

import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__A			:					Any						=			logging.get_logger(__name__)

__A			:					Union[str, Any]						=			{'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

__A			:					Optional[int]						=			{
    'vocab_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
    },
    'emoji_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
    },
}

__A			:					Union[str, Any]						=			{
    'abeja/gpt-neox-japanese-2.7b': 2_0_4_8,
}





def 	__a     (      A__			:  List[str]			,  A__			:  List[str]       ):
    with open(A__			,  "r"			,  encoding="utf-8"       ) as f:
        SCREAMING_SNAKE_CASE										=   json.loads(f.read()       )

    SCREAMING_SNAKE_CASE										=   collections.OrderedDict()
    SCREAMING_SNAKE_CASE										=   collections.OrderedDict()
    SCREAMING_SNAKE_CASE										=   collections.OrderedDict()
    with open(A__			,  "r"			,  encoding="utf-8"       ) as f:
        SCREAMING_SNAKE_CASE										=   f.readlines()
    SCREAMING_SNAKE_CASE										=   [[t.rstrip("\n"       )] if (t == "," or "," not in t) else t.rstrip("\n"       ).split(","       ) for t in token]
    for idx, b in enumerate(A__       ):
        SCREAMING_SNAKE_CASE										=   b
        SCREAMING_SNAKE_CASE										=   idx
        for wd in b:
            SCREAMING_SNAKE_CASE										=   idx

    return vocab, raw_vocab, ids_to_tokens, emoji





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      ["input_ids", "attention_mask"]





   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Optional[int]="<|endoftext|>"     , __lowerCamelCase					:			int="<|endoftext|>"     , __lowerCamelCase					:			Dict="<|startoftext|>"     , __lowerCamelCase					:			Any="<|endoftext|>"     , __lowerCamelCase					:			List[str]=False     , **__lowerCamelCase					:			str     , ):
       super().__init__(
           unk_token=__lowerCamelCase     , pad_token=__lowerCamelCase     , bos_token=__lowerCamelCase     , eos_token=__lowerCamelCase     , do_clean_text=__lowerCamelCase     , **__lowerCamelCase     , )
       if not os.path.isfile(__lowerCamelCase			):
           raise ValueError(
               f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
               " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"			)
       if not os.path.isfile(__lowerCamelCase			):
           raise ValueError(
               f"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"
               " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"			)
       SCREAMING_SNAKE_CASE										=   do_clean_text
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   load_vocab_and_emoji(__lowerCamelCase     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   SubWordJapaneseTokenizer(
           vocab=self.vocab     , ids_to_tokens=self.ids_to_tokens     , emoji=self.emoji			)





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
       return len(self.raw_vocab			)





   def      _snake_case		(       self					:			Optional[Any]			):
       return dict(self.raw_vocab     , **self.added_tokens_encoder			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			str			):
       return self.subword_tokenizer.tokenize(__lowerCamelCase     , clean=self.do_clean_text			)





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			List[Any]			):
       return self.vocab.get(__lowerCamelCase     , self.vocab.get(self.unk_token			)			)





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Dict			):
       return self.subword_tokenizer.convert_id_to_token(__lowerCamelCase			)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   "".join(__lowerCamelCase			).strip()
       return out_string





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			"Conversation"			):
       SCREAMING_SNAKE_CASE										=   []
       for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(__lowerCamelCase     , add_special_tokens=__lowerCamelCase			) + [self.eos_token_id]			)

       if len(__lowerCamelCase			) > self.model_max_length:
           SCREAMING_SNAKE_CASE										=   input_ids[-self.model_max_length :]
       return input_ids





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[str] = None			):
       SCREAMING_SNAKE_CASE										=   0
       if os.path.isdir(__lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   os.path.join(
               __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
           SCREAMING_SNAKE_CASE										=   os.path.join(
               __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]			)
       else:
           SCREAMING_SNAKE_CASE										=   (
               (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
           )
           SCREAMING_SNAKE_CASE										=   (
               (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
           )
       with open(__lowerCamelCase     , "w"     , encoding="utf-8"			) as writer:
           for token_index, token in self.ids_to_tokens.items():
               if index != token_index:
                   logger.warning(
                       f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                       " Please check that the vocabulary is not corrupted!"			)
                   SCREAMING_SNAKE_CASE										=   token_index
               writer.write(",".join(__lowerCamelCase			) + "\n"			)
               index += 1
       with open(__lowerCamelCase     , "w"     , encoding="utf-8"			) as writer:
           json.dump(self.emoji     , __lowerCamelCase			)
       return vocab_file, emoji_file





