





import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[Any]:  # picklable for multiprocessing
       return x.sum()




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[Any]:  # picklable for multiprocessing
       return i + 1



@dataclass
class 							_UpperCamelCase					:
       _UpperCamelCase				:					int
       _UpperCamelCase				:					str



class 							_UpperCamelCase					(	lowerCAmelCase_     ):

       def lowercase (		self: List[str]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  {}
              UpperCamelCase_          =  []
              UpperCamelCase_          =  1
              UpperCamelCase_          =  [1, 2]
              UpperCamelCase_          =  {"a": 1, "b": 2}
              UpperCamelCase_          =  {"a": [1, 2], "b": [3, 4]}
              UpperCamelCase_          =  {"a": {"1": 1}, "b": 2}
              UpperCamelCase_          =  {"a": 1, "b": 2, "c": 3, "d": 4}
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  []
              UpperCamelCase_          =  2
              UpperCamelCase_          =  [2, 3]
              UpperCamelCase_          =  {"a": 2, "b": 3}
              UpperCamelCase_          =  {"a": [2, 3], "b": [4, 5]}
              UpperCamelCase_          =  {"a": {"1": 2}, "b": 3}
              UpperCamelCase_          =  {"a": 2, "b": 3, "c": 4, "d": 5}
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  2
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   num_proc=_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   num_proc=_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   num_proc=_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   num_proc=_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   num_proc=_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   num_proc=_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   num_proc=_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   num_proc=_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  {"a": np.eye(2		), "b": np.zeros(3		), "c": np.ones(2		)}
              UpperCamelCase_          =  {"a": 2, "b": 0, "c": 2}
              UpperCamelCase_          =  {
                  "a": np.eye(2		).astype(_SCREAMING_SNAKE_CASE		),
                  "b": np.zeros(3		).astype(_SCREAMING_SNAKE_CASE		),
                  "c": np.ones(2		).astype(_SCREAMING_SNAKE_CASE		),
              }
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   map_numpy=_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(
                  {k: v.tolist() for k, v in map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   map_numpy=_SCREAMING_SNAKE_CASE		).items()}					,   {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}					,   )
              self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   map_numpy=_SCREAMING_SNAKE_CASE					,   num_proc=_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(
                  {k: v.tolist() for k, v in map_nested(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   map_numpy=_SCREAMING_SNAKE_CASE					,   num_proc=_SCREAMING_SNAKE_CASE		).items()}					,   {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}					,   )
              with self.assertRaises(_SCREAMING_SNAKE_CASE		):  # can't pickle a local lambda
                     map_nested(lambda _SCREAMING_SNAKE_CASE		: x + 1					,   _SCREAMING_SNAKE_CASE					,   num_proc=_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Tuple		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  {"a": 1, "b": 2}
              UpperCamelCase_          =  {"a": 3, "b": 4}
              UpperCamelCase_          =  {"a": 5, "b": 6}
              UpperCamelCase_          =  sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]		)
              self.assertEqual(sorted(zip_dict(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)		)					,   _SCREAMING_SNAKE_CASE		)



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




              """simple docstring"""


              class 							_UpperCamelCase					:
                     _UpperCamelCase				:					str         =   '''bar'''

              UpperCamelCase_          =  Foo()
              self.assertEqual(foo.my_attr					,   "bar"		)
              with temporary_assignment(_SCREAMING_SNAKE_CASE					,   "my_attr"					,   "BAR"		):
                     self.assertEqual(foo.my_attr					,   "BAR"		)
              self.assertEqual(foo.my_attr					,   "bar"		)




@pytest.mark.parametrize(
    "iterable_length, num_proc, expected_num_proc"					,		[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]					,		)
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Union[str, Any]:
       with patch("datasets.utils.py_utils._single_map_nested"  ) as mock_single_map_nested, patch(
           "datasets.parallel.parallel.Pool"  ) as mock_multiprocessing_pool:
              UpperCamelCase_          =  {F'''{i}''': i for i in range(UpperCamelCase_  )}
              UpperCamelCase_          =  map_nested(lambda UpperCamelCase_  : x + 10					,		UpperCamelCase_					,		num_proc=UpperCamelCase_					,		parallel_min_length=16  )
              if expected_num_proc == 1:
                     assert mock_single_map_nested.called
                     assert not mock_multiprocessing_pool.called
              else:
                     assert not mock_single_map_nested.called
                     assert mock_multiprocessing_pool.called
                     assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc



class 							_UpperCamelCase					(	lowerCAmelCase_     ):

