
"""simple docstring"""

import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
   from test_module.custom_tokenization_fast import CustomTokenizerFast
class   _UpperCAmelCase				(		unittest.TestCase				):




      def      a_   (				self				)    ->       Tuple:
          # A mock response for an HTTP head request to emulate server down
          UpperCAmelCase          =       mock.Mock()
          UpperCAmelCase          =       5_0_0
          UpperCAmelCase          =       {}
          UpperCAmelCase          =       HTTPError
          UpperCAmelCase          =       {}

          # Download this model to make sure it's in the cache.
          UpperCAmelCase          =       BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'				)

          # Under the mock environment we get a 500 error when trying to reach the tokenizer.
          with mock.patch('requests.Session.request' ,							return_value=lowercase_				) as mock_head:
              UpperCAmelCase          =       BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'				)
              # This check we did call the fake head request
              mock_head.assert_called()




      @require_tokenizers
      def      a_   (				self				)    ->       Optional[Any]:
          # A mock response for an HTTP head request to emulate server down
          UpperCAmelCase          =       mock.Mock()
          UpperCAmelCase          =       5_0_0
          UpperCAmelCase          =       {}
          UpperCAmelCase          =       HTTPError
          UpperCAmelCase          =       {}

          # Download this model to make sure it's in the cache.
          UpperCAmelCase          =       GPTaTokenizerFast.from_pretrained('gpt2'				)

          # Under the mock environment we get a 500 error when trying to reach the tokenizer.
          with mock.patch('requests.Session.request' ,							return_value=lowercase_				) as mock_head:
              UpperCAmelCase          =       GPTaTokenizerFast.from_pretrained('gpt2'				)
              # This check we did call the fake head request
              mock_head.assert_called()




      def      a_   (				self				)    ->       Any:
          # This test is for deprecated behavior and can be removed in v5
          try:
              UpperCAmelCase          =       tempfile.mktemp()
              with open(lowercase_ ,							'wb'				) as f:
                  http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' ,							lowercase_				)

              UpperCAmelCase          =       AlbertTokenizer.from_pretrained(lowercase_				)
          finally:
              os.remove(lowercase_				)

          # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
          # the current folder and have the right name.
          if os.path.isfile('tokenizer.json'				):
              # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
              return
          try:
              with open('tokenizer.json' ,							'wb'				) as f:
                  http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json' ,							lowercase_				)
              UpperCAmelCase          =       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
              # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
              self.assertEqual(tokenizer.vocab_size ,							1_0_0_0				)
              # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

          finally:
              os.remove('tokenizer.json'				)




      def      a_   (				self				)    ->       Dict:
          # This test is for deprecated behavior and can be removed in v5
          UpperCAmelCase          =       AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'				)

@is_staging_test
class   _UpperCAmelCase				(		unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				str				   =						["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]




      @classmethod
      def      a_   (				cls				)    ->       Dict:
          UpperCAmelCase          =       TOKEN
          HfFolder.save_token(lowercase_				)




      @classmethod
      def      a_   (				cls				)    ->       Tuple:
          try:
              delete_repo(token=cls._token ,							repo_id='test-tokenizer'				)
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token ,							repo_id='valid_org/test-tokenizer-org'				)
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token ,							repo_id='test-dynamic-tokenizer'				)
          except HTTPError:
              pass




      def      a_   (				self				)    ->       str:
          with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCAmelCase          =       os.path.join(lowercase_ ,							'vocab.txt'				)
              with open(lowercase_ ,							'w' ,							encoding='utf-8'				) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]				)				)
              UpperCAmelCase          =       BertTokenizer(lowercase_				)

          tokenizer.push_to_hub('test-tokenizer' ,							use_auth_token=self._token				)
          UpperCAmelCase          =       BertTokenizer.from_pretrained(F"{USER}/test-tokenizer"				)
          self.assertDictEqual(new_tokenizer.vocab ,							tokenizer.vocab				)

          # Reset repo
          delete_repo(token=self._token ,							repo_id='test-tokenizer'				)

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(lowercase_ ,							repo_id='test-tokenizer' ,							push_to_hub=lowercase_ ,							use_auth_token=self._token				)

          UpperCAmelCase          =       BertTokenizer.from_pretrained(F"{USER}/test-tokenizer"				)
          self.assertDictEqual(new_tokenizer.vocab ,							tokenizer.vocab				)




      def      a_   (				self				)    ->       Optional[int]:
          with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCAmelCase          =       os.path.join(lowercase_ ,							'vocab.txt'				)
              with open(lowercase_ ,							'w' ,							encoding='utf-8'				) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]				)				)
              UpperCAmelCase          =       BertTokenizer(lowercase_				)

          tokenizer.push_to_hub('valid_org/test-tokenizer-org' ,							use_auth_token=self._token				)
          UpperCAmelCase          =       BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'				)
          self.assertDictEqual(new_tokenizer.vocab ,							tokenizer.vocab				)

