

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							       =logging.get_logger(__name__)

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

_lowerCamelCase							       ={
    "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							       ={
    "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							       ={
    "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							       ={
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
_lowerCamelCase							       ={
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
_lowerCamelCase							       ={
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


_lowerCamelCase							       ={
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_lowerCamelCase							       ={
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_lowerCamelCase							       ={
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}



class 		a_       (							A_     ):



      """simple docstring"""







      __UpperCAmelCase					=      VOCAB_FILES_NAMES
      __UpperCAmelCase					=      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      __UpperCAmelCase					=      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCAmelCase					=      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      __UpperCAmelCase					=      DPRContextEncoderTokenizer







class 		a_       (							A_     ):



      """simple docstring"""







      __UpperCAmelCase					=      VOCAB_FILES_NAMES
      __UpperCAmelCase					=      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      __UpperCAmelCase					=      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCAmelCase					=      QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      __UpperCAmelCase					=      DPRQuestionEncoderTokenizer







_lowerCamelCase							       =collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

_lowerCamelCase							       =collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


_lowerCamelCase							       =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 		a_       :



      """simple docstring"""







      def __call__(      self     :							Optional[int]			,snake_case     :							Any			,snake_case     :							str = None			,snake_case     :							Dict = None			,snake_case     :							Any = False			,snake_case     :							Union[str, Any] = False			,snake_case     :							str = None			,snake_case     :							Tuple = None			,snake_case     :							Tuple = None			,**snake_case     :							List[str]			,):
           if titles is None and texts is None:
                return super().__call__(
                    snake_case			,padding=snake_case			,truncation=snake_case			,max_length=snake_case			,return_tensors=snake_case			,return_attention_mask=snake_case			,**snake_case			,)
           elif titles is None or texts is None:
                SCREAMING_SNAKE_CASE      						=titles if texts is None else texts
                return super().__call__(
                    snake_case			,snake_case			,padding=snake_case			,truncation=snake_case			,max_length=snake_case			,return_tensors=snake_case			,return_attention_mask=snake_case			,**snake_case			,)
           SCREAMING_SNAKE_CASE      						=titles if not isinstance(snake_case			,snake_case				) else [titles]
           SCREAMING_SNAKE_CASE      						=texts if not isinstance(snake_case			,snake_case				) else [texts]
           SCREAMING_SNAKE_CASE      						=len(snake_case				)
           SCREAMING_SNAKE_CASE      						=questions if not isinstance(snake_case			,snake_case				) else [questions] * n_passages
           assert len(snake_case				) == len(
               snake_case				), f'There should be as many titles than texts but got {len(snake_case				)} titles and {len(snake_case				)} texts.'
           SCREAMING_SNAKE_CASE      						=super().__call__(snake_case			,snake_case			,padding=snake_case			,truncation=snake_case				)['input_ids']
           SCREAMING_SNAKE_CASE      						=super().__call__(snake_case			,add_special_tokens=snake_case			,padding=snake_case			,truncation=snake_case				)['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(snake_case			,snake_case				)
               ]
           }
           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(snake_case			,padding=snake_case			,max_length=snake_case			,return_tensors=snake_case				)
      def        _lowerCAmelCase     (      self     :							Optional[int]			,snake_case     :							int			,snake_case     :							str			,snake_case     :							Union[str, Any] = 16			,snake_case     :							str = 64			,snake_case     :							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(snake_case				)
           SCREAMING_SNAKE_CASE      						=sorted(range(snake_case				)			,reverse=snake_case			,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(snake_case				)

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


      def        _lowerCAmelCase     (      self     :							List[Any]			,snake_case     :							Optional[Any]			,snake_case     :							str			,snake_case     :							Optional[int]			,snake_case     :							Tuple			,):
           SCREAMING_SNAKE_CASE      						=[]
           for start_index, start_score in enumerate(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)				)
           SCREAMING_SNAKE_CASE      						=sorted(snake_case			,key=lambda snake_case				: x[1]			,reverse=snake_case				)
           SCREAMING_SNAKE_CASE      						=[]
           for (start_index, end_index), score in scores:
                assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
                SCREAMING_SNAKE_CASE      						=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(snake_case				) == top_spans:
                     break
           return chosen_span_intervals