       @require_tf
       def lowercase (		self: Optional[Any]		)       -> Optional[int]:




              """simple docstring"""


              import tensorflow as tf
              from tensorflow.keras import layers

              UpperCamelCase_          =  layers.Dense(2		)

              def gen_random_output():
                     UpperCamelCase_          =  tf.random.uniform((1, 3)		)
                     return model(_SCREAMING_SNAKE_CASE		).numpy()

              with temp_seed(42					,   set_tensorflow=_SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  gen_random_output()
              with temp_seed(42					,   set_tensorflow=_SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  gen_random_output()
              UpperCamelCase_          =  gen_random_output()

              np.testing.assert_equal(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              self.assertGreater(np.abs(outa - outa		).sum()					,   0		)

       @require_torch
       def lowercase (		self: Optional[int]		)       -> Optional[int]:




              """simple docstring"""


              import torch

              def gen_random_output():
                     UpperCamelCase_          =  torch.nn.Linear(3					,   2		)
                     UpperCamelCase_          =  torch.rand(1					,   3		)
                     return model(_SCREAMING_SNAKE_CASE		).detach().numpy()

              with temp_seed(42					,   set_pytorch=_SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  gen_random_output()
              with temp_seed(42					,   set_pytorch=_SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  gen_random_output()
              UpperCamelCase_          =  gen_random_output()

              np.testing.assert_equal(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              self.assertGreater(np.abs(outa - outa		).sum()					,   0		)



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




              """simple docstring"""


              def gen_random_output():
                     return np.random.rand(1					,   3		)

              with temp_seed(42		):
                     UpperCamelCase_          =  gen_random_output()
              with temp_seed(42		):
                     UpperCamelCase_          =  gen_random_output()
              UpperCamelCase_          =  gen_random_output()

              np.testing.assert_equal(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              self.assertGreater(np.abs(outa - outa		).sum()					,   0		)




@pytest.mark.parametrize("input_data"					,		[{}]  )
def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       UpperCamelCase_          =  NestedDataStructure(UpperCamelCase_  ).data
       assert output_data == input_data




@pytest.mark.parametrize(
    "data, expected_output"					,		[
        ({}, []),
        ([], []),
        ("foo", ["foo"]),
        (["foo", "bar"], ["foo", "bar"]),
        ([["foo", "bar"]], ["foo", "bar"]),
        ([[["foo"], ["bar"]]], ["foo", "bar"]),
        ([[["foo"], "bar"]], ["foo", "bar"]),
        ({"a": 1, "b": 2}, [1, 2]),
        ({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
        ({"a": {"1": 1}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": [2]}, [1, 2]),
    ]					,		)
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       UpperCamelCase_          =  NestedDataStructure(UpperCamelCase_  ).flatten()
       assert output == expected_output




def  lowerCAmelCase_ (							)     ->		Tuple:
       UpperCamelCase_          =  A(x=1					,		y="foobar"  )
       UpperCamelCase_          =  {"x": 1, "y": "foobar"}
       assert asdict(UpperCamelCase_  ) == expected_output

       UpperCamelCase_          =  {"a": {"b": A(x=10					,		y="foo"  )}, "c": [A(x=20					,		y="bar"  )]}
       UpperCamelCase_          =  {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
       assert asdict(UpperCamelCase_  ) == expected_output

       with pytest.raises(UpperCamelCase_  ):
              asdict([1, A(x=10					,		y="foo"  )]  )




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       return text.split()




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[str]:
       yield (time.time(), content)
       time.sleep(2  )
       yield (time.time(), content)







def  lowerCAmelCase_ (							)     ->		int:
       with Pool(2  ) as pool:
              UpperCamelCase_          =  list(iflatmap_unordered(UpperCamelCase_					,		_split_text					,		kwargs_iterable=[{"text": "hello there"}] * 10  )  )
              assert out.count("hello"  ) == 10
              assert out.count("there"  ) == 10
              assert len(UpperCamelCase_  ) == 20

       # check multiprocess from pathos (uses dill for pickling)
       with multiprocess.Pool(2  ) as pool:
              UpperCamelCase_          =  list(iflatmap_unordered(UpperCamelCase_					,		_split_text					,		kwargs_iterable=[{"text": "hello there"}] * 10  )  )
              assert out.count("hello"  ) == 10
              assert out.count("there"  ) == 10
              assert len(UpperCamelCase_  ) == 20

