

'''simple docstring'''
import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      a__						(							__lowerCamelCase						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  Optional[Any]			 =							PreTrainedTokenizerFast
   _SCREAMING_SNAKE_CASE					:  int			 =							False
   _SCREAMING_SNAKE_CASE					:  List[Any]			 =							True
   _SCREAMING_SNAKE_CASE					:  Optional[int]			 =							'tokenizer_file'


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     False  # because we don't have pretrained_vocab_files_map
      super().setUp()
      _lowercase    :   Dict      =     True

      _lowercase    :   int      =     ["""robot-test/dummy-tokenizer-fast""", """robot-test/dummy-tokenizer-wordlevel"""]
      _lowercase    :   List[Any]      =     """SaulLu/dummy-tokenizer-bytelevel-bpe"""

      # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
      _lowercase    :   int      =     [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

      _lowercase    :   Union[str, Any]      =     PreTrainedTokenizerFast.from_pretrained(model_paths[0]						)
      tokenizer.save_pretrained(self.tmpdirname						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[int]      =     self.tmpdirname
      # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
            try:
               _lowercase    :   str      =     tempfile.mkdtemp()
               _lowercase    :   Any      =     self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase    ,       **_lowerCAmelCase						)

               tokenizer.save_pretrained(self.tmpdirname						)
               super().test_training_new_tokenizer()
            finally:
               # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
               # is restored
               shutil.rmtree(self.tmpdirname						)
               _lowercase    :   str      =     tmpdirname_orig


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Tuple      =     self.tmpdirname
      # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
            try:
               _lowercase    :   Optional[Any]      =     tempfile.mkdtemp()
               _lowercase    :   Any      =     self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase    ,       **_lowerCAmelCase						)

               tokenizer.save_pretrained(self.tmpdirname						)
               super().test_training_new_tokenizer_with_special_tokens_change()
            finally:
               # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
               # is restored
               shutil.rmtree(self.tmpdirname						)
               _lowercase    :   str      =     tmpdirname_orig






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Tuple      =     self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name						)

      _lowercase    :   Dict      =     ("""a""" for _ in range(1000						))
      _lowercase    :   int      =     tokenizer.train_new_from_iterator(text_iterator=_lowerCAmelCase    ,       length=1000    ,       vocab_size=50						)

      _lowercase    :   Optional[Any]      =     new_tokenizer.encode("a🤗"						)
      self.assertEqual(_lowerCAmelCase    ,       [64, 172, 253, 97, 245]						)




@require_tokenizers
class      a__						(							unittest.TestCase						):


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     AutoTokenizer.from_pretrained("bert-base-cased"						)
      _lowercase    :   List[Any]      =     json.loads(tokenizer._tokenizer.to_str()						)
      _lowercase    :   Optional[Any]      =     len(_lowerCAmelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
         # Hack to save this in the tokenizer_config.json
         _lowercase    :   Tuple      =     ["""tokenizer.4.0.0.json"""]
         tokenizer.save_pretrained(_lowerCAmelCase						)
         json.dump(_lowerCAmelCase    ,       open(os.path.join(_lowerCAmelCase    ,       "tokenizer.4.0.0.json"						)    ,       "w"						)						)

         # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
         _lowercase    :   Optional[int]      =     AutoTokenizer.from_pretrained(_lowerCAmelCase						)
         self.assertEqual(len(_lowerCAmelCase						)    ,       len(_lowerCAmelCase						) + 1						)
         _lowercase    :   Any      =     json.loads(new_tokenizer._tokenizer.to_str()						)
         self.assertIn("huggingface"    ,       json_tokenizer["model"]["vocab"]						)

         # Will need to be adjusted if we reach v42 and this test is still here.
         # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
         shutil.move(os.path.join(_lowerCAmelCase    ,       "tokenizer.4.0.0.json"						)    ,       os.path.join(_lowerCAmelCase    ,       "tokenizer.42.0.0.json"						)						)
         _lowercase    :   Union[str, Any]      =     ["""tokenizer.42.0.0.json"""]
         tokenizer.save_pretrained(_lowerCAmelCase						)
         _lowercase    :   Optional[Any]      =     AutoTokenizer.from_pretrained(_lowerCAmelCase						)
         self.assertEqual(len(_lowerCAmelCase						)    ,       len(_lowerCAmelCase						)						)
         _lowercase    :   List[Any]      =     json.loads(new_tokenizer._tokenizer.to_str()						)
         self.assertNotIn("huggingface"    ,       json_tokenizer["model"]["vocab"]						)






