




import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

UpperCamelCase								=					{
    'artists_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
    },
    'genres_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
    },
    'lyrics_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
    },
}

UpperCamelCase								=					{
    'jukebox': 512,
}





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				PRETRAINED_LYRIC_TOKENS_SIZES
    snake_case__ =				["input_ids", "attention_mask"]


    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Any=["v3", "v2", "v2"]       ,     SCREAMING_SNAKE_CASE__					:					Dict=512       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=5       ,     SCREAMING_SNAKE_CASE__					:					Dict="<|endoftext|>"       ,     **SCREAMING_SNAKE_CASE__					:					int       ,     )				->	List[str]:
         lowerCAmelCase__          =			AddedToken(SCREAMING_SNAKE_CASE__       ,     lstrip=SCREAMING_SNAKE_CASE__       ,     rstrip=SCREAMING_SNAKE_CASE__       ) if isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else unk_token
         super().__init__(
             unk_token=SCREAMING_SNAKE_CASE__       ,     n_genres=SCREAMING_SNAKE_CASE__       ,     version=SCREAMING_SNAKE_CASE__       ,     max_n_lyric_tokens=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			version
         lowerCAmelCase__          =			max_n_lyric_tokens
         lowerCAmelCase__          =			n_genres

         with open(SCREAMING_SNAKE_CASE__       ,     encoding="utf-8"       ) as vocab_handle:
              lowerCAmelCase__          =			json.load(SCREAMING_SNAKE_CASE__       )

         with open(SCREAMING_SNAKE_CASE__       ,     encoding="utf-8"       ) as vocab_handle:
              lowerCAmelCase__          =			json.load(SCREAMING_SNAKE_CASE__       )

         with open(SCREAMING_SNAKE_CASE__       ,     encoding="utf-8"       ) as vocab_handle:
              lowerCAmelCase__          =			json.load(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"
         # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
         if len(self.lyrics_encoder       ) == 79:
              lowerCAmelCase__          =			oov.replace(r"\-'"       ,     r"\-+'"       )

         lowerCAmelCase__          =			regex.compile(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			{v: k for k, v in self.artists_encoder.items()}
         lowerCAmelCase__          =			{v: k for k, v in self.genres_encoder.items()}
         lowerCAmelCase__          =			{v: k for k, v in self.lyrics_encoder.items()}


    @property
    def        a				(	self					:					Dict       )				->	Any:
         return len(self.artists_encoder       ) + len(self.genres_encoder       ) + len(self.lyrics_encoder       )


    def        a				(	self					:					List[Any]       )				->	Dict:
         return dict(self.artists_encoder       ,     self.genres_encoder       ,     self.lyrics_encoder       )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	List[str]:
         lowerCAmelCase__          =			[self.artists_encoder.get(SCREAMING_SNAKE_CASE__       ,     0       ) for artist in list_artists]
         for genres in range(len(SCREAMING_SNAKE_CASE__       )       ):
              lowerCAmelCase__          =			[self.genres_encoder.get(SCREAMING_SNAKE_CASE__       ,     0       ) for genre in list_genres[genres]]
              lowerCAmelCase__          =			list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]       ))

         lowerCAmelCase__          =			[[self.lyrics_encoder.get(SCREAMING_SNAKE_CASE__       ,     0       ) for character in list_lyrics[0]], [], []]
         return artists_id, list_genres, lyric_ids


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	int:
         return list(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     **SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	List[str]:
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			self.prepare_for_tokenization(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self._tokenize(SCREAMING_SNAKE_CASE__       )
         return artist, genre, lyrics


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					bool = False       )				->	Tuple[str, str, str, Dict[str, Any]]:
         for idx in range(len(self.version       )       ):
              if self.version[idx] == "v3":
                   lowerCAmelCase__          =			artists[idx].lower()
                   lowerCAmelCase__          =			[genres[idx].lower()]
              else:
                   lowerCAmelCase__          =			self._normalize(artists[idx]       ) + ".v2"
                   lowerCAmelCase__          =			[
                       self._normalize(SCREAMING_SNAKE_CASE__       ) + ".v2" for genre in genres[idx].split("_"       )
                   ]  # split is for the full dictionary with combined genres

