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


UpperCamelCase     :    List[Any]       =			logging.get_logger(__name__)

UpperCamelCase     :    List[Any]       =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase     :    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"
        ),
    },
}
UpperCamelCase     :    Any       =			{
    "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"
        ),
    },
}
UpperCamelCase     :    Optional[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"
        ),
    },
}

UpperCamelCase     :    int       =			{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
UpperCamelCase     :    Optional[int]       =			{
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
UpperCamelCase     :    Optional[Any]       =			{
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


UpperCamelCase     :    Tuple       =			{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCamelCase     :    Any       =			{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCamelCase     :    str       =			{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
     lowercase						       =						DPRContextEncoderTokenizer


class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
     lowercase						       =						DPRQuestionEncoderTokenizer


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

UpperCamelCase     :    Union[str, Any]       =			collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


UpperCamelCase     :    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    [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(__SCREAMING_SNAKE_CASE      )
class __lowerCAmelCase		:





     def __call__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''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:
       __UpperCamelCase      =	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     ,					)
      __UpperCamelCase      =	titles if not isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) else [titles]
      __UpperCamelCase      =	texts if not isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) else [texts]
      __UpperCamelCase      =	len(__UpperCAmelCase				)
      __UpperCamelCase      =	questions if not isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) else [questions] * n_passages
      assert len(__UpperCAmelCase				) == len(
          __UpperCAmelCase				), F'There should be as many titles than texts but got {len(__UpperCAmelCase				)} titles and {len(__UpperCAmelCase				)} texts.'
      __UpperCamelCase      =	super().__call__(__UpperCAmelCase     ,					__UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					truncation=__UpperCAmelCase				)['input_ids']
      __UpperCamelCase      =	super().__call__(__UpperCAmelCase     ,					add_special_tokens=__UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					truncation=__UpperCAmelCase				)['input_ids']
      __UpperCamelCase      =	{
          '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:
       __UpperCamelCase      =	[]
       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      =	attention_mask
      return self.pad(__UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					max_length=__UpperCAmelCase     ,					return_tensors=__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = 16     ,					__UpperCAmelCase = 64     ,					__UpperCAmelCase = 4     ,					):

      '''simple docstring'''




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

       __UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	[]
      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)				)
      __UpperCamelCase      =	sorted(__UpperCAmelCase     ,					key=lambda __UpperCAmelCase				: x[1]     ,					reverse=__UpperCAmelCase				)
      __UpperCamelCase      =	[]
      for (start_index, end_index), score in scores:
       assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
       __UpperCamelCase      =	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(__UpperCAmelCase				) == top_spans:
        break
      return chosen_span_intervals


@add_end_docstrings(__SCREAMING_SNAKE_CASE      )
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						READER_PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						READER_PRETRAINED_INIT_CONFIGURATION
     lowercase						       =						["input_ids", "attention_mask"]
     lowercase						       =						DPRReaderTokenizer







"""simple docstring"""

def 							A						(    snake_case				:list[list[int]]    , snake_case				:int    , snake_case				:int    , snake_case				:list[int]		)				->     bool:

 # 1. Validate that path exists between current and next vertices
 if graph[path[curr_ind - 1]][next_ver] == 0:
  return False

 # 2. Validate that next vertex is not already in path
 return not any(vertex == next_ver for vertex in path		)






def 							A						(    snake_case				:list[list[int]]    , snake_case				:list[int]    , snake_case				:int		)				->     bool:

 # Base Case
 if curr_ind == len(snake_case		):
  # return whether path exists between current and starting vertices
  return graph[path[curr_ind - 1]][path[0]] == 1

 # Recursive Step
 for next_ver in range(0    , len(snake_case		)		):
  if valid_connection(snake_case    , snake_case    , snake_case    , snake_case		):
   # Insert current vertex  into path as next transition
   __UpperCamelCase      =	next_ver
   # Validate created path
   if util_hamilton_cycle(snake_case    , snake_case    , curr_ind + 1		):
    return True
   # Backtrack
   __UpperCamelCase      =	-1
 return False






def 							A						(    snake_case				:list[list[int]]    , snake_case				:int = 0		)				->     list[int]:
 __UpperCamelCase      =	[-1] * (len(snake_case		) + 1)
 # initialize start and end of path with starting index
 __UpperCamelCase      =	__UpperCamelCase      =	start_index
 # evaluate and if we find answer return path either return empty array
 return path if util_hamilton_cycle(snake_case    , snake_case    , 1		) else []