@add_end_docstrings(A_     )
class 		a_       (							A_ ,			A_     ):



      """simple docstring"""







      __UpperCAmelCase					=      VOCAB_FILES_NAMES
      __UpperCAmelCase					=      READER_PRETRAINED_VOCAB_FILES_MAP
      __UpperCAmelCase					=      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCAmelCase					=      READER_PRETRAINED_INIT_CONFIGURATION
      __UpperCAmelCase					=      ['''input_ids''', '''attention_mask''']
      __UpperCAmelCase					=      DPRReaderTokenizer





import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_ta import TaTokenizer
else:
	lowerCAmelCase           =		None


lowerCAmelCase           =		logging.get_logger(__name__)

lowerCAmelCase           =		{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase           =		{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase           =		{
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}





class       A    (   A_ ):
							UpperCamelCase_   :							Dict       =VOCAB_FILES_NAMES
							UpperCamelCase_   :							Dict       =PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_   :							List[Any]       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_   :							str       =['''input_ids''', '''attention_mask''']
							UpperCamelCase_   :							List[str]       =TaTokenizer

							UpperCamelCase_   :							List[int]       =[]


							def __init__(self      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase="</s>"      ,				lowerCAmelCase="<unk>"      ,				lowerCAmelCase="<pad>"      ,				lowerCAmelCase=1_0_0      ,				lowerCAmelCase=None      ,				**lowerCAmelCase      ,				):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
											__lowercase=			[f'<extra_id_{i}>' for i in range(lowerCAmelCase					)]
									elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra special tokens
											__lowercase=			len(set(filter(lambda lowerCAmelCase					: bool('extra_id_' in str(lowerCAmelCase					)					)      ,				lowerCAmelCase					)					)					)
											if extra_tokens != extra_ids:
													raise ValueError(
													    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
													    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
													    ' tokens'					)

									super().__init__(
									    lowerCAmelCase      ,				tokenizer_file=lowerCAmelCase      ,				eos_token=lowerCAmelCase      ,				unk_token=lowerCAmelCase      ,				pad_token=lowerCAmelCase      ,				extra_ids=lowerCAmelCase      ,				additional_special_tokens=lowerCAmelCase      ,				**lowerCAmelCase      ,				)

									__lowercase=			vocab_file
									__lowercase=			False if not self.vocab_file else True
									__lowercase=			extra_ids


							@staticmethod
							def 			_A       (lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
											__lowercase=			TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
											if init_max_model_length is not None and init_max_model_length != max_model_length:
													return init_max_model_length
											elif init_max_model_length is None:
													warnings.warn(
													    'This tokenizer was incorrectly instantiated with a model max length of'
													    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
													    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
													    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
													    f' {pretrained_model_name_or_path} automatically truncating your input to'
													    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
													    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
													    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
													    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'      ,				lowerCAmelCase      ,				)

									return max_model_length


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									if not self.can_save_slow_tokenizer:
											raise ValueError(
											    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
											    'tokenizer.'					)

									if not os.path.isdir(lowerCAmelCase					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
									__lowercase=			os.path.join(
									    lowerCAmelCase      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase					):
											copyfile(self.vocab_file      ,				lowerCAmelCase					)
											logger.info(f'Copy vocab file to {out_vocab_file}'					)

									return (out_vocab_file,)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
											return self.prefix_tokens + token_ids_a
									else:
											__lowercase=			token_ids_a + [self.eos_token_id]
											return self.prefix_tokens + token_ids_a + token_ids_a


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			[self.eos_token_id]

									if token_ids_a is None:
											return len(token_ids_a + eos					) * [0]
									return len(token_ids_a + eos + token_ids_a + eos					) * [0]


							def 			_A       (self					):
									return list(
									    set(filter(lambda lowerCAmelCase					: bool(re.search(r'<extra_id_\d+>'      ,				lowerCAmelCase					)					) is not None      ,				self.additional_special_tokens					)					)					)







							def 			_A       (self					):
									return [self.convert_tokens_to_ids(lowerCAmelCase					) for token in self.get_sentinel_tokens()]






