
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


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

A     :	int    = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A     :	Dict    = {
    '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     :	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     :	Tuple    = {
    '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     :	Tuple    = {
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
A     :	List[Any]    = {
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


A     :	Optional[int]    = {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A     :	str    = {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
A     :	Union[str, Any]    = {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}







class 			__A( a			):
				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 			__A( a			):
				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



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

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


A     :	Tuple    = 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:




				def __call__(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = None		,     _snake_case = False		,     _snake_case = False		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     **_snake_case		,     )    ->      BatchEncoding:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = 16		,     _snake_case = 64		,     _snake_case = 4		,     )    ->      List[DPRSpanPrediction]:


								'''simple docstring'''




								__a										=    reader_input['''input_ids''']
								__a							,     __a							,     __a										=    reader_output[:3]
								__a										=    len(_snake_case				)
								__a										=    sorted(range(_snake_case				)		,     reverse=_snake_case		,     key=relevance_logits.__getitem__				)
								__a										=    []
								for doc_id in sorted_docs:
												__a										=    list(input_ids[doc_id]				)
												# assuming question & title information is at the beginning of the sequence
												__a										=    sequence_ids.index(self.sep_token_id		,     2				) + 1  # second sep id
												if sequence_ids[-1] == self.pad_token_id:
																__a										=    sequence_ids.index(self.pad_token_id				)
												else:
																__a										=    len(_snake_case				)

												__a										=    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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     )    ->      List[DPRSpanPrediction]:


								'''simple docstring'''




								__a										=    []
								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)				)
								__a										=    sorted(_snake_case		,     key=lambda _snake_case				: x[1]		,     reverse=_snake_case				)
								__a										=    []
								for (start_index, end_index), score in scores:
												assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
												__a										=    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			):
				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
"""simple docstring"""
import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__					       =	'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		CamembertTokenizer
      a__        =		CamembertTokenizerFast
      a__        =		True
      a__        =		True



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        super().setUp()

        # We have a SentencePiece fixture for testing
        a__:     Tuple										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Optional[Any]										=	'<pad>'
        a__:     List[Any]										=	1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase)    ,			lowercase)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase)    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     str										=	list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0]    ,			'<s>NOTUSED')
        self.assertEqual(vocab_keys[1]    ,			'<pad>')
        self.assertEqual(vocab_keys[-1]    ,			'<mask>')
        self.assertEqual(len(lowercase)    ,			10_04)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        self.assertEqual(self.get_tokenizer().vocab_size    ,			10_05)



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)
        a__:     List[Any]										=	CamembertTokenizerFast.from_pretrained(self.tmpdirname)

        a__:     Dict										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[int]										=	tokenizer.encode(lowercase)
        a__:     Any										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Optional[Any]										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        # <unk> tokens are not the same for `rust` than for `slow`.
        # Because spm gives back raw token instead of `unk` in EncodeAsPieces
        # tokens = tokenizer.tokenize(sequence)
        a__:     Tuple										=	tokenizer.convert_ids_to_tokens(lowercase)
        a__:     Tuple										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        if not self.test_rust_tokenizer:
          return

        a__:     Dict										=	self.get_tokenizer()
        a__:     str										=	self.get_rust_tokenizer()

        a__:     int										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[Any]										=	tokenizer.tokenize(lowercase)
        a__:     List[Any]										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     str										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Tuple										=	self.get_rust_tokenizer()
        a__:     Union[str, Any]										=	tokenizer.encode(lowercase)
        a__:     List[Any]										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	{'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # camembert is a french model. So we also use french texts.
        a__:     int										=	[
            'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
            'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
            'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
            'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
            'telles que la traduction et la synthèse de texte.',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase    ,			model_name='camembert-base'    ,			revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'    ,			sequences=lowercase    ,			)


