




"""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 import BertTokenizer


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE    =	{
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE    =	{
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE    =	{
    "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"
        ),
    },
}

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


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




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				VOCAB_FILES_NAMES
    lowercase__						 =				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowercase__						 =				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__						 =				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				VOCAB_FILES_NAMES
    lowercase__						 =				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowercase__						 =				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__						 =				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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

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


SCREAMING_SNAKE_CASE    =	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      UpperCAmelCase_  :




    def __call__(   self						:	int     ,			snake_case_						:	Optional[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:




     '''simple docstring'''

     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:
      A__	=    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_     ,			)
     A__	=    titles if not isinstance(snake_case_     ,			snake_case_				) else [titles]
     A__	=    texts if not isinstance(snake_case_     ,			snake_case_				) else [texts]
     A__	=    len(snake_case_				)
     A__	=    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."""				)
     A__	=    super().__call__(snake_case_     ,			snake_case_     ,			padding=snake_case_     ,			truncation=snake_case_				)["input_ids"]
     A__	=    super().__call__(snake_case_     ,			add_special_tokens=snake_case_     ,			padding=snake_case_     ,			truncation=snake_case_				)["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(snake_case_     ,			snake_case_				)
         ]
     }
     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(snake_case_     ,			padding=snake_case_     ,			max_length=snake_case_     ,			return_tensors=snake_case_				)




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	BatchEncoding     ,			snake_case_						:	DPRReaderOutput     ,			snake_case_						:	int = 16     ,			snake_case_						:	int = 64     ,			snake_case_						:	int = 4     ,			)				->							List[DPRSpanPrediction]:




     '''simple docstring'''

     A__	=    reader_input["input_ids"]
     A__,				A__,				A__	=    reader_output[:3]
     A__	=    len(snake_case_				)
     A__	=    sorted(range(snake_case_				)     ,			reverse=snake_case_     ,			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(snake_case_				)

      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=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 							__magic_name__							(   self						:	int     ,			snake_case_						:	List[int]     ,			snake_case_						:	List[int]     ,			snake_case_						:	int     ,			snake_case_						:	int     ,			)				->							List[DPRSpanPrediction]:




     '''simple docstring'''

     A__	=    []
     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)				)
     A__	=    sorted(snake_case_     ,			key=lambda snake_case_				: x[1]     ,			reverse=snake_case_				)
     A__	=    []
     for (start_index, end_index), score in scores:
      if start_index > end_index:
       raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""				)
      A__	=    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      UpperCAmelCase_  (						A_,       A_							):
    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''']











"""simple docstring"""



import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 return x + 2






class      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	Any				)				->							Any:




     '''simple docstring'''

     A__	=    "x = 3"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result == 3
     self.assertDictEqual(snake_case_     ,			{"x": 3}				)

     A__	=    "x = y"
     A__	=    {"y": 5}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 5, "y": 5}				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Any:




     '''simple docstring'''

     A__	=    "y = add_two(x)"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 5}				)

     # Won't work without the tool
     with CaptureStdout() as out:
      A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result is None
     assert "tried to execute add_two" in out.out




    def 							__magic_name__							(   self						:	Dict				)				->							List[str]:




     '''simple docstring'''

     A__	=    "x = 3"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result == 3
     self.assertDictEqual(snake_case_     ,			{"x": 3}				)




    def 							__magic_name__							(   self						:	Dict				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    "test_dict = {'x': x, 'y': add_two(x)}"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 5}				)
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_dict": {"x": 3, "y": 5}}				)




    def 							__magic_name__							(   self						:	int				)				->							str:




     '''simple docstring'''

     A__	=    "x = 3\ny = 5"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 5}				)




    def 							__magic_name__							(   self						:	Any				)				->							List[Any]:




     '''simple docstring'''

     A__	=    "text = f'This is x: {x}.'"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == "This is x: 3."
     self.assertDictEqual(snake_case_     ,			{"x": 3, "text": "This is x: 3."}				)




    def 							__magic_name__							(   self						:	str				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    "if x <= 3:\n    y = 2\nelse:\n    y = 5"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 2
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 2}				)

     A__	=    {"x": 8}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 8, "y": 5}				)




    def 							__magic_name__							(   self						:	List[str]				)				->							Tuple:




     '''simple docstring'''

     A__	=    "test_list = [x, add_two(x)]"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     self.assertListEqual(snake_case_     ,			[3, 5]				)
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_list": [3, 5]}				)




    def 							__magic_name__							(   self						:	Tuple				)				->							int:




     '''simple docstring'''

     A__	=    "y = x"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result == 3
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 3}				)




    def 							__magic_name__							(   self						:	str				)				->							Dict:




     '''simple docstring'''

     A__	=    "test_list = [x, add_two(x)]\ntest_list[1]"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_list": [3, 5]}				)

     A__	=    "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_dict": {"x": 3, "y": 5}}				)




    def 							__magic_name__							(   self						:	Optional[int]				)				->							Tuple:




     '''simple docstring'''

     A__	=    "x = 0\nfor i in range(3):\n    x = i"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{"range": range}     ,			state=snake_case_				)
     assert result == 2
     self.assertDictEqual(snake_case_     ,			{"x": 2, "i": 2}				)