       # check that we get items as fast as possible
       with Pool(2  ) as pool:
              UpperCamelCase_          =  []
              for yield_time, content in iflatmap_unordered(
                  UpperCamelCase_					,		_aseconds_generator_of_aitems_with_timing					,		kwargs_iterable=[{"content": "a"}, {"content": "b"}]  ):
                     assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
                     out.append(UpperCamelCase_  )
              assert out.count("a"  ) == 2
              assert out.count("b"  ) == 2
              assert len(UpperCamelCase_  ) == 4








import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{'vocab_file': 'spiece.model'}

_UpperCAmelCase        =				{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

_UpperCAmelCase        =				{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
_UpperCAmelCase        =				0
_UpperCAmelCase        =				1
_UpperCAmelCase        =				2
_UpperCAmelCase        =				3
_UpperCAmelCase        =				4



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					List[str]         =   VOCAB_FILES_NAMES
       _UpperCamelCase				:					int         =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase				:					Any         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase				:					Dict         =   '''left'''

       def __init__(		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: Optional[int]=False					,   _SCREAMING_SNAKE_CASE: List[Any]=True					,   _SCREAMING_SNAKE_CASE: List[str]=False					,   _SCREAMING_SNAKE_CASE: Any="<s>"					,   _SCREAMING_SNAKE_CASE: Dict="</s>"					,   _SCREAMING_SNAKE_CASE: List[str]="<unk>"					,   _SCREAMING_SNAKE_CASE: Tuple="<sep>"					,   _SCREAMING_SNAKE_CASE: Optional[int]="<pad>"					,   _SCREAMING_SNAKE_CASE: int="<cls>"					,   _SCREAMING_SNAKE_CASE: Optional[Any]="<mask>"					,   _SCREAMING_SNAKE_CASE: List[str]=["<eop>", "<eod>"]					,   _SCREAMING_SNAKE_CASE: Optional[Dict[str, Any]] = None					,   **_SCREAMING_SNAKE_CASE: Optional[Any]					,   )       -> None:




              """simple docstring"""


              UpperCamelCase_          =  AddedToken(_SCREAMING_SNAKE_CASE					,   lstrip=_SCREAMING_SNAKE_CASE					,   rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		) else mask_token

              UpperCamelCase_          =  {} if sp_model_kwargs is None else sp_model_kwargs

              super().__init__(
                  do_lower_case=_SCREAMING_SNAKE_CASE					,   remove_space=_SCREAMING_SNAKE_CASE					,   keep_accents=_SCREAMING_SNAKE_CASE					,   bos_token=_SCREAMING_SNAKE_CASE					,   eos_token=_SCREAMING_SNAKE_CASE					,   unk_token=_SCREAMING_SNAKE_CASE					,   sep_token=_SCREAMING_SNAKE_CASE					,   pad_token=_SCREAMING_SNAKE_CASE					,   cls_token=_SCREAMING_SNAKE_CASE					,   mask_token=_SCREAMING_SNAKE_CASE					,   additional_special_tokens=_SCREAMING_SNAKE_CASE					,   sp_model_kwargs=self.sp_model_kwargs					,   **_SCREAMING_SNAKE_CASE					,   )

              UpperCamelCase_          =  3

              UpperCamelCase_          =  do_lower_case
              UpperCamelCase_          =  remove_space
              UpperCamelCase_          =  keep_accents
              UpperCamelCase_          =  vocab_file

              UpperCamelCase_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
              self.sp_model.Load(_SCREAMING_SNAKE_CASE		)

       @property
       def lowercase (		self: List[Any]		)       -> Dict:




              """simple docstring"""


              return len(self.sp_model		)

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




              """simple docstring"""


              UpperCamelCase_          =  {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
              vocab.update(self.added_tokens_encoder		)
              return vocab

       def __getstate__(		self: int		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  self.__dict__.copy()
              UpperCamelCase_          =  None
              return state

       def __setstate__(		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  d

              # for backward compatibility
              if not hasattr(self					,   "sp_model_kwargs"		):
                     UpperCamelCase_          =  {}

              UpperCamelCase_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
              self.sp_model.Load(self.vocab_file		)

       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> int:




              """simple docstring"""


              if self.remove_space:
                     UpperCamelCase_          =  " ".join(inputs.strip().split()		)
              else:
                     UpperCamelCase_          =  inputs
              UpperCamelCase_          =  outputs.replace("``"					,   "\""		).replace("''"					,   "\""		)

              if not self.keep_accents:
                     UpperCamelCase_          =  unicodedata.normalize("NFKD"					,   _SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  "".join([c for c in outputs if not unicodedata.combining(_SCREAMING_SNAKE_CASE		)]		)
              if self.do_lower_case:
                     UpperCamelCase_          =  outputs.lower()

              return outputs

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: str		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  self.preprocess_text(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  self.sp_model.encode(_SCREAMING_SNAKE_CASE					,   out_type=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  []
              for piece in pieces:
                     if len(_SCREAMING_SNAKE_CASE		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
                            UpperCamelCase_          =  self.sp_model.EncodeAsPieces(piece[:-1].replace(_SCREAMING_SNAKE_CASE					,   ""		)		)
                            if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                   if len(cur_pieces[0]		) == 1:
                                          UpperCamelCase_          =  cur_pieces[1:]
                                   else:
                                          UpperCamelCase_          =  cur_pieces[0][1:]
                            cur_pieces.append(piece[-1]		)
                            new_pieces.extend(_SCREAMING_SNAKE_CASE		)
                     else:
                            new_pieces.append(_SCREAMING_SNAKE_CASE		)

              return new_pieces

       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: str		)       -> Any:




              """simple docstring"""


              return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		)       -> str:




              """simple docstring"""


              return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  "".join(_SCREAMING_SNAKE_CASE		).replace(_SCREAMING_SNAKE_CASE					,   " "		).strip()
              return out_string

       def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: bool = False					,   _SCREAMING_SNAKE_CASE: bool = None					,   _SCREAMING_SNAKE_CASE: bool = True					,   **_SCREAMING_SNAKE_CASE: Dict					,   )       -> str:




              """simple docstring"""


              UpperCamelCase_          =  kwargs.pop("use_source_tokenizer"					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE					,   skip_special_tokens=_SCREAMING_SNAKE_CASE		)

              # To avoid mixing byte-level and unicode for byte-level BPT
              # we need to build string separately for added tokens and byte-level tokens
              # cf. https://github.com/huggingface/transformers/issues/1133
              UpperCamelCase_          =  []
              UpperCamelCase_          =  []
              for token in filtered_tokens:
                     if skip_special_tokens and token in self.all_special_ids:
                            continue
                     if token in self.added_tokens_encoder:
                            if current_sub_text:
                                   sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)		)
                                   UpperCamelCase_          =  []
                            sub_texts.append(_SCREAMING_SNAKE_CASE		)
                     else:
                            current_sub_text.append(_SCREAMING_SNAKE_CASE		)
              if current_sub_text:
                     sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)		)

              # Mimic the behavior of the Rust tokenizer:
              # By default, there are no spaces between special tokens
              UpperCamelCase_          =  "".join(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  (
                  clean_up_tokenization_spaces
                  if clean_up_tokenization_spaces is not None
                  else self.clean_up_tokenization_spaces
              )
              if clean_up_tokenization_spaces:
                     UpperCamelCase_          =  self.clean_up_tokenization(_SCREAMING_SNAKE_CASE		)
                     return clean_text
              else:
                     return text

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              UpperCamelCase_          =  [self.sep_token_id]
              UpperCamelCase_          =  [self.cls_token_id]
              if token_ids_a is None:
                     return token_ids_a + sep + cls
              return token_ids_a + sep + token_ids_a + sep + cls

       def lowercase (		self: Dict					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None					,   _SCREAMING_SNAKE_CASE: bool = False		)       -> List[int]:




              """simple docstring"""


              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=_SCREAMING_SNAKE_CASE					,   token_ids_a=_SCREAMING_SNAKE_CASE					,   already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

              if token_ids_a is not None:
                     return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1, 1]
              return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1, 1]

       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              UpperCamelCase_          =  [self.sep_token_id]
              UpperCamelCase_          =  [2]

              if token_ids_a is None:
                     return len(token_ids_a + sep		) * [0] + cls_segment_id
              return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id



       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Optional[str] = None		)       -> Tuple[str]:




              """simple docstring"""


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

              if os.path.abspath(self.vocab_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		) and os.path.isfile(self.vocab_file		):
                     copyfile(self.vocab_file					,   _SCREAMING_SNAKE_CASE		)
              elif not os.path.isfile(self.vocab_file		):
                     with open(_SCREAMING_SNAKE_CASE					,   "wb"		) as fi:
                            UpperCamelCase_          =  self.sp_model.serialized_model_proto()
                            fi.write(_SCREAMING_SNAKE_CASE		)

              return (out_vocab_file,)

