


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


__UpperCAmelCase      :Dict			    =       logging.get_logger(__name__)

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

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


__UpperCAmelCase      :List[Any]			    =       {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__UpperCAmelCase      :int			    =       {
    "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      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			int							       =       VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE	:			Tuple							       =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE	:			Dict							       =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE	:			Tuple							       =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



__UpperCAmelCase      :List[Any]			    =       collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

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


__UpperCAmelCase      :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    ```\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					:		Union[str, Any]							,			snake_case					:		int							,			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					:		Dict							,			)						->							BatchEncoding:
          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:
               __UpperCAmelCase     :			Optional[Any]  							=      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							,			)
          __UpperCAmelCase     :			Optional[Any]  							=      titles if not isinstance(snake_case							,			snake_case      ) else [titles]
          __UpperCAmelCase     :			Any  							=      texts if not isinstance(snake_case							,			snake_case      ) else [texts]
          __UpperCAmelCase     :			Tuple  							=      len(snake_case      )
          __UpperCAmelCase     :			str  							=      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.'      )
          __UpperCAmelCase     :			int  							=      super().__call__(snake_case							,			snake_case							,			padding=snake_case							,			truncation=snake_case      )['''input_ids''']
          __UpperCAmelCase     :			List[str]  							=      super().__call__(snake_case							,			add_special_tokens=snake_case							,			padding=snake_case							,			truncation=snake_case      )['''input_ids''']
          __UpperCAmelCase     :			List[str]  							=      {
              '''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:
               __UpperCAmelCase     :			Dict  							=      []
               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     :			int  							=      attention_mask
          return self.pad(snake_case							,			padding=snake_case							,			max_length=snake_case							,			return_tensors=snake_case      )

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		BatchEncoding							,			snake_case					:		DPRReaderOutput							,			snake_case					:		int = 16							,			snake_case					:		int = 64							,			snake_case					:		int = 4							,			)						->							List[DPRSpanPrediction]:
          __UpperCAmelCase     :			List[str]  							=      reader_input['''input_ids''']
          __UpperCAmelCase     :			Optional[Any]  							=      reader_output[:3]
          __UpperCAmelCase     :			Optional[int]  							=      len(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      sorted(range(snake_case      )							,			reverse=snake_case							,			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     :			Any  							=      sequence_ids.index(self.sep_token_id							,			2      ) + 1  # second sep id
               if sequence_ids[-1] == self.pad_token_id:
                    __UpperCAmelCase     :			Union[str, Any]  							=      sequence_ids.index(self.pad_token_id      )
               else:
                    __UpperCAmelCase     :			Optional[int]  							=      len(snake_case      )

               __UpperCAmelCase     :			List[str]  							=      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 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		List[int]							,			snake_case					:		List[int]							,			snake_case					:		int							,			snake_case					:		int							,			)						->							List[DPRSpanPrediction]:
          __UpperCAmelCase     :			Union[str, Any]  							=      []
          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)      )
          __UpperCAmelCase     :			List[str]  							=      sorted(snake_case							,			key=lambda snake_case      : x[1]							,			reverse=snake_case      )
          __UpperCAmelCase     :			List[str]  							=      []
          for (start_index, end_index), score in scores:
               if start_index > end_index:
                    raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]'      )
               __UpperCAmelCase     :			List[Any]  							=      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      a    (		_a     ,  _a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			str							       =       VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE	:			Tuple							       =       READER_PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE	:			int							       =       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       READER_PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE	:			List[str]							       =       ["input_ids", "attention_mask"]



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCAmelCase      :Union[str, Any]			    =       {
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Dict			    =       [
          "RagModel",
          "RagPreTrainedModel",
          "RagSequenceForGeneration",
          "RagTokenForGeneration",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Dict			    =       [
          "TFRagModel",
          "TFRagPreTrainedModel",
          "TFRagSequenceForGeneration",
          "TFRagTokenForGeneration",
      ]


if TYPE_CHECKING:
      from .configuration_rag import RagConfig
      from .retrieval_rag import RagRetriever
      from .tokenization_rag import RagTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rag import (
                TFRagModel,
                TFRagPreTrainedModel,
                TFRagSequenceForGeneration,
                TFRagTokenForGeneration,
            )

else:
      import sys

      __UpperCAmelCase      :Any			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)