


'''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


__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': 5_12,
    'facebook/dpr-ctx_encoder-multiset-base': 5_12,
}
__A	={
    'facebook/dpr-question_encoder-single-nq-base': 5_12,
    'facebook/dpr-question_encoder-multiset-base': 5_12,
}
__A	={
    'facebook/dpr-reader-single-nq-base': 5_12,
    'facebook/dpr-reader-multiset-base': 5_12,
}


__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 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict   =					VOCAB_FILES_NAMES
					lowerCAmelCase      :Union[str, Any]   =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase      :Optional[int]   =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase      :List[Any]   =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Optional[int]   =					VOCAB_FILES_NAMES
					lowerCAmelCase      :Optional[int]   =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase      :Optional[int]   =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase      :Dict   =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

__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'\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 		_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__(
											    _lowerCamelCase							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=_lowerCamelCase							,							return_tensors=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							**_lowerCamelCase							,							)
								elif titles is None or texts is None:
											UpperCAmelCase__  :					Optional[int]        =     titles if texts is None else texts
											return super().__call__(
											    _lowerCamelCase							,							_lowerCamelCase							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=_lowerCamelCase							,							return_tensors=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							**_lowerCamelCase							,							)
								UpperCAmelCase__  :					Tuple        =     titles if not isinstance(_lowerCamelCase							,							_lowerCamelCase) else [titles]
								UpperCAmelCase__  :					List[Any]        =     texts if not isinstance(_lowerCamelCase							,							_lowerCamelCase) else [texts]
								UpperCAmelCase__  :					Any        =     len(_lowerCamelCase)
								UpperCAmelCase__  :					Dict        =     questions if not isinstance(_lowerCamelCase							,							_lowerCamelCase) else [questions] * n_passages
								if len(_lowerCamelCase) != len(_lowerCamelCase):
											raise ValueError(
											    f'''There should be as many titles than texts but got {len(_lowerCamelCase)} titles and {len(_lowerCamelCase)} texts.''')
								UpperCAmelCase__  :					int        =     super().__call__(_lowerCamelCase							,							_lowerCamelCase							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase)["""input_ids"""]
								UpperCAmelCase__  :					str        =     super().__call__(_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase)["""input_ids"""]
								UpperCAmelCase__  :					Union[str, Any]        =     {
								    """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(_lowerCamelCase							,							_lowerCamelCase)
								    ]
								}
								if return_attention_mask is not False:
											UpperCAmelCase__  :					str        =     []
											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__  :					Any        =     attention_mask
								return self.pad(_lowerCamelCase							,							padding=_lowerCamelCase							,							max_length=_lowerCamelCase							,							return_tensors=_lowerCamelCase)




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

											UpperCAmelCase__  :					Optional[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=_lowerCamelCase							,							top_spans=_lowerCamelCase							,							)
											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=_lowerCamelCase							,							start_index=_lowerCamelCase							,							end_index=_lowerCamelCase							,							text=self.decode(sequence_ids[start_index : end_index + 1])							,							))
											if len(_lowerCamelCase) >= num_spans:
														break
								return nbest_spans_predictions[:num_spans]




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							):
								UpperCAmelCase__  :					Tuple        =     []
								for start_index, start_score in enumerate(_lowerCamelCase):
											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__  :					str        =     sorted(_lowerCamelCase							,							key=lambda _lowerCamelCase: x[1]							,							reverse=_lowerCamelCase)
								UpperCAmelCase__  :					Tuple        =     []
								for (start_index, end_index), score in scores:
											if start_index > end_index:
														raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]''')
											UpperCAmelCase__  :					Tuple        =     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(_lowerCamelCase) == top_spans:
														break
								return chosen_span_intervals


@add_end_docstrings(a__	)
class 		_snake_case					(     a__       ,   a__	):
					lowerCAmelCase      :List[Any]   =					VOCAB_FILES_NAMES
					lowerCAmelCase      :Optional[int]   =					READER_PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase      :Union[str, Any]   =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase      :Optional[int]   =					READER_PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase      :Tuple   =					['''input_ids''', '''attention_mask''']



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			if height >= 1:
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						move_disk(UpperCamelCase__ ,      UpperCamelCase__					)
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			print("""moving disk from""" ,      UpperCamelCase__ ,      """to""" ,      UpperCamelCase__					)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[str]        =     int(input("""Height of hanoi: """					).strip()					)
			move_tower(UpperCamelCase__ ,      """A""" ,      """B""" ,      """C"""					)


if __name__ == "__main__":
		main()