


'''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


__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					:  Union[str, 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"
        ),
    },
}
__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					:  str											=      {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
__A					:  Union[str, Any]											=      {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
__A					:  List[str]											=      {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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






class    __snake_case       (   __a):







    """simple docstring"""



    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    __snake_case       (   __a):







    """simple docstring"""



    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







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

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


__A					:  Optional[int]											=      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    __snake_case       :







    """simple docstring"""



    def __call__(   self    :  Any    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  Optional[str] = None    ,						lowerCamelCase    :  Optional[str] = None    ,						lowerCamelCase    :  Union[bool, str] = False    ,						lowerCamelCase    :  Union[bool, str] = False    ,						lowerCamelCase    :  Optional[int] = None    ,						lowerCamelCase    :  Optional[Union[str, TensorType]] = None    ,						lowerCamelCase    :  Optional[bool] = None    ,						**lowerCamelCase    :  Optional[Any]    ,						)   ->  Any:


      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:
        lowerCAmelCase_			:      Any			=   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    ,						)
      lowerCAmelCase_			:      Optional[int]			=   titles if not isinstance(_A    ,						_A		) else [titles]
      lowerCAmelCase_			:      Optional[int]			=   texts if not isinstance(_A    ,						_A		) else [texts]
      lowerCAmelCase_			:      List[Any]			=   len(_A		)
      lowerCAmelCase_			:      Dict			=   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.'
      lowerCAmelCase_			:      Dict			=   super().__call__(_A    ,						_A    ,						padding=_A    ,						truncation=_A		)['''input_ids''']
      lowerCAmelCase_			:      List[str]			=   super().__call__(_A    ,						add_special_tokens=_A    ,						padding=_A    ,						truncation=_A		)['''input_ids''']
      lowerCAmelCase_			:      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(_A    ,						_A		)
          ]
      }
      if return_attention_mask is not False:
        lowerCAmelCase_			:      Optional[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]		)
        lowerCAmelCase_			:      List[str]			=   attention_mask
      return self.pad(_A    ,						padding=_A    ,						max_length=_A    ,						return_tensors=_A		)

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  BatchEncoding    ,						lowerCamelCase    :  DPRReaderOutput    ,						lowerCamelCase    :  int = 16    ,						lowerCamelCase    :  int = 64    ,						lowerCamelCase    :  int = 4    ,						)   ->  int:
      lowerCAmelCase_			:      str			=   reader_input['''input_ids''']
      lowerCAmelCase_			:      List[Any]			=   reader_output[:3]
      lowerCAmelCase_			:      Tuple			=   len(_A		)
      lowerCAmelCase_			:      Any			=   sorted(range(_A		)    ,						reverse=_A    ,						key=relevance_logits.__getitem__		)
      lowerCAmelCase_			:      List[DPRReaderOutput]			=   []
      for doc_id in sorted_docs:
        lowerCAmelCase_			:      Any			=   list(input_ids[doc_id]		)
        # assuming question & title information is at the beginning of the sequence
        lowerCAmelCase_			:      Dict			=   sequence_ids.index(self.sep_token_id    ,						2		) + 1  # second sep id
        if sequence_ids[-1] == self.pad_token_id:
          lowerCAmelCase_			:      Optional[Any]			=   sequence_ids.index(self.pad_token_id		)
        else:
          lowerCAmelCase_			:      Dict			=   len(_A		)

        lowerCAmelCase_			:      List[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=_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 __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  List[int]    ,						lowerCamelCase    :  List[int]    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  int    ,						)   ->  Any:
      lowerCAmelCase_			:      Optional[int]			=   []
      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)		)
      lowerCAmelCase_			:      Tuple			=   sorted(_A    ,						key=lambda lowerCamelCase		: x[1]    ,						reverse=_A		)
      lowerCAmelCase_			:      int			=   []
      for (start_index, end_index), score in scores:
        assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
        lowerCAmelCase_			:      int			=   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    __snake_case       (   __a	,__a):







    """simple docstring"""



    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'''
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


UpperCamelCase__    					=      logging.get_logger(__name__)





def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		str													=				R'''\w+[.]\d+'''
    UpperCAmelCase__			:		List[Any]													=				re.findall(lowerCAmelCase__  ,					lowerCAmelCase__						)
    for pat in pats:
        UpperCAmelCase__			:		Union[str, Any]													=				key.replace(lowerCAmelCase__  ,					'''_'''.join(pat.split('''.'''						)						)						)
    return key





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    UpperCAmelCase__			:		Optional[Any]													=				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)
    ):
        UpperCAmelCase__			:		Union[str, Any]													=				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:
        UpperCAmelCase__			:		Optional[int]													=				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:
        UpperCAmelCase__			:		str													=				pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

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

    # linear layer
    UpperCAmelCase__			:		int													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        UpperCAmelCase__			:		Optional[Any]													=				pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    UpperCAmelCase__			:		Optional[Any]													=				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 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=42						)    ->	Tuple:
    # Step 1: Convert pytorch tensor to numpy
    UpperCAmelCase__			:		int													=				{k: v.numpy() for k, v in pt_state_dict.items()}

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

    UpperCAmelCase__			:		Optional[Any]													=				flatten_dict(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        UpperCAmelCase__			:		Optional[int]													=				rename_key(lowerCAmelCase__						)
        UpperCAmelCase__			:		str													=				tuple(renamed_pt_key.split('''.'''						)						)

        # Correctly rename weight parameters
        UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				rename_key_and_reshape_tensor(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

        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
        UpperCAmelCase__			:		List[str]													=				jnp.asarray(lowerCAmelCase__						)

    return unflatten_dict(lowerCAmelCase__						)
