






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


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 				lowerCAmelCase_	(		a_			):
				_UpperCamelCase					:       Tuple							       = VOCAB_FILES_NAMES
				_UpperCamelCase					:       Optional[Any]							       = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				_UpperCamelCase					:       List[Any]							       = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_UpperCamelCase					:       Any							       = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				_UpperCamelCase					:       List[str]							       = DPRContextEncoderTokenizer




class 				lowerCAmelCase_	(		a_			):
				_UpperCamelCase					:       Optional[int]							       = VOCAB_FILES_NAMES
				_UpperCamelCase					:       List[str]							       = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				_UpperCamelCase					:       Optional[int]							       = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_UpperCamelCase					:       str							       = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				_UpperCamelCase					:       Any							       = DPRQuestionEncoderTokenizer




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    [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 				lowerCAmelCase_	:


				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:
																		_lowercase							:   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_ ,						)
											_lowercase							:   Optional[Any] 							=   titles if not isinstance(lowerCAmelCase_ ,						lowerCAmelCase_      ) else [titles]
											_lowercase							:   List[str] 							=   texts if not isinstance(lowerCAmelCase_ ,						lowerCAmelCase_      ) else [texts]
											_lowercase							:   Union[str, Any] 							=   len(lowerCAmelCase_      )
											_lowercase							:   str 							=   questions if not isinstance(lowerCAmelCase_ ,						lowerCAmelCase_      ) else [questions] * n_passages
											assert len(lowerCAmelCase_      ) == len(
											    lowerCAmelCase_      ), F"""There should be as many titles than texts but got {len(lowerCAmelCase_      )} titles and {len(lowerCAmelCase_      )} texts."""
											_lowercase							:   Optional[int] 							=   super().__call__(lowerCAmelCase_ ,						lowerCAmelCase_ ,						padding=lowerCAmelCase_ ,						truncation=lowerCAmelCase_      )['input_ids']
											_lowercase							:   Union[str, Any] 							=   super().__call__(lowerCAmelCase_ ,						add_special_tokens=lowerCAmelCase_ ,						padding=lowerCAmelCase_ ,						truncation=lowerCAmelCase_      )['input_ids']
											_lowercase							:   Optional[int] 							=   {
											    '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:
																		_lowercase							:   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]      )
																		_lowercase							:   Optional[Any] 							=   attention_mask
											return self.pad(lowerCAmelCase_ ,						padding=lowerCAmelCase_ ,						max_length=lowerCAmelCase_ ,						return_tensors=lowerCAmelCase_      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase = 1_6 ,						_lowerCAmelCase = 6_4 ,						_lowerCAmelCase = 4 ,						):
											_lowercase							:   Any 							=   reader_input['input_ids']
											_lowercase							,							_lowercase							,							_lowercase							:   Dict 							=   reader_output[:3]
											_lowercase							:   Optional[Any] 							=   len(lowerCAmelCase_      )
											_lowercase							:   Any 							=   sorted(range(lowerCAmelCase_      ) ,						reverse=lowerCAmelCase_ ,						key=relevance_logits.__getitem__      )
											_lowercase							:   Optional[int] 							=   []
											for doc_id in sorted_docs:
																		_lowercase							:   str 							=   list(input_ids[doc_id]      )
																		# assuming question & title information is at the beginning of the sequence
																		_lowercase							:   Tuple 							=   sequence_ids.index(self.sep_token_id ,						2      ) + 1  # second sep id
																		if sequence_ids[-1] == self.pad_token_id:
																									_lowercase							:   Optional[int] 							=   sequence_ids.index(self.pad_token_id      )
																		else:
																									_lowercase							:   Any 							=   len(lowerCAmelCase_      )

																		_lowercase							:   List[str] 							=   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 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						):
											_lowercase							:   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)      )
											_lowercase							:   Optional[Any] 							=   sorted(lowerCAmelCase_ ,						key=lambda _lowerCAmelCase      : x[1] ,						reverse=lowerCAmelCase_      )
											_lowercase							:   Union[str, Any] 							=   []
											for (start_index, end_index), score in scores:
																		assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
																		_lowercase							:   Tuple 							=   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(lowerCAmelCase_      ) == top_spans:
																									break
											return chosen_span_intervals




@add_end_docstrings(a_			)
class 				lowerCAmelCase_	(		a_   ,		a_			):
				_UpperCamelCase					:       Union[str, Any]							       = VOCAB_FILES_NAMES
				_UpperCamelCase					:       Dict							       = READER_PRETRAINED_VOCAB_FILES_MAP
				_UpperCamelCase					:       Tuple							       = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_UpperCamelCase					:       Optional[Any]							       = READER_PRETRAINED_INIT_CONFIGURATION
				_UpperCamelCase					:       str							       = ['''input_ids''', '''attention_mask''']
				_UpperCamelCase					:       Optional[int]							       = DPRReaderTokenizer








'''simple docstring'''





def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			return int((input_a, input_a).count(1 ) != 0 )






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			assert or_gate(0,0 ) == 0
			assert or_gate(0,1 ) == 1
			assert or_gate(1,0 ) == 1
			assert or_gate(1,1 ) == 1


if __name__ == "__main__":
					print(or_gate(0, 1))
					print(or_gate(1, 0))
					print(or_gate(0, 0))
					print(or_gate(1, 1))



