



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


__lowerCamelCase     :							List[Any]  							=				logging.get_logger(__name__)

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

__lowerCamelCase     :							int  							=				{
    "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"
        ),
    },
}
__lowerCamelCase     :							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"
        ),
    },
}
__lowerCamelCase     :							Union[str, 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"
        ),
    },
}

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


__lowerCamelCase     :							Optional[Any]  							=				{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__lowerCamelCase     :							Optional[int]  							=				{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__lowerCamelCase     :							List[Any]  							=				{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}





class  __magic_name__   (							_A						):
		lowercase      :       List[Any]          =VOCAB_FILES_NAMES
		lowercase      :       List[str]          =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		lowercase      :       Tuple          =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase      :       int          =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
		lowercase      :       Optional[int]          =DPRContextEncoderTokenizer





class  __magic_name__   (							_A						):
		lowercase      :       str          =VOCAB_FILES_NAMES
		lowercase      :       Optional[int]          =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		lowercase      :       Optional[int]          =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase      :       Any          =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
		lowercase      :       int          =DPRQuestionEncoderTokenizer
__lowerCamelCase     :							Tuple  							=				collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

__lowerCamelCase     :							Dict  							=				collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


__lowerCamelCase     :							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    [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  __magic_name__   :






		def __call__(						self      :     Any				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     str = None				,		UpperCamelCase__      :     Any = None				,		UpperCamelCase__      :     Optional[Any] = False				,		UpperCamelCase__      :     int = False				,		UpperCamelCase__      :     str = None				,		UpperCamelCase__      :     Union[str, Any] = None				,		UpperCamelCase__      :     Optional[Any] = None				,		**UpperCamelCase__      :     int				,		)				->					BatchEncoding:
								'''simple docstring'''







								if titles is None and texts is None:
														return super().__call__(
														    UpperCamelCase__				,		padding=UpperCamelCase__				,		truncation=UpperCamelCase__				,		max_length=UpperCamelCase__				,		return_tensors=UpperCamelCase__				,		return_attention_mask=UpperCamelCase__				,		**UpperCamelCase__				,		)
								elif titles is None or texts is None:
														UpperCAmelCase         =    titles if texts is None else texts
														return super().__call__(
														    UpperCamelCase__				,		UpperCamelCase__				,		padding=UpperCamelCase__				,		truncation=UpperCamelCase__				,		max_length=UpperCamelCase__				,		return_tensors=UpperCamelCase__				,		return_attention_mask=UpperCamelCase__				,		**UpperCamelCase__				,		)
								UpperCAmelCase         =    titles if not isinstance(UpperCamelCase__				,		UpperCamelCase__      ) else [titles]
								UpperCAmelCase         =    texts if not isinstance(UpperCamelCase__				,		UpperCamelCase__      ) else [texts]
								UpperCAmelCase         =    len(UpperCamelCase__      )
								UpperCAmelCase         =    questions if not isinstance(UpperCamelCase__				,		UpperCamelCase__      ) else [questions] * n_passages
								assert len(UpperCamelCase__      ) == len(
								    UpperCamelCase__      ), F'There should be as many titles than texts but got {len(UpperCamelCase__      )} titles and {len(UpperCamelCase__      )} texts.'
								UpperCAmelCase         =    super().__call__(UpperCamelCase__				,		UpperCamelCase__				,		padding=UpperCamelCase__				,		truncation=UpperCamelCase__      )["input_ids"]
								UpperCAmelCase         =    super().__call__(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__				,		padding=UpperCamelCase__				,		truncation=UpperCamelCase__      )["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(UpperCamelCase__				,		UpperCamelCase__      )
								    ]
								}
								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(UpperCamelCase__				,		padding=UpperCamelCase__				,		max_length=UpperCamelCase__				,		return_tensors=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     List[Any] = 16				,		UpperCamelCase__      :     str = 64				,		UpperCamelCase__      :     Dict = 4				,		)				->					List[DPRSpanPrediction]:
								'''simple docstring'''







								UpperCAmelCase         =    reader_input["input_ids"]
								UpperCAmelCase         =    reader_output[:3]
								UpperCAmelCase         =    len(UpperCamelCase__      )
								UpperCAmelCase         =    sorted(range(UpperCamelCase__      )				,		reverse=UpperCamelCase__				,		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(UpperCamelCase__      )

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







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Union[str, Any]				,		)				->					List[DPRSpanPrediction]:
								'''simple docstring'''







								UpperCAmelCase         =    []
								for start_index, start_score in enumerate(UpperCamelCase__      ):
														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(UpperCamelCase__				,		key=lambda UpperCamelCase__      : x[1]				,		reverse=UpperCamelCase__      )
								UpperCAmelCase         =    []
								for (start_index, end_index), score in scores:
														assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
														UpperCAmelCase         =    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(UpperCamelCase__      ) == top_spans:
																				break
								return chosen_span_intervals





@add_end_docstrings(_A						)
class  __magic_name__   (							_A,  _A						):
		lowercase      :       str          =VOCAB_FILES_NAMES
		lowercase      :       Any          =READER_PRETRAINED_VOCAB_FILES_MAP
		lowercase      :       Optional[int]          =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase      :       List[Any]          =READER_PRETRAINED_INIT_CONFIGURATION
		lowercase      :       List[Any]          =['''input_ids''', '''attention_mask''']
		lowercase      :       Dict          =DPRReaderTokenizer








import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation





class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("swish"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.SiLU      )

								self.assertEqual(act(torch.tensor(-1_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("silu"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.SiLU      )

								self.assertEqual(act(torch.tensor(-1_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("mish"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.Mish      )

								self.assertEqual(act(torch.tensor(-2_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )







		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("gelu"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.GELU      )

								self.assertEqual(act(torch.tensor(-1_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )



