


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


_snake_case  :	Optional[int]			      =       logging.get_logger(__name__)

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

_snake_case  :	List[Any]			      =       {
    "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"
        ),
    },
}
_snake_case  :	Union[str, Any]			      =       {
    "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"
        ),
    },
}
_snake_case  :	Any			      =       {
    "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"
        ),
    },
}

_snake_case  :	List[str]			      =       {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
_snake_case  :	str			      =       {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
_snake_case  :	List[str]			      =       {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


_snake_case  :	Dict			      =       {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_snake_case  :	int			      =       {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_snake_case  :	Any			      =       {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							int  						=						VOCAB_FILES_NAMES
							__UpperCAmelCase					:							Dict  						=						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase					:							Any  						=						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase					:							str  						=						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							int  						=						VOCAB_FILES_NAMES
							__UpperCAmelCase					:							str  						=						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase					:							Any  						=						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase					:							int  						=						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
_snake_case  :	List[str]			      =       collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

_snake_case  :	Tuple			      =       collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


_snake_case  :	List[Any]			      =       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(_lowerCAmelCase   )
class        a	:




							"""simple docstring"""


							def __call__(							self :						List[str]					,			lowerCamelCase :						Any					,			lowerCamelCase :						Optional[str] = None					,			lowerCamelCase :						Optional[str] = None					,			lowerCamelCase :						Union[bool, str] = False					,			lowerCamelCase :						Union[bool, str] = False					,			lowerCamelCase :						Optional[int] = None					,			lowerCamelCase :						Optional[Union[str, TensorType]] = None					,			lowerCamelCase :						Optional[bool] = None					,			**lowerCamelCase :						Any					,			)			->					BatchEncoding:
														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:
																					__snake_case	:    List[str]											=     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					,			)
														__snake_case	:    int											=     titles if not isinstance(lowerCamelCase					,			lowerCamelCase       ) else [titles]
														__snake_case	:    List[str]											=     texts if not isinstance(lowerCamelCase					,			lowerCamelCase       ) else [texts]
														__snake_case	:    Optional[Any]											=     len(lowerCamelCase       )
														__snake_case	:    Optional[int]											=     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.'       )
														__snake_case	:    List[Any]											=     super().__call__(lowerCamelCase					,			lowerCamelCase					,			padding=lowerCamelCase					,			truncation=lowerCamelCase       )["input_ids"]
														__snake_case	:    int											=     super().__call__(lowerCamelCase					,			add_special_tokens=lowerCamelCase					,			padding=lowerCamelCase					,			truncation=lowerCamelCase       )["input_ids"]
														__snake_case	:    Tuple											=     {
														    "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:
																					__snake_case	:    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]       )
																					__snake_case	:    int											=     attention_mask
														return self.pad(lowerCamelCase					,			padding=lowerCamelCase					,			max_length=lowerCamelCase					,			return_tensors=lowerCamelCase       )

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

																					__snake_case	:    Optional[Any]											=     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 :						Any					,			lowerCamelCase :						List[int]					,			lowerCamelCase :						List[int]					,			lowerCamelCase :						int					,			lowerCamelCase :						int					,			)			->					List[DPRSpanPrediction]:
														__snake_case	:    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)       )
														__snake_case	:    Union[str, Any]											=     sorted(lowerCamelCase					,			key=lambda lowerCamelCase       : x[1]					,			reverse=lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     []
														for (start_index, end_index), score in scores:
																					if start_index > end_index:
																												raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]'       )
																					__snake_case	:    Union[str, 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(lowerCamelCase       ) == top_spans:
																												break
														return chosen_span_intervals

@add_end_docstrings(_lowerCAmelCase   )
class        a	(_lowerCAmelCase  ,       _lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Optional[Any]  						=						VOCAB_FILES_NAMES
							__UpperCAmelCase					:							Dict  						=						READER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase					:							Union[str, Any]  						=						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase					:							Any  						=						READER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase					:							Any  						=						["input_ids", "attention_mask"]





import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


_snake_case  :	List[str]			      =       logging.get_logger(__name__)

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def __init__(							self :						int					,			*lowerCamelCase :						Any					,			**lowerCamelCase :						Union[str, Any]       )			->					None:
														warnings.warn(
														    "The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
														    " Please use PoolFormerImageProcessor instead."					,			lowerCamelCase					,			)
														super().__init__(*lowerCamelCase					,			**lowerCamelCase       )

