


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	 =      logging.get_logger(__name__)

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

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


__a	 =      {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__a	 =      {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__a	 =      {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}






class 	__lowercase					(	a_	):
      UpperCamelCase										=							VOCAB_FILES_NAMES
      UpperCamelCase										=							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase										=							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase										=							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase										=							DPRContextEncoderTokenizer






class 	__lowercase					(	a_	):
      UpperCamelCase										=							VOCAB_FILES_NAMES
      UpperCamelCase										=							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase										=							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase										=							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase										=							DPRQuestionEncoderTokenizer

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

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


__a	 =      R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''






@add_start_docstrings(a_	)
class 	__lowercase					:





      def __call__(		self    :       Union[str, Any]       ,							__lowerCamelCase    :       List[Any]       ,							__lowerCamelCase    :       Optional[Any] = None       ,							__lowerCamelCase    :       Any = None       ,							__lowerCamelCase    :       Tuple = False       ,							__lowerCamelCase    :       Optional[int] = False       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       Dict = None       ,							**__lowerCamelCase    :       Optional[int]       ,							)     -> 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:
            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 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       List[Any]       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       List[Any] = 1_6       ,							__lowerCamelCase    :       Tuple = 6_4       ,							__lowerCamelCase    :       Any = 4       ,							)     -> List[DPRSpanPrediction]:






         """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 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       str       ,							__lowerCamelCase    :       int       ,							)     -> List[DPRSpanPrediction]:






         """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 __lowerCamelCase							: 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(a_	)
class 	__lowercase					(	a_			,       a_	):
      UpperCamelCase										=							VOCAB_FILES_NAMES
      UpperCamelCase										=							READER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase										=							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase										=							READER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase										=							['''input_ids''', '''attention_mask''']
      UpperCamelCase										=							DPRReaderTokenizer











import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase      :       int    =						logging.get_logger(__name__)





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       r"\w+[.]\d+"
     __lowerCAmelCase             =       re.findall(__snake_case       ,			__snake_case							)
     for pat in pats:
          __lowerCAmelCase             =       key.replace(__snake_case       ,			"_".join(pat.split("."							)							)							)
     return key





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("scale",)
     if (
         any("norm" in str_ for str_ in pt_tuple_key							)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("scale",)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("scale",)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("embedding",)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("kernel",)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          __lowerCAmelCase             =       pt_tensor.transpose(2       ,			3       ,			1       ,			0							)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("kernel",)
     if pt_tuple_key[-1] == "weight":
          __lowerCAmelCase             =       pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("weight",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("bias",)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case=42							):
     # Step 1: Convert pytorch tensor to numpy
     __lowerCAmelCase             =       {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     __lowerCAmelCase             =       flax_model.init_weights(PRNGKey(__snake_case							)							)

     __lowerCAmelCase             =       flatten_dict(__snake_case							)
     __lowerCAmelCase             =       {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          __lowerCAmelCase             =       rename_key(__snake_case							)
          __lowerCAmelCase             =       tuple(renamed_pt_key.split("."							)							)

          # Correctly rename weight parameters
          __lowerCAmelCase      ,			__lowerCAmelCase             =       rename_key_and_reshape_tensor(__snake_case       ,			__snake_case       ,			__snake_case							)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                        F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""							)

        # also add unexpected weight so that warning is thrown
          __lowerCAmelCase             =       jnp.asarray(__snake_case							)

     return unflatten_dict(__snake_case							)






