





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


SCREAMING_SNAKE_CASE__       :			Tuple			      =						get_tests_dir("""fixtures/test_sentencepiece.model""")
SCREAMING_SNAKE_CASE__       :			Tuple			      =						get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

SCREAMING_SNAKE_CASE__       :			int			      =						"""pt""" if is_torch_available() else """tf"""







@require_sentencepiece
@require_tokenizers
class   __lowerCAmelCase			(					_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						Optional[int]     		=				CamembertTokenizer
   _UpperCamelCase :						Dict     		=				CamembertTokenizerFast
   _UpperCamelCase :						Optional[Any]     		=				True
   _UpperCamelCase :						Union[str, Any]     		=				True





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      super().setUp()

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





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  "<pad>"
      a__	:						int      		=  1

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





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  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(snake_case      )					,			1_004      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      self.assertEqual(self.get_tokenizer().vocab_size					,			1_005      )





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Any      		=  CamembertTokenizer(snake_case      )
      tokenizer.save_pretrained(self.tmpdirname      )
      a__	:						str      		=  CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

      a__	:						List[Any]      		=  "I was born in 92000, and this is falsé."

      a__	:						List[str]      		=  tokenizer.encode(snake_case      )
      a__	:						List[str]      		=  rust_tokenizer.encode(snake_case      )
      self.assertListEqual(snake_case					,			snake_case      )

      a__	:						Optional[int]      		=  tokenizer.encode(snake_case					,			add_special_tokens=snake_case      )
      a__	:						Dict      		=  rust_tokenizer.encode(snake_case					,			add_special_tokens=snake_case      )
      self.assertListEqual(snake_case					,			snake_case      )

      # <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(snake_case      )
      a__	:						Union[str, Any]      		=  rust_tokenizer.tokenize(snake_case      )
      self.assertListEqual(snake_case					,			snake_case      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      if not self.test_rust_tokenizer:
         return

      a__	:						Tuple      		=  self.get_tokenizer()
      a__	:						List[str]      		=  self.get_rust_tokenizer()

      a__	:						Tuple      		=  "I was born in 92000, and this is falsé."

      a__	:						Dict      		=  tokenizer.tokenize(snake_case      )
      a__	:						str      		=  rust_tokenizer.tokenize(snake_case      )
      self.assertListEqual(snake_case					,			snake_case      )

      a__	:						int      		=  tokenizer.encode(snake_case					,			add_special_tokens=snake_case      )
      a__	:						Any      		=  rust_tokenizer.encode(snake_case					,			add_special_tokens=snake_case      )
      self.assertListEqual(snake_case					,			snake_case      )

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





   @slow
   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						List[str]      		=  {"input_ids": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 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__	:						List[Any]      		=  [
          "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=snake_case					,			model_name="camembert-base"					,			revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"					,			sequences=snake_case					,			)








from __future__ import annotations

from random import random







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case = None      )				->  Any:
      """simple docstring"""





      a__	:						Optional[int]      		=  value
      a__	:						Tuple      		=  random()
      a__	:						Node | None      		=  None
      a__	:						Node | None      		=  None





   def __repr__( self      )				->  str:
      """simple docstring"""





      from pprint import pformat

      if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
      else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}					,			indent=1      )





   def __str__( self      )				->  str:
      """simple docstring"""





      a__	:						List[Any]      		=  str(self.value      ) + " "
      a__	:						List[str]      		=  str(self.left or ""      )
      a__	:						Tuple      		=  str(self.right or ""      )
      return value + left + right







def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if root is None:  # None tree is split into 2 Nones
      return None, None
   elif root.value is None:
      return None, None
   else:
      if value < root.value:
         a__		,     a__	:						Dict      		=  split(root.left							, lowerCamelCase   )
         return left, root
      else:
         a__		,     a__	:						int      		=  split(root.right							, lowerCamelCase   )
         return root, right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if (not left) or (not right):  # If one node is None, return the other
      return left or right
   elif left.prior < right.prior:
      a__	:						List[Any]      		=  merge(left.right							, lowerCamelCase   )
      return left
   else:
      a__	:						int      		=  merge(lowerCamelCase							, right.left   )
      return right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  Node(lowerCamelCase   )
   a__		,     a__	:						List[str]      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(merge(lowerCamelCase							, lowerCamelCase   )							, lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__		,     a__	:						Optional[int]      		=  split(lowerCamelCase							, value - 1   )
   a__		,     a__	:						Tuple      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(lowerCamelCase							, lowerCamelCase   )






def       _A  (      lowerCamelCase   ):
   if not root:  # None
      return
   else:
      inorder(root.left   )
      print(root.value							, end=","   )
      inorder(root.right   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for arg in args.split():
      if arg[0] == "+":
         a__	:						int      		=  insert(lowerCamelCase							, int(arg[1:]   )   )

      elif arg[0] == "-":
         a__	:						Union[str, Any]      		=  erase(lowerCamelCase							, int(arg[1:]   )   )

      else:
         print("Unknown command"   )

   return root






def       _A  (      ):
   a__	:						List[str]      		=  None
   print(
       "enter numbers to create a tree, + value to add value into treap, "
       "- value to erase all nodes with value. 'q' to quit. "   )

   a__	:						int      		=  input()
   while args != "q":
      a__	:						Union[str, Any]      		=  interact_treap(lowerCamelCase							, lowerCamelCase   )
      print(lowerCamelCase   )
      a__	:						Optional[Any]      		=  input()

   print("good by!"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

