





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       :   Any					= logging.get_logger(__name__)

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

A       :   str					= {
    '''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       :   str					= {
    '''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       :   List[Any]					= {
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

A       :   Optional[Any]					= {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
A       :   Optional[int]					= {
    '''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       :   int					= {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
A       :   Union[str, Any]					= {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
A       :   List[str]					= {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Optional[int]							     =       VOCAB_FILES_NAMES
			__lowerCamelCase      :			Any							     =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase      :			List[Any]							     =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase      :			int							     =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Tuple							     =       VOCAB_FILES_NAMES
			__lowerCamelCase      :			List[str]							     =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase      :			Tuple							     =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase      :			List[Any]							     =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

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

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


A       :   Tuple					= R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''


@add_start_docstrings(SCREAMING_SNAKE_CASE       )
class     A     :
			'''simple docstring'''




			def __call__(     self  :       Optional[int]				,      __lowerCAmelCase  :       int				,      __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  :       List[Any]				,      )      ->      BatchEncoding:







					"""simple docstring"""






					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:
							A__     =  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				,      )
					A__     =  titles if not isinstance(__lowerCAmelCase				,      __lowerCAmelCase  ) else [titles]
					A__     =  texts if not isinstance(__lowerCAmelCase				,      __lowerCAmelCase  ) else [texts]
					A__     =  len(__lowerCAmelCase  )
					A__     =  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.'  )
					A__     =  super().__call__(__lowerCAmelCase				,      __lowerCAmelCase				,      padding=__lowerCAmelCase				,      truncation=__lowerCAmelCase  )["""input_ids"""]
					A__     =  super().__call__(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase				,      padding=__lowerCAmelCase				,      truncation=__lowerCAmelCase  )["""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(__lowerCAmelCase				,      __lowerCAmelCase  )
					    ]
					}
					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(__lowerCAmelCase				,      padding=__lowerCAmelCase				,      max_length=__lowerCAmelCase				,      return_tensors=__lowerCAmelCase  )






			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       BatchEncoding				,      __lowerCAmelCase  :       DPRReaderOutput				,      __lowerCAmelCase  :       int = 16				,      __lowerCAmelCase  :       int = 64				,      __lowerCAmelCase  :       int = 4				,      )      ->      List[DPRSpanPrediction]:







					"""simple docstring"""






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

							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=__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   a_			(     self  :       str				,      __lowerCAmelCase  :       List[int]				,      __lowerCAmelCase  :       List[int]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int				,      )      ->      List[DPRSpanPrediction]:







					"""simple docstring"""






					A__     =  []
					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)  )
					A__     =  sorted(__lowerCAmelCase				,      key=lambda __lowerCAmelCase  : x[1]				,      reverse=__lowerCAmelCase  )
					A__     =  []
					for (start_index, end_index), score in scores:
							if start_index > end_index:
									raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]'  )
							A__     =  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(SCREAMING_SNAKE_CASE       )
class     A     (SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			str							     =       VOCAB_FILES_NAMES
			__lowerCamelCase      :			Optional[Any]							     =       READER_PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase      :			Tuple							     =       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase      :			Tuple							     =       READER_PRETRAINED_INIT_CONFIGURATION
			__lowerCamelCase      :			str							     =       ['''input_ids''', '''attention_mask''']










import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
					A       :   Optional[Any]					= '''pt'''
elif is_tf_available():
					A       :   List[Any]					= '''tf'''
else:
					A       :   Union[str, Any]					= '''jax'''


class     A     (SCREAMING_SNAKE_CASE  ,	unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			str							     =       ByTaTokenizer
			__lowerCamelCase      :			Tuple							     =       False






			def   a_			(     self  :       Dict  )      ->      Dict:







					"""simple docstring"""






					super().setUp()
					A__     =  ByTaTokenizer()
					tokenizer.save_pretrained(self.tmpdirname  )






			@cached_property
			def   a_			(     self  :       Dict  )      ->      List[Any]:







					"""simple docstring"""






					return ByTaTokenizer.from_pretrained("""google/byt5-small"""  )






			def   a_			(     self  :       Union[str, Any]				,      **__lowerCAmelCase  :       int  )      ->      ByTaTokenizer:







					"""simple docstring"""






					return self.tokenizer_class.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       Optional[Any]				,      __lowerCAmelCase  :       Dict				,      __lowerCAmelCase  :       Union[str, Any]=False				,      __lowerCAmelCase  :       Dict=20				,      __lowerCAmelCase  :       List[str]=5  )      ->      Tuple[str, list]:







					"""simple docstring"""






					A__     =  []
					for i in range(len(__lowerCAmelCase  )  ):
							try:
									A__     =  tokenizer.decode([i]				,      clean_up_tokenization_spaces=__lowerCAmelCase  )
							except UnicodeDecodeError:
									pass
							toks.append((i, tok)  )

					A__     =  list(filter(lambda __lowerCAmelCase  : re.match(R"""^[ a-zA-Z]+$"""				,      t[1]  )				,      __lowerCAmelCase  )  )
					A__     =  list(filter(lambda __lowerCAmelCase  : [t[0]] == tokenizer.encode(t[1]				,      add_special_tokens=__lowerCAmelCase  )				,      __lowerCAmelCase  )  )
					if max_length is not None and len(__lowerCAmelCase  ) > max_length:
							A__     =  toks[:max_length]
					if min_length is not None and len(__lowerCAmelCase  ) < min_length and len(__lowerCAmelCase  ) > 0:
							while len(__lowerCAmelCase  ) < min_length:
									A__     =  toks + toks
        # toks_str = [t[1] for t in toks]
					A__     =  [t[0] for t in toks]

					# Ensure consistency
					A__     =  tokenizer.decode(__lowerCAmelCase				,      clean_up_tokenization_spaces=__lowerCAmelCase  )
					if " " not in output_txt and len(__lowerCAmelCase  ) > 1:
							A__     =  (
							    tokenizer.decode([toks_ids[0]]				,      clean_up_tokenization_spaces=__lowerCAmelCase  )
							    + """ """
							    + tokenizer.decode(toks_ids[1:]				,      clean_up_tokenization_spaces=__lowerCAmelCase  )
							)
					if with_prefix_space:
							A__     =  """ """ + output_txt
					A__     =  tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
					return output_txt, output_ids






			def   a_			(     self  :       List[str]  )      ->      int:







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]  )
					A__     =  tokenizer(["""hi""", """I went to the gym""", """"""]  )
					self.assertListEqual(batch_with_eos_added["""input_ids"""]				,      batch_without_eos_added["""input_ids"""]  )






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







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  """Unicode €."""
					A__     =  tokenizer(__lowerCAmelCase  )
					A__     =  [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
					self.assertEqual(encoded["""input_ids"""]				,      __lowerCAmelCase  )

					# decoding
					A__     =  tokenizer.decode(__lowerCAmelCase  )
					self.assertEqual(__lowerCAmelCase				,      """Unicode €.</s>"""  )

					A__     =  tokenizer("""e è é ê ë"""  )
					A__     =  [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
					self.assertEqual(encoded["""input_ids"""]				,      __lowerCAmelCase  )
					# decoding
					A__     =  tokenizer.decode(__lowerCAmelCase  )
					self.assertEqual(__lowerCAmelCase				,      """e è é ê ë</s>"""  )

					# encode/decode, but with `encode` instead of `__call__`
					self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""  )  )				,      """e è é ê ë</s>"""  )






			def   a_			(     self  :       Dict  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					# fmt: off
					A__     =  [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
					# fmt: on
					A__     =  tokenizer(__lowerCAmelCase				,      padding=__lowerCAmelCase				,      return_tensors=__lowerCAmelCase  )
					self.assertIsInstance(__lowerCAmelCase				,      __lowerCAmelCase  )

					if FRAMEWORK != "jax":
							A__     =  list(batch.input_ids.numpy()[0]  )
					else:
							A__     =  list(batch.input_ids.tolist()[0]  )

					self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )

					self.assertEqual((2, 37)				,      batch.input_ids.shape  )
					self.assertEqual((2, 37)				,      batch.attention_mask.shape  )






			def   a_			(     self  :       Optional[Any]  )      ->      List[str]:







