'''simple docstring'''

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

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

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


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

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


lowerCAmelCase_ : Any						  =      get_tests_dir("fixtures")





class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[Any]:
         lowercase_     =      mock.Mock()
         lowercase_     =      500
         lowercase_     =      {}
         lowercase_     =      HTTPError
         lowercase_     =      {}

         # Download this model to make sure it's in the cache.
         lowercase_     =      WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
         # Under the mock environment we get a 500 error when trying to reach the model.
         with mock.patch("requests.Session.request"   ,		return_value=lowerCAmelCase_) as mock_head:
               lowercase_     =      WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
               # This check we did call the fake head request
               mock_head.assert_called()


   def __UpperCAmelCase       (   self				:	str)      ->					Optional[Any]:
         lowercase_     =      WavaVecaFeatureExtractor.from_pretrained(
             "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json")





@is_staging_test
class 							lowercase						(						unittest.TestCase ):


   @classmethod
   def __UpperCAmelCase       (   cls				:	str)      ->					Union[str, Any]:
         lowercase_     =      TOKEN
         HfFolder.save_token(lowerCAmelCase_)


   @classmethod
   def __UpperCAmelCase       (   cls				:	Any)      ->					str:







         try:
               delete_repo(token=cls._token   ,		repo_id="test-feature-extractor")
         except HTTPError:
               pass

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

         try:
               delete_repo(token=cls._token   ,		repo_id="test-dynamic-feature-extractor")
         except HTTPError:
               pass


   def __UpperCAmelCase       (   self				:	Dict)      ->					Tuple:
         lowercase_     =      WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase_)
         feature_extractor.push_to_hub("test-feature-extractor"   ,		use_auth_token=self._token)

         lowercase_     =      WavaVecaFeatureExtractor.from_pretrained(F'{USER}/test-feature-extractor')
         for k, v in feature_extractor.__dict__.items():
               self.assertEqual(lowerCAmelCase_   ,		getattr(lowerCAmelCase_   ,		lowerCAmelCase_))

         # Reset repo
         delete_repo(token=self._token   ,		repo_id="test-feature-extractor")

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
               feature_extractor.save_pretrained(
                   lowerCAmelCase_   ,		repo_id="test-feature-extractor"   ,		push_to_hub=lowerCAmelCase_   ,		use_auth_token=self._token)

         lowercase_     =      WavaVecaFeatureExtractor.from_pretrained(F'{USER}/test-feature-extractor')
         for k, v in feature_extractor.__dict__.items():
               self.assertEqual(lowerCAmelCase_   ,		getattr(lowerCAmelCase_   ,		lowerCAmelCase_))


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					str:
         lowercase_     =      WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase_)
         feature_extractor.push_to_hub("valid_org/test-feature-extractor"   ,		use_auth_token=self._token)

         lowercase_     =      WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor")
         for k, v in feature_extractor.__dict__.items():
               self.assertEqual(lowerCAmelCase_   ,		getattr(lowerCAmelCase_   ,		lowerCAmelCase_))

         # Reset repo
         delete_repo(token=self._token   ,		repo_id="valid_org/test-feature-extractor")

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
               feature_extractor.save_pretrained(
                   lowerCAmelCase_   ,		repo_id="valid_org/test-feature-extractor-org"   ,		push_to_hub=lowerCAmelCase_   ,		use_auth_token=self._token)

         lowercase_     =      WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org")
         for k, v in feature_extractor.__dict__.items():
               self.assertEqual(lowerCAmelCase_   ,		getattr(lowerCAmelCase_   ,		lowerCAmelCase_))


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Dict:
         CustomFeatureExtractor.register_for_auto_class()
         lowercase_     =      CustomFeatureExtractor.from_pretrained(lowerCAmelCase_)

         feature_extractor.push_to_hub("test-dynamic-feature-extractor"   ,		use_auth_token=self._token)

         # This has added the proper auto_map field to the config
         self.assertDictEqual(
             feature_extractor.auto_map   ,		{"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"}   ,		)

         lowercase_     =      AutoFeatureExtractor.from_pretrained(
             F'{USER}/test-dynamic-feature-extractor'   ,		trust_remote_code=lowerCAmelCase_)
         # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
         self.assertEqual(new_feature_extractor.__class__.__name__   ,		"CustomFeatureExtractor")







'''simple docstring'''

import functools
from typing import Any


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						list[str]				)	->							bool:
      '''simple docstring'''


      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or len(__lowerCamelCase				) == 0:
            raise ValueError("the string should be not empty string"				)

      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or not all(
          isinstance(__lowerCamelCase ,		__lowerCamelCase				) and len(__lowerCamelCase				) > 0 for item in words				):
            raise ValueError("the words should be a list of non-empty strings"				)

      # Build trie
      lowercase_     =      {}
      lowercase_     =      "WORD_KEEPER"

      for word in words:
            lowercase_     =      trie
            for c in word:
                  if c not in trie_node:
                        lowercase_     =      {}

                  lowercase_     =      trie_node[c]

            lowercase_     =      True

      lowercase_     =      len(__lowerCamelCase				)

      # Dynamic programming method
      @functools.cache
      def is_breakable(__lowerCamelCase			:						int				) -> bool:
            if index == len_string:
                  return True

            lowercase_     =      trie
            for i in range(__lowerCamelCase ,		__lowerCamelCase				):
                  lowercase_     =      trie_node.get(string[i] ,		__lowerCamelCase				)

                  if trie_node is None:
                        return False

                  if trie_node.get(__lowerCamelCase ,		__lowerCamelCase				) and is_breakable(i + 1				):
                        return True

            return False

      return is_breakable(0				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