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Any     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   vocab  # same as swe
       SCREAMING_SNAKE_CASE										=   ids_to_tokens  # same as bpe
       SCREAMING_SNAKE_CASE										=   emoji
       SCREAMING_SNAKE_CASE										=   np.max([len(__lowerCamelCase			) for w in self.vocab.keys()]			)
       SCREAMING_SNAKE_CASE										=   re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"			)
       SCREAMING_SNAKE_CASE										=   re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"			)
       SCREAMING_SNAKE_CASE										=   re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"			)
       SCREAMING_SNAKE_CASE										=   re.compile(
           r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"			)
       SCREAMING_SNAKE_CASE										=   re.compile(
           r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"			)
       SCREAMING_SNAKE_CASE										=   re.compile(
           r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"			)
       SCREAMING_SNAKE_CASE										=   "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
       SCREAMING_SNAKE_CASE										=   "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
       SCREAMING_SNAKE_CASE										=   str.maketrans({k: "<BLOCK>" for k in keisen + blocks}			)





   def __len__(       self					:			Any			):
       return len(self.ids_to_tokens			)





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   self.content_repattera.sub("<URL>"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.content_repattera.sub("<EMAIL>"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.content_repattera.sub("<TEL>"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.content_repattera.sub("<DATE>"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.content_repattera.sub("<DATE>"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.content_repattera.sub("<PRICE>"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   content.translate(self.content_transa			)
       while "<BLOCK><BLOCK>" in content:
           SCREAMING_SNAKE_CASE										=   content.replace("<BLOCK><BLOCK>"     , "<BLOCK>"			)
       return content





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Optional[Any]=False			):
       SCREAMING_SNAKE_CASE										=   text.replace(" "     , "<SP>"			)
       SCREAMING_SNAKE_CASE										=   text.replace("　"     , "<SP>"			)
       SCREAMING_SNAKE_CASE										=   text.replace("\r\n"     , "<BR>"			)
       SCREAMING_SNAKE_CASE										=   text.replace("\n"     , "<BR>"			)
       SCREAMING_SNAKE_CASE										=   text.replace("\r"     , "<BR>"			)
       SCREAMING_SNAKE_CASE										=   text.replace("\t"     , "<TAB>"			)
       SCREAMING_SNAKE_CASE										=   text.replace("—"     , "ー"			)
       SCREAMING_SNAKE_CASE										=   text.replace("−"     , "ー"			)
       for k, v in self.emoji["emoji"].items():
           if k in text:
               SCREAMING_SNAKE_CASE										=   text.replace(__lowerCamelCase     , __lowerCamelCase			)
       if clean:
           SCREAMING_SNAKE_CASE										=   self.clean_text(__lowerCamelCase			)

       def check_simbol(__lowerCamelCase					:			Any			):
           SCREAMING_SNAKE_CASE										=   x.encode()
           if len(__lowerCamelCase			) == 1 and len(__lowerCamelCase			) == 2:
               SCREAMING_SNAKE_CASE										=   (int(e[0]			) << 8) + int(e[1]			)
               if (
                   (c >= 0xC_2A1 and c <= 0xC_2BF)
                   or (c >= 0xC_780 and c <= 0xC_783)
                   or (c >= 0xC_AB9 and c <= 0xC_BBF)
                   or (c >= 0xC_C80 and c <= 0xC_DA2)
               ):
                   return True
           return False

       def checkuae(__lowerCamelCase					:			Dict			):
           SCREAMING_SNAKE_CASE										=   x.encode()
           if len(__lowerCamelCase			) == 1 and len(__lowerCamelCase			) == 3:
               SCREAMING_SNAKE_CASE										=   (int(e[0]			) << 16) + (int(e[1]			) << 8) + int(e[2]			)
               if c >= 0xE28_080 and c <= 0xE2B_07F:
                   return True
           return False

       SCREAMING_SNAKE_CASE										=   0
       SCREAMING_SNAKE_CASE										=   []
       while pos < len(__lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   min(len(__lowerCamelCase			)     , pos + self.maxlen + 1			) if text[pos] == "<" else pos + 3
           SCREAMING_SNAKE_CASE										=   []  # (token_id, token, pos)
           for e in range(__lowerCamelCase     , __lowerCamelCase     , -1			):
               SCREAMING_SNAKE_CASE										=   text[pos:e]
               if wd in self.vocab:
                   if wd[0] == "<" and len(__lowerCamelCase			) > 2:
                       SCREAMING_SNAKE_CASE										=   [(self.vocab[wd], wd, e)]
                       break
                   else:
                       candidates.append((self.vocab[wd], wd, e)			)
           if len(__lowerCamelCase			) > 0:
               # the smallest token_id is adopted
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   sorted(__lowerCamelCase     , key=lambda __lowerCamelCase			: x[0]			)[0]
               result.append(__lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   e
           else:
               SCREAMING_SNAKE_CASE										=   pos + 1
               SCREAMING_SNAKE_CASE										=   text[pos:end]
               if check_simbol(__lowerCamelCase			):
                   result.append("<KIGOU>"			)
               elif checkuae(__lowerCamelCase			):
                   result.append("<U2000U2BFF>"			)
               else:
                   for i in wd.encode("utf-8"			):
                       result.append("<|byte%d|>" % i			)
               SCREAMING_SNAKE_CASE										=   end
       return result





