


"""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"""




    UpperCAmelCase	:							int     =	VOCAB_FILES_NAMES
    UpperCAmelCase	:							Union[str, Any]     =	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase	:							Union[str, Any]     =	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase	:							List[Any]     =	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase	:							Optional[int]     =	DPRContextEncoderTokenizer






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Any     =	VOCAB_FILES_NAMES
    UpperCAmelCase	:							int     =	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase	:							List[Any]     =	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase	:							Optional[Any]     =	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase	:							List[Any]     =	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"""
    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 						_A  :







    """simple docstring"""




    def __call__( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Optional[str] = None    ,			__UpperCAmelCase    :   Optional[str] = None    ,			__UpperCAmelCase    :   Union[bool, str] = False    ,			__UpperCAmelCase    :   Union[bool, str] = False    ,			__UpperCAmelCase    :   Optional[int] = None    ,			__UpperCAmelCase    :   Optional[Union[str, TensorType]] = None    ,			__UpperCAmelCase    :   Optional[bool] = None    ,			**__UpperCAmelCase    :   Optional[Any]    ,			):
      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:
        a						:		int											=      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    ,			)
      a						:		Union[str, Any]											=      titles if not isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) else [titles]
      a						:		str											=      texts if not isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) else [texts]
      a						:		Union[str, Any]											=      len(__UpperCAmelCase)
      a						:		Optional[int]											=      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.'''
      a						:		Tuple											=      super().__call__(__UpperCAmelCase    ,			__UpperCAmelCase    ,			padding=__UpperCAmelCase    ,			truncation=__UpperCAmelCase)["input_ids"]
      a						:		Optional[int]											=      super().__call__(__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase    ,			padding=__UpperCAmelCase    ,			truncation=__UpperCAmelCase)["input_ids"]
      a						:		Dict											=      {
          "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:
        a						:		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])
        a						:		Dict											=      attention_mask
      return self.pad(__UpperCAmelCase    ,			padding=__UpperCAmelCase    ,			max_length=__UpperCAmelCase    ,			return_tensors=__UpperCAmelCase)






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

        a						:		int											=      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      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   List[int]    ,			__UpperCAmelCase    :   List[int]    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   int    ,			):
      a						:		Tuple											=      []
      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))
      a						:		Union[str, Any]											=      sorted(__UpperCAmelCase    ,			key=lambda __UpperCAmelCase: x[1]    ,			reverse=__UpperCAmelCase)
      a						:		Union[str, Any]											=      []
      for (start_index, end_index), score in scores:
        assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
        a						:		List[Any]											=      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 						_A  (  _a   ,_a  ):







    """simple docstring"""




    UpperCAmelCase	:							str     =	VOCAB_FILES_NAMES
    UpperCAmelCase	:							Dict     =	READER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase	:							Union[str, Any]     =	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase	:							Optional[int]     =	READER_PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase	:							Optional[int]     =	["""input_ids""", """attention_mask"""]
    UpperCAmelCase	:							str     =	DPRReaderTokenizer










"""simple docstring"""







import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      TapasConfig.from_json_file(A_	)
  # set absolute/relative position embeddings parameter
  a						:		str											=      reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    a						:		Dict											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WTQ":
    # run_task_main.py hparams
    a						:		Any											=      4
    a						:		Dict											=      True
    # hparam_utils.py hparams
    a						:		str											=      0.6_6_4_6_9_4
    a						:		Optional[int]											=      0.2_0_7_9_5_1
    a						:		Optional[Any]											=      0.1_2_1_1_9_4
    a						:		Union[str, Any]											=      True
    a						:		int											=      True
    a						:		Tuple											=      False
    a						:		Dict											=      0.0_3_5_2_5_1_3

    a						:		List[str]											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    a						:		Union[str, Any]											=      4
    a						:		List[Any]											=      False
    # hparam_utils.py hparams
    a						:		Dict											=      3_6.4_5_1_9
    a						:		List[str]											=      0.9_0_3_4_2_1
    a						:		Optional[Any]											=      2_2_2.0_8_8
    a						:		Dict											=      True
    a						:		Union[str, Any]											=      True
    a						:		List[str]											=      True
    a						:		List[str]											=      0.7_6_3_1_4_1

    a						:		Any											=      TapasForQuestionAnswering(config=A_	)
  elif task == "TABFACT":
    a						:		int											=      TapasForSequenceClassification(config=A_	)
  elif task == "MLM":
    a						:		int											=      TapasForMaskedLM(config=A_	)
  elif task == "INTERMEDIATE_PRETRAINING":
    a						:		List[Any]											=      TapasModel(config=A_	)
  else:
    raise ValueError(F'''Task {task} not supported.'''	)

  print(F'''Building PyTorch model from configuration: {config}'''	)
  # Load weights from tf checkpoint
  load_tf_weights_in_tapas(A_  ,       A_  ,       A_	)

  # Save pytorch-model (weights and configuration)
  print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
  model.save_pretrained(A_	)

  # Save tokenizer files
  print(F'''Save tokenizer files to {pytorch_dump_path}'''	)
  a						:		Optional[int]											=      TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"  ,       model_max_length=512	)
  tokenizer.save_pretrained(A_	)

  print("Used relative position embeddings:"  ,       model.config.reset_position_index_per_cell	)


if __name__ == "__main__":
 __lowercase    =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
 )
 parser.add_argument(
     """--reset_position_index_per_cell""",
     default=False,
     action="""store_true""",
     help="""Whether to use relative position embeddings or not. Defaults to True.""",
 )
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--tapas_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained TAPAS model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 __lowercase    =				parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )






