





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


UpperCamelCase__     =	logging.get_logger(__name__)

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

UpperCamelCase__     =	{
    "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"
        ),
    },
}
UpperCamelCase__     =	{
    "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"
        ),
    },
}
UpperCamelCase__     =	{
    "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"
        ),
    },
}

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


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

class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : List[str]         =    VOCAB_FILES_NAMES
							snake_case       : List[Any]         =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							snake_case       : Dict         =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case       : Optional[Any]         =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : Optional[int]         =    VOCAB_FILES_NAMES
							snake_case       : Union[str, Any]         =    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							snake_case       : Tuple         =    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case       : Union[str, Any]         =    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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

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


UpperCamelCase__     =	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     __SCREAMING_SNAKE_CASE   :






							def __call__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = False					,	__lowerCAmelCase = False					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	**__lowerCAmelCase					,	):
														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:
																					UpperCamelCase__			       =     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_					,	)
														UpperCamelCase__			       =     titles if not isinstance(snake_case_					,	snake_case_					) else [titles]
														UpperCamelCase__			       =     texts if not isinstance(snake_case_					,	snake_case_					) else [texts]
														UpperCamelCase__			       =     len(snake_case_					)
														UpperCamelCase__			       =     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."""					)
														UpperCamelCase__			       =     super().__call__(snake_case_					,	snake_case_					,	padding=snake_case_					,	truncation=snake_case_					)["""input_ids"""]
														UpperCamelCase__			       =     super().__call__(snake_case_					,	add_special_tokens=snake_case_					,	padding=snake_case_					,	truncation=snake_case_					)["""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(snake_case_					,	snake_case_					)
														    ]
														}
														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(snake_case_					,	padding=snake_case_					,	max_length=snake_case_					,	return_tensors=snake_case_					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase = 16					,	__lowerCAmelCase = 64					,	__lowerCAmelCase = 4					,	):
														UpperCamelCase__			       =     reader_input["""input_ids"""]
														UpperCamelCase__			       =     reader_output[:3]
														UpperCamelCase__			       =     len(snake_case_					)
														UpperCamelCase__			       =     sorted(range(snake_case_					)					,	reverse=snake_case_					,	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(snake_case_					)

																					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=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 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	):
														UpperCamelCase__			       =     []
														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)					)
														UpperCamelCase__			       =     sorted(snake_case_					,	key=lambda __lowerCAmelCase					: x[1]					,	reverse=snake_case_					)
														UpperCamelCase__			       =     []
														for (start_index, end_index), score in scores:
																					if start_index > end_index:
																												raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""					)
																					UpperCamelCase__			       =     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     __SCREAMING_SNAKE_CASE   (       _a				,						_a    ):
							snake_case       : Optional[Any]         =    VOCAB_FILES_NAMES
							snake_case       : str         =    READER_PRETRAINED_VOCAB_FILES_MAP
							snake_case       : Optional[int]         =    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case       : Optional[int]         =    READER_PRETRAINED_INIT_CONFIGURATION
							snake_case       : Tuple         =    ["""input_ids""", """attention_mask"""]











from datetime import datetime as dt
import os

from github import Github


UpperCamelCase__     =	[
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]





def       _UpperCamelCase       ():






							"""simple docstring"""





							UpperCamelCase__			       =     Github(os.environ["""GITHUB_TOKEN"""]    )
							UpperCamelCase__			       =     g.get_repo("""huggingface/transformers"""    )
							UpperCamelCase__			       =     repo.get_issues(state="""open"""    )

							for issue in open_issues:
														UpperCamelCase__			       =     sorted([comment for comment in issue.get_comments()]				,     key=lambda a__    : i.created_at				,     reverse=a__    )
														UpperCamelCase__			       =     comments[0] if len(a__    ) > 0 else None
														if (
														    last_comment is not None
														    and last_comment.user.login == "github-actions[bot]"
														    and (dt.utcnow() - issue.updated_at).days > 7
														    and (dt.utcnow() - issue.created_at).days >= 30
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
														):
																					# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
																					issue.edit(state="""closed"""    )
														elif (
														    (dt.utcnow() - issue.updated_at).days > 23
														    and (dt.utcnow() - issue.created_at).days >= 30
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
														):
																					# print(f"Would add stale comment to {issue.number}")
																					issue.create_comment(
																					    """This issue has been automatically marked as stale because it has not had """
																					    """recent activity. If you think this still needs to be addressed """
																					    """please comment on this thread.\n\nPlease note that issues that do not follow the """
																					    """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
																					    """are likely to be ignored."""    )


if __name__ == "__main__":
					main()




