






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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


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

A_						:						List[str]						      =					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_						:						List[Any]						      =					{
    '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_						:						str						      =					{
    '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_						:						List[Any]						      =					{
    '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_						:						Optional[Any]						      =					{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
A_						:						Optional[int]						      =					{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
A_						:						Any						      =					{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


A_						:						Any						      =					{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A_						:						Union[str, Any]						      =					{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
A_						:						List[str]						      =					{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}




class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						Tuple										=VOCAB_FILES_NAMES
       a							:						Dict										=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       a							:						Dict										=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						Dict										=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       a							:						Optional[Any]										=DPRContextEncoderTokenizer







class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						Dict										=VOCAB_FILES_NAMES
       a							:						Optional[Any]										=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       a							:						int										=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						int										=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       a							:						Dict										=DPRQuestionEncoderTokenizer







A_						:						Optional[Any]						      =					collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

A_						:						Optional[int]						      =					collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


A_						:						List[str]						      =					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    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\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    Return:\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(UpperCAmelCase_   )
class 							_lowerCAmelCase:





       """simple docstring"""







       def __call__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = False    ,     _lowerCamelCase = False    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
         if titles is None and texts is None:
           return super().__call__(
               _lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     max_length=_lowerCamelCase    ,     return_tensors=_lowerCamelCase    ,     return_attention_mask=_lowerCamelCase    ,     **_lowerCamelCase    ,     )
         elif titles is None or texts is None:
           UpperCamelCase_:   Optional[int]        =							titles if texts is None else texts
           return super().__call__(
               _lowerCamelCase    ,     _lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     max_length=_lowerCamelCase    ,     return_tensors=_lowerCamelCase    ,     return_attention_mask=_lowerCamelCase    ,     **_lowerCamelCase    ,     )
         UpperCamelCase_:   Any        =							titles if not isinstance(_lowerCamelCase    ,     _lowerCamelCase				) else [titles]
         UpperCamelCase_:   Optional[Any]        =							texts if not isinstance(_lowerCamelCase    ,     _lowerCamelCase				) else [texts]
         UpperCamelCase_:   Dict        =							len(_lowerCamelCase				)
         UpperCamelCase_:   Tuple        =							questions if not isinstance(_lowerCamelCase    ,     _lowerCamelCase				) else [questions] * n_passages
         assert len(_lowerCamelCase				) == len(
             _lowerCamelCase				), f'''There should be as many titles than texts but got {len(_lowerCamelCase				)} titles and {len(_lowerCamelCase				)} texts.'''
         UpperCamelCase_:   str        =							super().__call__(_lowerCamelCase    ,     _lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase				)['input_ids']
         UpperCamelCase_:   str        =							super().__call__(_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase				)['input_ids']
         UpperCamelCase_:   Tuple        =							{
             '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(_lowerCamelCase    ,     _lowerCamelCase				)
             ]
         }
         if return_attention_mask is not False:
           UpperCamelCase_:   List[Any]        =							[]
           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(_lowerCamelCase    ,     padding=_lowerCamelCase    ,     max_length=_lowerCamelCase    ,     return_tensors=_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = 1_6    ,     _lowerCamelCase = 6_4    ,     _lowerCamelCase = 4    ,     ):
         UpperCamelCase_:   Optional[int]        =							reader_input['input_ids']
         UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   List[str]        =							reader_output[:3]
         UpperCamelCase_:   Any        =							len(_lowerCamelCase				)
         UpperCamelCase_:   Optional[Any]        =							sorted(range(_lowerCamelCase				)    ,     reverse=_lowerCamelCase    ,     key=relevance_logits.__getitem__				)
         UpperCamelCase_:   List[DPRReaderOutput]        =							[]
         for doc_id in sorted_docs:
           UpperCamelCase_:   int        =							list(input_ids[doc_id]				)
           # assuming question & title information is at the beginning of the sequence
           UpperCamelCase_:   Tuple        =							sequence_ids.index(self.sep_token_id    ,     2				) + 1  # second sep id
           if sequence_ids[-1] == self.pad_token_id:
             UpperCamelCase_:   str        =							sequence_ids.index(self.pad_token_id				)
           else:
             UpperCamelCase_:   Tuple        =							len(_lowerCamelCase				)

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





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     ):
         UpperCamelCase_:   List[str]        =							[]
         for start_index, start_score in enumerate(_lowerCamelCase				):
           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_:   List[Any]        =							sorted(_lowerCamelCase    ,     key=lambda _lowerCamelCase				: x[1]    ,     reverse=_lowerCamelCase				)
         UpperCamelCase_:   Any        =							[]
         for (start_index, end_index), score in scores:
           assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
           UpperCamelCase_:   str        =							end_index - start_index + 1
           assert length <= max_answer_length, 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(_lowerCamelCase				) == top_spans:
             break
         return chosen_span_intervals







@add_end_docstrings(UpperCAmelCase_   )
class 							_lowerCAmelCase(			UpperCAmelCase_				,      UpperCAmelCase_   ):





       """simple docstring"""







       a							:						Optional[int]										=VOCAB_FILES_NAMES
       a							:						int										=READER_PRETRAINED_VOCAB_FILES_MAP
       a							:						Any										=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						Optional[Any]										=READER_PRETRAINED_INIT_CONFIGURATION
       a							:						List[Any]										=['''input_ids''', '''attention_mask''']
       a							:						Any										=DPRReaderTokenizer






'''simple docstring'''




import os
from collections.abc import Iterator





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "."		)		->  Iterator[str]:
   for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase		):
      snake_case 						=   [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
      for filename in filenames:
         if filename == "__init__.py":
            continue
         if os.path.splitext(__lowerCAmelCase		)[1] in (".py", ".ipynb"):
            yield os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		).lstrip("""./"""		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]		)		->  Any:
   return F'''{i * "  "}*''' if i else "\n##"





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							str		)		->  str:
   snake_case 						=   old_path.split(os.sep		)
   for i, new_part in enumerate(new_path.split(os.sep		)		):
      if (i + 1 > len(__lowerCAmelCase		) or old_parts[i] != new_part) and new_part:
         print(F'''{md_prefix(__lowerCAmelCase		)} {new_part.replace("_"      ,    " "		).title()}'''		)
   return new_path





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "."		)		->  None:
   snake_case 						=   """"""
   for filepath in sorted(good_file_paths(__lowerCAmelCase		)		):
      snake_case						,    snake_case 						=   os.path.split(__lowerCAmelCase		)
      if filepath != old_path:
         snake_case 						=   print_path(__lowerCAmelCase      ,    __lowerCAmelCase		)
      snake_case 						=   (filepath.count(os.sep		) + 1) if filepath else 0
      snake_case 						=   F'''{filepath}/{filename}'''.replace(""" """      ,    """%20"""		)
      snake_case 						=   os.path.splitext(filename.replace("""_"""      ,    """ """		).title()		)[0]
      print(F'''{md_prefix(__lowerCAmelCase		)} [{filename}]({url})'''		)


if __name__ == "__main__":
 print_directory_md(".")

