





"""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


_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_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Optional[Any]									=		VOCAB_FILES_NAMES
				__snake_case :							str									=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__snake_case :							str									=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__snake_case :							Optional[Any]									=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION

class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Any									=		VOCAB_FILES_NAMES
				__snake_case :							List[Any]									=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__snake_case :							Optional[Any]									=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__snake_case :							Tuple									=		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(snake_case__ )
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""




				def __call__(  self	:Tuple    ,     __lowercase	:int    ,     __lowercase	:Optional[str] = None    ,     __lowercase	:Optional[str] = None    ,     __lowercase	:Union[bool, str] = False    ,     __lowercase	:Union[bool, str] = False    ,     __lowercase	:Optional[int] = None    ,     __lowercase	:Optional[Union[str, TensorType]] = None    ,     __lowercase	:Optional[bool] = None    ,     **__lowercase	:List[Any]    ,     ):
											if titles is None and texts is None:
																		return super().__call__(
																		    __lowercase    ,     padding=__lowercase    ,     truncation=__lowercase    ,     max_length=__lowercase    ,     return_tensors=__lowercase    ,     return_attention_mask=__lowercase    ,     **__lowercase    ,     )
											elif titles is None or texts is None:
																		__lowerCamelCase   :   Any       =titles if texts is None else texts
																		return super().__call__(
																		    __lowercase    ,     __lowercase    ,     padding=__lowercase    ,     truncation=__lowercase    ,     max_length=__lowercase    ,     return_tensors=__lowercase    ,     return_attention_mask=__lowercase    ,     **__lowercase    ,     )
											__lowerCamelCase   :   Union[str, Any]       =titles if not isinstance(__lowercase    ,     __lowercase      ) else [titles]
											__lowerCamelCase   :   int       =texts if not isinstance(__lowercase    ,     __lowercase      ) else [texts]
											__lowerCamelCase   :   str       =len(__lowercase      )
											__lowerCamelCase   :   Union[str, Any]       =questions if not isinstance(__lowercase    ,     __lowercase      ) else [questions] * n_passages
											if len(__lowercase      ) != len(__lowercase      ):
																		raise ValueError(
																		    f'There should be as many titles than texts but got {len(__lowercase      )} titles and {len(__lowercase      )} texts.'      )
											__lowerCamelCase   :   List[Any]       =super().__call__(__lowercase    ,     __lowercase    ,     padding=__lowercase    ,     truncation=__lowercase      )['''input_ids''']
											__lowerCamelCase   :   List[str]       =super().__call__(__lowercase    ,     add_special_tokens=__lowercase    ,     padding=__lowercase    ,     truncation=__lowercase      )['''input_ids''']
											__lowerCamelCase   :   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(__lowercase    ,     __lowercase      )
											    ]
											}
											if return_attention_mask is not False:
																		__lowerCamelCase   :   Optional[int]       =[]
																		for input_ids in encoded_inputs["input_ids"]:
																									attention_mask.append([int(input_id != self.pad_token_id      ) for input_id in input_ids]      )
																		__lowerCamelCase   :   int       =attention_mask
											return self.pad(__lowercase    ,     padding=__lowercase    ,     max_length=__lowercase    ,     return_tensors=__lowercase      )




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

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




				def        __lowercase	(  self	:Dict    ,     __lowercase	:List[int]    ,     __lowercase	:List[int]    ,     __lowercase	:int    ,     __lowercase	:int    ,     ):
											__lowerCamelCase   :   Any       =[]
											for start_index, start_score in enumerate(__lowercase      ):
																		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)      )
											__lowerCamelCase   :   str       =sorted(__lowercase    ,     key=lambda __lowercase      : x[1]    ,     reverse=__lowercase      )
											__lowerCamelCase   :   str       =[]
											for (start_index, end_index), score in scores:
																		if start_index > end_index:
																									raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]'      )
																		__lowerCamelCase   :   Any       =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(__lowercase      ) == top_spans:
																									break
											return chosen_span_intervals

@add_end_docstrings(snake_case__ )
class 		SCREAMING_SNAKE_CASE_				(     snake_case__							,	snake_case__ ):
				"""simple docstring"""
				__snake_case :							Optional[int]									=		VOCAB_FILES_NAMES
				__snake_case :							Any									=		READER_PRETRAINED_VOCAB_FILES_MAP
				__snake_case :							Tuple									=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__snake_case :							str									=		READER_PRETRAINED_INIT_CONFIGURATION
				__snake_case :							List[str]									=		["""input_ids""", """attention_mask"""]











"""simple docstring"""





from __future__ import annotations



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list[int | str] ):


							'''simple docstring'''







							create_state_space_tree(SCREAMING_SNAKE_CASE     ,    []     ,    0     ,    [0 for i in range(len(SCREAMING_SNAKE_CASE ) )] )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list[int | str]     ,    SCREAMING_SNAKE_CASE							:				list[int | str]     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				list[int]     ,    ):


							'''simple docstring'''







							if index == len(SCREAMING_SNAKE_CASE ):
														print(SCREAMING_SNAKE_CASE )
														return

							for i in range(len(SCREAMING_SNAKE_CASE ) ):
														if not index_used[i]:
																					current_sequence.append(sequence[i] )
																					__lowerCamelCase   :   List[Any]       =True
																					create_state_space_tree(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    index + 1     ,    SCREAMING_SNAKE_CASE )
																					current_sequence.pop()
																					__lowerCamelCase   :   List[Any]       =False
_UpperCamelCase					      =   [3, 1, 2, 4]
generate_all_permutations(sequence)

_UpperCamelCase					      =   ["A", "B", "C"]
generate_all_permutations(sequence_a)