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					A__     =  tokenizer(__lowerCAmelCase				,      padding=__lowerCAmelCase				,      return_tensors=__lowerCAmelCase  )
					# check if input_ids are returned and no decoder_input_ids
					self.assertIn("""input_ids"""				,      __lowerCAmelCase  )
					self.assertIn("""attention_mask"""				,      __lowerCAmelCase  )
					self.assertNotIn("""decoder_input_ids"""				,      __lowerCAmelCase  )
					self.assertNotIn("""decoder_attention_mask"""				,      __lowerCAmelCase  )






			def   a_			(     self  :       int  )      ->      Any:







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  [
					    """Summary of the text.""",
					    """Another summary.""",
					]
					A__     =  tokenizer(
					    text_target=__lowerCAmelCase				,      max_length=32				,      padding="""max_length"""				,      truncation=__lowerCAmelCase				,      return_tensors=__lowerCAmelCase  )
					self.assertEqual(32				,      targets["""input_ids"""].shape[1]  )






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







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  ["""A long paragraph for summarization. </s>"""]
					A__     =  ["""Summary of the text. </s>"""]
					# fmt: off
					A__     =  [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
					A__     =  [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
					# fmt: on

					A__     =  tokenizer(__lowerCAmelCase				,      text_target=__lowerCAmelCase  )

					self.assertEqual(__lowerCAmelCase				,      batch["""input_ids"""][0]  )
					self.assertEqual(__lowerCAmelCase				,      batch["""labels"""][0]  )






			def   a_			(     self  :       str  )      ->      Dict:







					"""simple docstring"""






					A__     =  self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									self.assertNotEqual(tokenizer.model_max_length				,      42  )

        # Now let's start the test
					A__     =  self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									# Isolate this from the other tests because we save additional tokens/etc
									A__     =  tempfile.mkdtemp()

									A__     =  """ He is very happy, UNwant\u00E9d,running"""
									A__     =  tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									tokenizer.save_pretrained(__lowerCAmelCase  )

									A__     =  tokenizer.__class__.from_pretrained(__lowerCAmelCase  )
									A__     =  after_tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )

									shutil.rmtree(__lowerCAmelCase  )

					A__     =  self.get_tokenizers(model_max_length=42  )
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									# Isolate this from the other tests because we save additional tokens/etc
									A__     =  tempfile.mkdtemp()

									A__     =  """ He is very happy, UNwant\u00E9d,running"""
									tokenizer.add_tokens(["""bim""", """bambam"""]  )
									A__     =  tokenizer.additional_special_tokens
									additional_special_tokens.append("""new_additional_special_token"""  )
									tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}  )
									A__     =  tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									tokenizer.save_pretrained(__lowerCAmelCase  )

									A__     =  tokenizer.__class__.from_pretrained(__lowerCAmelCase  )
									A__     =  after_tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )
									self.assertIn("""new_additional_special_token"""				,      after_tokenizer.additional_special_tokens  )
									self.assertEqual(after_tokenizer.model_max_length				,      42  )

									A__     =  tokenizer.__class__.from_pretrained(__lowerCAmelCase				,      model_max_length=43  )
									self.assertEqual(tokenizer.model_max_length				,      43  )

									shutil.rmtree(__lowerCAmelCase  )






			def   a_			(     self  :       Optional[int]  )      ->      str:







					"""simple docstring"""






					A__     =  []
					if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

					if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

					for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer_utils.save_pretrained(__lowerCAmelCase  )

									with open(os.path.join(__lowerCAmelCase				,      """special_tokens_map.json"""  )				,      encoding="""utf-8"""  ) as json_file:
											A__     =  json.load(__lowerCAmelCase  )

									with open(os.path.join(__lowerCAmelCase				,      """tokenizer_config.json"""  )				,      encoding="""utf-8"""  ) as json_file:
											A__     =  json.load(__lowerCAmelCase  )