          # Reset repo
          delete_repo(token=self._token ,							repo_id='valid_org/test-tokenizer-org'				)

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(
                  lowercase_ ,							repo_id='valid_org/test-tokenizer-org' ,							push_to_hub=lowercase_ ,							use_auth_token=self._token				)

          UpperCAmelCase          =       BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'				)
          self.assertDictEqual(new_tokenizer.vocab ,							tokenizer.vocab				)




      @require_tokenizers
      def      a_   (				self				)    ->       Dict:
          CustomTokenizer.register_for_auto_class()
          with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCAmelCase          =       os.path.join(lowercase_ ,							'vocab.txt'				)
              with open(lowercase_ ,							'w' ,							encoding='utf-8'				) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]				)				)
              UpperCAmelCase          =       CustomTokenizer(lowercase_				)

          # No fast custom tokenizer
          tokenizer.push_to_hub('test-dynamic-tokenizer' ,							use_auth_token=self._token				)

          UpperCAmelCase          =       AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer" ,							trust_remote_code=lowercase_				)
          # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
          self.assertEqual(tokenizer.__class__.__name__ ,							'CustomTokenizer'				)

          # Fast and slow custom tokenizer
          CustomTokenizerFast.register_for_auto_class()
          with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCAmelCase          =       os.path.join(lowercase_ ,							'vocab.txt'				)
              with open(lowercase_ ,							'w' ,							encoding='utf-8'				) as vocab_writer:
                  vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]				)				)

              UpperCAmelCase          =       BertTokenizerFast.from_pretrained(lowercase_				)
              bert_tokenizer.save_pretrained(lowercase_				)
              UpperCAmelCase          =       CustomTokenizerFast.from_pretrained(lowercase_				)

          tokenizer.push_to_hub('test-dynamic-tokenizer' ,							use_auth_token=self._token				)

          UpperCAmelCase          =       AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer" ,							trust_remote_code=lowercase_				)
          # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
          self.assertEqual(tokenizer.__class__.__name__ ,							'CustomTokenizerFast'				)
          UpperCAmelCase          =       AutoTokenizer.from_pretrained(
              F"{USER}/test-dynamic-tokenizer" ,							use_fast=lowercase_ ,							trust_remote_code=lowercase_				)
          # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
          self.assertEqual(tokenizer.__class__.__name__ ,							'CustomTokenizer'				)

class   _UpperCAmelCase				(		unittest.TestCase				):




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       Trie()
          trie.add('Hello 友達'				)
          self.assertEqual(trie.data ,							{'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}}				)
          trie.add('Hello'				)
          trie.data
          self.assertEqual(trie.data ,							{'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}}				)




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       Trie()
          self.assertEqual(trie.split('[CLS] This is a extra_id_100'				) ,							['[CLS] This is a extra_id_100']				)
          trie.add('[CLS]'				)
          trie.add('extra_id_1'				)
          trie.add('extra_id_100'				)
          self.assertEqual(trie.split('[CLS] This is a extra_id_100'				) ,							['[CLS]', ' This is a ', 'extra_id_100']				)




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       Trie()
          trie.add('A'				)
          self.assertEqual(trie.split('ABC'				) ,							['A', 'BC']				)
          self.assertEqual(trie.split('BCA'				) ,							['BC', 'A']				)




      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       Trie()
          trie.add('TOKEN]'				)
          trie.add('[SPECIAL_TOKEN]'				)
          self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'				) ,							['This is something ', '[SPECIAL_TOKEN]']				)




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       Trie()
          trie.add('A'				)
          trie.add('P'				)
          trie.add('[SPECIAL_TOKEN]'				)
          self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'				) ,							['This is something ', '[SPECIAL_TOKEN]']				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       Trie()
          trie.add('AB'				)
          trie.add('B'				)
          trie.add('C'				)
          self.assertEqual(trie.split('ABC'				) ,							['AB', 'C']				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       Trie()
          trie.add('ABC'				)
          trie.add('B'				)
          trie.add('CD'				)
          self.assertEqual(trie.split('ABCD'				) ,							['ABC', 'D']				)




      def      a_   (				self				)    ->       Union[str, Any]:
          # Even if the offsets are wrong, we necessarily output correct string
          # parts.
          UpperCAmelCase          =       Trie()
          UpperCAmelCase          =       trie.cut_text('ABC' ,							[0, 0, 2, 1, 2, 3]				)
          self.assertEqual(lowercase_ ,							['AB', 'C']				)





"""simple docstring"""

from __future__ import annotations

from collections.abc import Iterator
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       value
          UpperCAmelCase          =       None
          UpperCAmelCase          =       None

class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       tree




      def      a_   (				self ,							lowercase_				)    ->       int:
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left				) + self.depth_first_search(node.right				)
          )




      def __iter__(				self				)    ->       Iterator[int]:
          yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