         if self.version[0] == "v2":
              lowerCAmelCase__          =			regex.compile(r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"       )
              lowerCAmelCase__          =			"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"
              lowerCAmelCase__          =			{vocab[index]: index + 1 for index in range(len(SCREAMING_SNAKE_CASE__       )       )}
              lowerCAmelCase__          =			0
              lowerCAmelCase__          =			len(SCREAMING_SNAKE_CASE__       ) + 1
              lowerCAmelCase__          =			self.vocab
              lowerCAmelCase__          =			{v: k for k, v in self.vocab.items()}
              lowerCAmelCase__          =			""
         else:
              lowerCAmelCase__          =			regex.compile(r"[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+"       )

         lowerCAmelCase__          =			self._run_strip_accents(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			lyrics.replace("\\"       ,     "\n"       )
         lowerCAmelCase__          =			self.out_of_vocab.sub(""       ,     SCREAMING_SNAKE_CASE__       ), [], []
         return artists, genres, lyrics


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	int:
         lowerCAmelCase__          =			unicodedata.normalize("NFD"       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[]
         for char in text:
              lowerCAmelCase__          =			unicodedata.category(SCREAMING_SNAKE_CASE__       )
              if cat == "Mn":
                   continue
              output.append(SCREAMING_SNAKE_CASE__       )
         return "".join(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	str:
         lowerCAmelCase__          =			(
             [chr(SCREAMING_SNAKE_CASE__       ) for i in range(ord("a"       )       ,     ord("z"       ) + 1       )]
             + [chr(SCREAMING_SNAKE_CASE__       ) for i in range(ord("A"       )       ,     ord("Z"       ) + 1       )]
             + [chr(SCREAMING_SNAKE_CASE__       ) for i in range(ord("0"       )       ,     ord("9"       ) + 1       )]
             + ["."]
         )
         lowerCAmelCase__          =			frozenset(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			re.compile(r"_+"       )
         lowerCAmelCase__          =			"".join([c if c in accepted else "_" for c in text.lower()]       )
         lowerCAmelCase__          =			pattern.sub("_"       ,     SCREAMING_SNAKE_CASE__       ).strip("_"       )
         return text


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					List[str]       )				->	str:
         return " ".join(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, TensorType]] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = False       )				->	Any:
         # Convert to TensorType
         if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			TensorType(SCREAMING_SNAKE_CASE__       )

         # Get a function reference for the correct framework
         if tensor_type == TensorType.TENSORFLOW:
              if not is_tf_available():
                   raise ImportError(
                       "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed."       )
              import tensorflow as tf

              lowerCAmelCase__          =			tf.constant
              lowerCAmelCase__          =			tf.is_tensor
         elif tensor_type == TensorType.PYTORCH:
              if not is_torch_available():
                   raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed."       )
              import torch

              lowerCAmelCase__          =			torch.tensor
              lowerCAmelCase__          =			torch.is_tensor
         elif tensor_type == TensorType.JAX:
              if not is_flax_available():
                   raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed."       )
              import jax.numpy as jnp  # noqa: F811

              lowerCAmelCase__          =			jnp.array
              lowerCAmelCase__          =			_is_jax
         else:
              lowerCAmelCase__          =			np.asarray
              lowerCAmelCase__          =			_is_numpy

         # Do the tensor conversion in batch

         try:
              if prepend_batch_axis:
                   lowerCAmelCase__          =			[inputs]

              if not is_tensor(SCREAMING_SNAKE_CASE__       ):
                   lowerCAmelCase__          =			as_tensor(SCREAMING_SNAKE_CASE__       )
         except:  # noqa E722
              raise ValueError(
                  "Unable to create tensor, you should probably activate truncation and/or padding "
                  "with 'padding=True' 'truncation=True' to have batched tensors with the same length."       )

         return inputs


    def __call__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict=""       ,     SCREAMING_SNAKE_CASE__					:					Any="pt"       )				->	BatchEncoding:
         lowerCAmelCase__          =			[0, 0, 0]
         lowerCAmelCase__          =			[artist] * len(self.version       )
         lowerCAmelCase__          =			[genres] * len(self.version       )