									A__     =  [f'<extra_id_{i}>' for i in range(1_25  )]

									A__     =  added_tokens_extra_ids + [
									    """an_additional_special_token"""
									]
									A__     =  added_tokens_extra_ids + [
									    """an_additional_special_token"""
									]

									with open(os.path.join(__lowerCAmelCase				,      """special_tokens_map.json"""  )				,      """w"""				,      encoding="""utf-8"""  ) as outfile:
											json.dump(__lowerCAmelCase				,      __lowerCAmelCase  )
									with open(os.path.join(__lowerCAmelCase				,      """tokenizer_config.json"""  )				,      """w"""				,      encoding="""utf-8"""  ) as outfile:
											json.dump(__lowerCAmelCase				,      __lowerCAmelCase  )

									# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
									# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
									# "special_tokens_map.json" files
									A__     =  tokenizer_class.from_pretrained(
									    __lowerCAmelCase				,      )
									self.assertIn(
									    """an_additional_special_token"""				,      tokenizer_without_change_in_init.additional_special_tokens  )
									# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
									self.assertEqual(
									    ["""an_additional_special_token"""]				,      tokenizer_without_change_in_init.convert_ids_to_tokens(
									        tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]  )  )				,      )

									# Now we test that we can change the value of additional_special_tokens in the from_pretrained
									A__     =  added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""				,      lstrip=__lowerCAmelCase  )]
									A__     =  tokenizer_class.from_pretrained(
									    __lowerCAmelCase				,      additional_special_tokens=__lowerCAmelCase				,      )

									self.assertIn("""a_new_additional_special_token"""				,      tokenizer.additional_special_tokens  )
									self.assertEqual(
									    ["""a_new_additional_special_token"""]				,      tokenizer.convert_ids_to_tokens(
									        tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]  )  )				,      )






			def   a_			(     self  :       Tuple  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  []
					if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

					if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

					for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer_utils.save_pretrained(__lowerCAmelCase  )

									A__     =  tokenizer_class.from_pretrained(__lowerCAmelCase  )

									self.assertTrue(tokenizer.decode([2_55]  ) == """"""  )






			def   a_			(     self  :       List[Any]  )      ->      Tuple:







					"""simple docstring"""






					pass






			def   a_			(     self  :       Optional[int]  )      ->      Any:







					"""simple docstring"""






					pass






			def   a_			(     self  :       int  )      ->      Optional[Any]:







					"""simple docstring"""






					pass






			def   a_			(     self  :       str  )      ->      Optional[int]:







					"""simple docstring"""






					pass






			def   a_			(     self  :       int  )      ->      Dict:







					"""simple docstring"""






					A__     =  self.get_tokenizers(fast=__lowerCAmelCase				,      do_lower_case=__lowerCAmelCase  )
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									A__     =  ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
									A__     =  tokenizer.convert_tokens_to_string(__lowerCAmelCase  )

									self.assertIsInstance(__lowerCAmelCase				,      __lowerCAmelCase  )







			def   a_			(     self  :       List[Any]  )      ->      int:







					"""simple docstring"""






					A__     =  self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									A__     =  [
									    """bos_token""",
									    """eos_token""",
									    """unk_token""",
									    """sep_token""",
									    """pad_token""",
									    """cls_token""",
									    """mask_token""",
									]

									A__     =  0
									A__     =  tokenizer.convert_ids_to_tokens(
									    __lowerCAmelCase				,      skip_special_tokens=__lowerCAmelCase  )

									for attr in attributes_list:
											setattr(__lowerCAmelCase				,      attr + """_id"""				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      __lowerCAmelCase  )				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      attr + """_id"""  )				,      __lowerCAmelCase  )

											setattr(__lowerCAmelCase				,      attr + """_id"""				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      __lowerCAmelCase  )				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      attr + """_id"""  )				,      __lowerCAmelCase  )

									setattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""				,      []  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens"""  )				,      []  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""  )				,      []  )

									setattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""				,      [token_id_to_test_setters]  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens"""  )				,      [token_to_test_setters]  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""  )				,      [token_id_to_test_setters]  )



