



'''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


lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase_									=    {
    "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"
        ),
    },
}
lowercase_									=    {
    "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"
        ),
    },
}
lowercase_									=    {
    "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"
        ),
    },
}

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


lowercase_									=    {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
lowercase_									=    {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
lowercase_									=    {
    "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       :			Optional[Any]		=			VOCAB_FILES_NAMES
 __lowerCamelCase       :			Optional[int]		=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase       :			str		=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase       :			Optional[Any]		=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
 __lowerCamelCase       :			int		=			DPRContextEncoderTokenizer






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Dict		=			VOCAB_FILES_NAMES
 __lowerCamelCase       :			str		=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase       :			str		=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase       :			Union[str, Any]		=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
 __lowerCamelCase       :			List[str]		=			DPRQuestionEncoderTokenizer





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

lowercase_									=    collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


lowercase_									=    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(A		)
class __A     :



 '''simple docstring'''







 def __call__(self					,  A					,  A = None					,  A = None					,  A = False					,  A = False					,  A = None					,  A = None					,  A = None					,  **A					,  )       ->	BatchEncoding:


      """simple docstring"""



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



 def    a__    (self					,  A					,  A					,  A = 16					,  A = 64					,  A = 4					,  )       ->	List[DPRSpanPrediction]:


      """simple docstring"""



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

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



 def    a__    (self					,  A					,  A					,  A					,  A					,  )       ->	List[DPRSpanPrediction]:


      """simple docstring"""



      _a           =    []
      for start_index, start_score in enumerate(A			):
           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)			)
      _a           =    sorted(A					,  key=lambda A			: x[1]					,  reverse=A			)
      _a           =    []
      for (start_index, end_index), score in scores:
           assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
           _a           =    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(A			) == top_spans:
                break
      return chosen_span_intervals






@add_end_docstrings(A		)
class __A     (    A  ,  A		):



 '''simple docstring'''







 __lowerCamelCase       :			Union[str, Any]		=			VOCAB_FILES_NAMES
 __lowerCamelCase       :			Any		=			READER_PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase       :			List[Any]		=			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase       :			Dict		=			READER_PRETRAINED_INIT_CONFIGURATION
 __lowerCamelCase       :			Optional[Any]		=			['input_ids', 'attention_mask']
 __lowerCamelCase       :			Optional[int]		=			DPRReaderTokenizer








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_									=    {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    ["BlipImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    [
      "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BlipModel",
      "BlipPreTrainedModel",
      "BlipForConditionalGeneration",
      "BlipForQuestionAnswering",
      "BlipVisionModel",
      "BlipTextModel",
      "BlipForImageTextRetrieval",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    [
      "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFBlipModel",
      "TFBlipPreTrainedModel",
      "TFBlipForConditionalGeneration",
      "TFBlipForQuestionAnswering",
      "TFBlipVisionModel",
      "TFBlipTextModel",
      "TFBlipForImageTextRetrieval",
  ]

if TYPE_CHECKING:
  from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
  from .processing_blip import BlipProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_blip import BlipImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blip import (
        BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlipForConditionalGeneration,
        BlipForImageTextRetrieval,
        BlipForQuestionAnswering,
        BlipModel,
        BlipPreTrainedModel,
        BlipTextModel,
        BlipVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blip import (
        TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBlipForConditionalGeneration,
        TFBlipForImageTextRetrieval,
        TFBlipForQuestionAnswering,
        TFBlipModel,
        TFBlipPreTrainedModel,
        TFBlipTextModel,
        TFBlipVisionModel,
    )

else:
  import sys

  lowercase_									=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



