :     Any     )							->							int:

     '''simple docstring'''




     return self._config.num_heads

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     PreTrainedTokenizer				,     __lowerCamelCase     :     int = -1				,     __lowerCamelCase     :     int = -1				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     Optional[TensorType] = None				,     )							->							Mapping[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     super(__lowerCamelCase				,     self     ).generate_dummy_inputs(
         __lowerCamelCase				,     batch_size=__lowerCamelCase				,     seq_length=__lowerCamelCase				,     is_pair=__lowerCamelCase				,     framework=__lowerCamelCase     )

     # We need to order the input in the way they appears in the forward()
     lowerCamelCase__  =     OrderedDict({"input_ids": common_inputs["input_ids"]}     )

     # Need to add the past_keys
     if self.use_past:
      if not is_torch_available():
       raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."     )
      else:
       import torch

       lowerCamelCase__  ,	lowerCamelCase__  =     common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       lowerCamelCase__  =     seqlen + 2
       lowerCamelCase__  =     (
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       lowerCamelCase__  =     [
           (torch.zeros(__lowerCamelCase     ), torch.zeros(__lowerCamelCase     )) for _ in range(self.num_layers     )
       ]

     lowerCamelCase__  =     common_inputs["attention_mask"]
     if self.use_past:
      lowerCamelCase__  =     ordered_inputs["attention_mask"].dtype
      lowerCamelCase__  =     torch.cat(
          [ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase				,     __lowerCamelCase				,     dtype=__lowerCamelCase     )]				,     dim=1     )

     return ordered_inputs




    @property
    def    a__				(					self     :     Optional[Any]     )							->							int:

     '''simple docstring'''




     return 13