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Optional[Any]="\n"			):
       SCREAMING_SNAKE_CASE										=   []
       SCREAMING_SNAKE_CASE										=   []
       SCREAMING_SNAKE_CASE										=   self.ids_to_tokens[index][0]
       if word[:6] == "<|byte" and word[-2:] == "|>":
           byte_tokens.append(int(word[6:-2]			)			)
       else:
           if len(__lowerCamelCase			) > 0:
               words.append(bytearray(__lowerCamelCase			).decode("utf-8"     , errors="replace"			)			)
               SCREAMING_SNAKE_CASE										=   []
           if word[:7] == "<|emoji" and word[-2:] == "|>":
               words.append(self.emoji["emoji_inv"][word]			)
           elif word == "<SP>":
               words.append(" "			)
           elif word == "<BR>":
               words.append(__lowerCamelCase			)
           elif word == "<TAB>":
               words.append("\t"			)
           elif word == "<BLOCK>":
               words.append("▀"			)
           elif word == "<KIGOU>":
               words.append("ǀ"			)
           elif word == "<U2000U2BFF>":
               words.append("‖"			)
           else:
               words.append(__lowerCamelCase			)
       if len(__lowerCamelCase			) > 0:
           words.append(bytearray(__lowerCamelCase			).decode("utf-8"     , errors="replace"			)			)
       SCREAMING_SNAKE_CASE										=   "".join(__lowerCamelCase			)
       return text


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			:					str						=			logging.get_logger(__name__)

__A			:					Optional[Any]						=			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__A			:					Tuple						=			{
    '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			:					Optional[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'
        ),
    },
}
__A			:					str						=			{
    '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			:					Optional[Any]						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
__A			:					List[str]						=			{
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
__A			:					Any						=			{
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


__A			:					str						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__A			:					Any						=			{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__A			:					Dict						=			{
    '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'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




__A			:					Optional[int]						=			collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

__A			:					List[Any]						=			collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__A			:					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(__snake_case		)
class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __call__(       self					:			int     , __lowerCamelCase					:			Dict     , __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     , ):
       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:
           SCREAMING_SNAKE_CASE										=   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     , )
       SCREAMING_SNAKE_CASE										=   titles if not isinstance(__lowerCamelCase     , __lowerCamelCase			) else [titles]
       SCREAMING_SNAKE_CASE										=   texts if not isinstance(__lowerCamelCase     , __lowerCamelCase			) else [texts]
       SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   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."			)
       SCREAMING_SNAKE_CASE										=   super().__call__(__lowerCamelCase     , __lowerCamelCase     , padding=__lowerCamelCase     , truncation=__lowerCamelCase			)["input_ids"]
       SCREAMING_SNAKE_CASE										=   super().__call__(__lowerCamelCase     , add_special_tokens=__lowerCamelCase     , padding=__lowerCamelCase     , truncation=__lowerCamelCase			)["input_ids"]
       SCREAMING_SNAKE_CASE										=   {
           "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:
           SCREAMING_SNAKE_CASE										=   []
           for input_ids in encoded_inputs["input_ids"]:
               attention_mask.append([int(input_id != self.pad_token_id			) for input_id in input_ids]			)
           SCREAMING_SNAKE_CASE										=   attention_mask
       return self.pad(__lowerCamelCase     , padding=__lowerCamelCase     , max_length=__lowerCamelCase     , return_tensors=__lowerCamelCase			)





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

           SCREAMING_SNAKE_CASE										=   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					:			Optional[int]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , ):
       SCREAMING_SNAKE_CASE										=   []
       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)			)
       SCREAMING_SNAKE_CASE										=   sorted(__lowerCamelCase     , key=lambda __lowerCamelCase			: x[1]     , reverse=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   []
       for (start_index, end_index), score in scores:
           if start_index > end_index:
               raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]"			)
           SCREAMING_SNAKE_CASE										=   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(__snake_case		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      READER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      READER_PRETRAINED_INIT_CONFIGURATION
   lowerCamelCase__				=      ["input_ids", "attention_mask"]