         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			self.tokenize(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			self._convert_token_to_id(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			[-INFINITY] * len(full_tokens[-1]       )
         lowerCAmelCase__          =			[
             self.convert_to_tensors(
                 [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]       ,     tensor_type=SCREAMING_SNAKE_CASE__       )
             for i in range(len(self.version       )       )
         ]
         return BatchEncoding({"input_ids": input_ids, "attention_masks": attention_masks}       )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = None       )				->	Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE__       ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
              return

         lowerCAmelCase__          =			os.path.join(
             SCREAMING_SNAKE_CASE__       ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["artists_file"]       )
         with open(SCREAMING_SNAKE_CASE__       ,     "w"       ,     encoding="utf-8"       ) as f:
              f.write(json.dumps(self.artists_encoder       ,     ensure_ascii=SCREAMING_SNAKE_CASE__       )       )

         lowerCAmelCase__          =			os.path.join(
             SCREAMING_SNAKE_CASE__       ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["genres_file"]       )
         with open(SCREAMING_SNAKE_CASE__       ,     "w"       ,     encoding="utf-8"       ) as f:
              f.write(json.dumps(self.genres_encoder       ,     ensure_ascii=SCREAMING_SNAKE_CASE__       )       )

         lowerCAmelCase__          =			os.path.join(
             SCREAMING_SNAKE_CASE__       ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["lyrics_file"]       )
         with open(SCREAMING_SNAKE_CASE__       ,     "w"       ,     encoding="utf-8"       ) as f:
              f.write(json.dumps(self.lyrics_encoder       ,     ensure_ascii=SCREAMING_SNAKE_CASE__       )       )

         return (artists_file, genres_file, lyrics_file)


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Dict:
         lowerCAmelCase__          =			self.artists_decoder.get(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[self.genres_decoder.get(SCREAMING_SNAKE_CASE__       ) for genre in genres_index]
         lowerCAmelCase__          =			[self.lyrics_decoder.get(SCREAMING_SNAKE_CASE__       ) for character in lyric_index]
         return artist, genres, lyrics






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	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				DPRContextEncoderTokenizer






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				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(UpperCamelCase__   )
class 			__lowerCamelCase	:




    """simple docstring"""

    def __call__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = None       ,     SCREAMING_SNAKE_CASE__					:					Union[bool, str] = False       ,     SCREAMING_SNAKE_CASE__					:					Union[bool, str] = False       ,     SCREAMING_SNAKE_CASE__					:					Optional[int] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, TensorType]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     **SCREAMING_SNAKE_CASE__					:					List[Any]       ,     )				->	BatchEncoding:
         if titles is None and texts is None:
              return super().__call__(
                  SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       ,     return_attention_mask=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
         elif titles is None or texts is None:
              lowerCAmelCase__          =			titles if texts is None else texts
              return super().__call__(
                  SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       ,     return_attention_mask=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			titles if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [titles]
         lowerCAmelCase__          =			texts if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [texts]
         lowerCAmelCase__          =			len(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			questions if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else [questions] * n_passages
         assert len(SCREAMING_SNAKE_CASE__       ) == len(
             SCREAMING_SNAKE_CASE__       ), f'There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE__       )} titles and {len(SCREAMING_SNAKE_CASE__       )} texts.'
         lowerCAmelCase__          =			super().__call__(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       )["input_ids"]
         lowerCAmelCase__          =			super().__call__(SCREAMING_SNAKE_CASE__       ,     add_special_tokens=SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       )["input_ids"]
         lowerCAmelCase__          =			{
             "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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
             ]
         }
         if return_attention_mask is not False:
              lowerCAmelCase__          =			[]
              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__          =			attention_mask
         return self.pad(SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       )


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

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


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     )				->	List[DPRSpanPrediction]:
         lowerCAmelCase__          =			[]
         for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE__       ):
              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__          =			sorted(SCREAMING_SNAKE_CASE__       ,     key=lambda SCREAMING_SNAKE_CASE__       : x[1]       ,     reverse=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[]
         for (start_index, end_index), score in scores:
              assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
              lowerCAmelCase__          =			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(SCREAMING_SNAKE_CASE__       ) == top_spans:
                   break
         return chosen_span_intervals






@add_end_docstrings(UpperCamelCase__   )
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				READER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				READER_PRETRAINED_INIT_CONFIGURATION
    snake_case__ =				["input_ids", "attention_mask"]
    snake_case__ =				DPRReaderTokenizer
