





"""simple docstring"""





_lowerCamelCase  					=     {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_lowerCamelCase  					=     [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowerCamelCase  					=     [0 for x in range(len(graph))]
_lowerCamelCase  					=     []




def 		lowerCAmelCase_     (  lowercase_       :							Union[str, Any]			,				lowercase_       :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Tuple 							=	1
 while stack:
  __SCREAMING_SNAKE_CASE			:      Any 							=	stack.pop()
  print(lowercase_			,				clothes[current_clothing]		)
  order += 1




def 		lowerCAmelCase_     (  lowercase_       :							List[str]			,				lowercase_       :							List[str]			,				lowercase_       :							int		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Tuple 							=	1
 for v in graph[u]:
  if not visited[v]:
   depth_first_search(lowercase_			,				lowercase_			,				lowercase_		)

 stack.append(lowercase_		)




def 		lowerCAmelCase_     (  lowercase_       :							Union[str, Any]			,				lowercase_       :							Tuple		):

 '''simple docstring'''






 for v in range(len(lowercase_		)		):
  if not visited[v]:
   depth_first_search(lowercase_			,				lowercase_			,				lowercase_		)


if __name__ == "__main__":
    topological_sort(graph, visited)
    print(stack)
    print_stack(stack, clothes)








"""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       snake_case		(       __UpperCAmelCase			,					unittest.TestCase       ):
      lowerCamelCase__      =			PreTrainedTokenizerFast
      lowerCamelCase__      =			False
      lowerCamelCase__      =			True
      lowerCamelCase__      =			'''tokenizer_file'''

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

       __SCREAMING_SNAKE_CASE			:      int 							=	['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	'''SaulLu/dummy-tokenizer-bytelevel-bpe'''

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

       __SCREAMING_SNAKE_CASE			:      str 							=	PreTrainedTokenizerFast.from_pretrained(model_paths[0]						)
       tokenizer.save_pretrained(self.tmpdirname						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
       # model
       pass

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						):
       # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
       # model
       pass

      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						):
       # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
       # model
       pass

      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						):
       # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
       pass

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	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:
          __SCREAMING_SNAKE_CASE			:      int 							=	tempfile.mkdtemp()
          __SCREAMING_SNAKE_CASE			:      List[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						)
          __SCREAMING_SNAKE_CASE			:      Tuple 							=	tmpdirname_orig

      def 						SCREAMING_SNAKE_CASE_      (      self       :str						):
       __SCREAMING_SNAKE_CASE			:      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:
          __SCREAMING_SNAKE_CASE			:      Dict 							=	tempfile.mkdtemp()
          __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	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						)
          __SCREAMING_SNAKE_CASE			:      List[Any] 							=	tmpdirname_orig



      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						):
       __SCREAMING_SNAKE_CASE			:      str 							=	self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	('''a''' for _ in range(1_0_0_0						))
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	tokenizer.train_new_from_iterator(text_iterator=_lowerCamelCase						,							length=1_0_0_0						,							vocab_size=5_0						)

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	new_tokenizer.encode('''a🤗'''						)
       self.assertEqual(_lowerCamelCase						,							[6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]						)







@require_tokenizers
class       snake_case		(       unittest.TestCase       ):

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	AutoTokenizer.from_pretrained('''bert-base-cased'''						)
       __SCREAMING_SNAKE_CASE			:      str 							=	json.loads(tokenizer._tokenizer.to_str()						)
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	len(_lowerCamelCase						)

       with tempfile.TemporaryDirectory() as tmp_dir:
        # Hack to save this in the tokenizer_config.json
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	['''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
        __SCREAMING_SNAKE_CASE			:      Dict 							=	AutoTokenizer.from_pretrained(_lowerCamelCase						)
        self.assertEqual(len(_lowerCamelCase						)						,							len(_lowerCamelCase						) + 1						)
        __SCREAMING_SNAKE_CASE			:      Dict 							=	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'''						)						)
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	['''tokenizer.42.0.0.json''']
        tokenizer.save_pretrained(_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	AutoTokenizer.from_pretrained(_lowerCamelCase						)
        self.assertEqual(len(_lowerCamelCase						)						,							len(_lowerCamelCase						)						)
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	json.loads(new_tokenizer._tokenizer.to_str()						)
        self.assertNotIn('''huggingface'''						,							json_tokenizer['''model''']['''vocab''']						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						):
       # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	'''hf-internal-testing/test-two-tokenizers'''

       # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
       __SCREAMING_SNAKE_CASE			:      int 							=	AutoTokenizer.from_pretrained(_lowerCamelCase						)
       self.assertEqual(len(_lowerCamelCase						)						,							2_8_9_9_7						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	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

       __SCREAMING_SNAKE_CASE			:      Dict 							=	'''3.0.0'''
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	old_transformers.models.auto.AutoTokenizer.from_pretrained(_lowerCamelCase						)
       self.assertEqual(len(_lowerCamelCase						)						,							2_8_9_9_6						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	json.loads(old_tokenizer._tokenizer.to_str()						)
       self.assertNotIn('''huggingface'''						,							json_tokenizer['''model''']['''vocab''']						)







@require_tokenizers
class       snake_case		(       unittest.TestCase       ):

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       # See https://github.com/huggingface/transformers/pull/12550
       # and https://github.com/huggingface/tokenizers/issues/537
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''						)
       __SCREAMING_SNAKE_CASE			:      str 							=	'''The Matrix is a 1999 science fiction action film.'''

       with concurrent.futures.ThreadPoolExecutor() as executor:
        __SCREAMING_SNAKE_CASE			:      Dict 							=	[executor.submit(self.fetch						,							_lowerCamelCase						,							_lowerCamelCase						) for i in range(1_0						)]
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	[future.result() for future in futures]
        self.assertEqual(_lowerCamelCase						,							[[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0						)]						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :int						,							_lowerCamelCase       :Optional[int]						):
       return tokenizer.encode(_lowerCamelCase						,							truncation='''longest_first'''						,							padding='''longest'''						)