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     """hf-internal-testing/test-two-tokenizers"""

      # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
      _lowercase    :   int      =     AutoTokenizer.from_pretrained(_lowerCAmelCase						)
      self.assertEqual(len(_lowerCAmelCase						)    ,       28997						)
      _lowercase    :   List[Any]      =     json.loads(tokenizer._tokenizer.to_str()						)
      self.assertIn("huggingface"    ,       json_tokenizer["model"]["vocab"]						)

      # Testing an older version by monkey-patching the version in the module it's used.
      import transformers as old_transformers

      _lowercase    :   Any      =     """3.0.0"""
      _lowercase    :   Optional[int]      =     old_transformers.models.auto.AutoTokenizer.from_pretrained(_lowerCAmelCase						)
      self.assertEqual(len(_lowerCAmelCase						)    ,       28996						)
      _lowercase    :   Optional[int]      =     json.loads(old_tokenizer._tokenizer.to_str()						)
      self.assertNotIn("huggingface"    ,       json_tokenizer["model"]["vocab"]						)




@require_tokenizers
class      a__						(							unittest.TestCase						):


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   int      =     PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"						)
      _lowercase    :   List[str]      =     """The Matrix is a 1999 science fiction action film."""

      with concurrent.futures.ThreadPoolExecutor() as executor:
         _lowercase    :   List[str]      =     [executor.submit(self.fetch    ,       _lowerCAmelCase    ,       _lowerCAmelCase						) for i in range(10						)]
         _lowercase    :   Dict      =     [future.result() for future in futures]
         self.assertEqual(_lowerCAmelCase    ,       [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10						)]						)






   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      return tokenizer.encode(_lowerCAmelCase    ,       truncation="longest_first"    ,       padding="longest"						)









'''simple docstring'''
from __future__ import annotations

from typing import Generic, TypeVar

_snake_case     						=						TypeVar('T')


class      a__						(							Generic[T]						):


   def __init__(				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Any      =     data  # Assign data
      _lowercase    :   Node[T] | None      =     None  # Initialize next as null
      _lowercase    :   Node[T] | None      =     None  # Initialize prev as null




class      a__						(							Generic[T]						):


   def __init__(				self						):





      """simple docstring"""





      _lowercase    :   Node[T] | None      =     None


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """simple docstring"""





      if self.head is None:
         _lowercase    :   Optional[int]      =     Node(_UpperCamelCase						)
      else:
         _lowercase    :   str      =     Node(_UpperCamelCase						)
         _lowercase    :   Optional[Any]      =     new_node
         _lowercase    :   List[str]      =     self.head
         _lowercase    :   Tuple      =     None
         _lowercase    :   Tuple      =     new_node


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      if self.head is None:
         return None
      else:
         assert self.head is not None
         _lowercase    :   str      =     self.head.data
         _lowercase    :   Optional[Any]      =     self.head.next
         if self.head is not None:
            _lowercase    :   Optional[int]      =     None
         return temp


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.head.data if self.head is not None else None


   def __len__(				self						):





      """simple docstring"""





      _lowercase    :   Optional[Any]      =     self.head
      _lowercase    :   Tuple      =     0
      while temp is not None:
         count += 1
         _lowercase    :   Tuple      =     temp.next
      return count


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.head is None






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      print("stack elements are:"						)
      _lowercase    :   str      =     self.head
      while temp is not None:
         print(temp.data    ,       end="->"						)
         _lowercase    :   Any      =     temp.next


# Code execution starts here
if __name__ == "__main__":
    # Start with the empty stack
    _snake_case     						=						Stack()

    # Insert 4 at the beginning. So stack becomes 4->None
    print('Stack operations using Doubly LinkedList')
    stack.push(4)

    # Insert 5 at the beginning. So stack becomes 4->5->None
    stack.push(5)

    # Insert 6 at the beginning. So stack becomes 4->5->6->None
    stack.push(6)

    # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
    stack.push(7)

    # Print the stack
    stack.print_stack()

    # Print the top element
    print('\nTop element is ', stack.top())

    # Print the stack size
    print('Size of the stack is ', len(stack))

    # pop the top element
    stack.pop()

    # pop the top element
    stack.pop()

    # two elements have now been popped off
    stack.print_stack()

    # Print True if the stack is empty else False
    print('\nstack is empty:', stack.is_empty())






