





'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


a__							:							List[str] =get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
a__							:							Optional[Any] =get_tests_dir('''fixtures/vocab.json''')
a__							:							Optional[Any] =get_tests_dir('''fixtures''')

class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        __UpperCamelCase       =  0






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'	)
        self.assertIsInstance(__A							,	__A	)






 def        _lowerCamelCase					(       self       :							int	):
        with tempfile.TemporaryDirectory() as tmpdirname:
               __UpperCamelCase       =  WavaVecaConfig()
               __UpperCamelCase       =  AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'	)

               # save in new folder
               model_config.save_pretrained(__A	)
               processor.save_pretrained(__A	)

               __UpperCamelCase       =  AutoProcessor.from_pretrained(__A	)

        self.assertIsInstance(__A							,	__A	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        with tempfile.TemporaryDirectory() as tmpdirname:
               # copy relevant files
               copyfile(__A							,	os.path.join(__A							,	__A	)	)
               copyfile(__A							,	os.path.join(__A							,	'vocab.json'	)	)

               __UpperCamelCase       =  AutoProcessor.from_pretrained(__A	)

        self.assertIsInstance(__A							,	__A	)






 def        _lowerCamelCase					(       self       :							Tuple	):
        with tempfile.TemporaryDirectory() as tmpdirname:
               __UpperCamelCase       =  WavaVecaFeatureExtractor()
               __UpperCamelCase       =  AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'	)

               __UpperCamelCase       =  WavaVecaProcessor(__A							,	__A	)

               # save in new folder
               processor.save_pretrained(__A	)

               # drop `processor_class` in tokenizer
               with open(os.path.join(__A							,	__A	)							,	'r'	) as f:
                      __UpperCamelCase       =  json.load(__A	)
                      config_dict.pop('processor_class'	)

               with open(os.path.join(__A							,	__A	)							,	'w'	) as f:
                      f.write(json.dumps(__A	)	)

               __UpperCamelCase       =  AutoProcessor.from_pretrained(__A	)

        self.assertIsInstance(__A							,	__A	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        with tempfile.TemporaryDirectory() as tmpdirname:
               __UpperCamelCase       =  WavaVecaFeatureExtractor()
               __UpperCamelCase       =  AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'	)

               __UpperCamelCase       =  WavaVecaProcessor(__A							,	__A	)

               # save in new folder
               processor.save_pretrained(__A	)

               # drop `processor_class` in feature extractor
               with open(os.path.join(__A							,	__A	)							,	'r'	) as f:
                      __UpperCamelCase       =  json.load(__A	)
                      config_dict.pop('processor_class'	)

               with open(os.path.join(__A							,	__A	)							,	'w'	) as f:
                      f.write(json.dumps(__A	)	)

               __UpperCamelCase       =  AutoProcessor.from_pretrained(__A	)

        self.assertIsInstance(__A							,	__A	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        with tempfile.TemporaryDirectory() as tmpdirname:
               __UpperCamelCase       =  WavaVecaConfig(processor_class='Wav2Vec2Processor'	)
               model_config.save_pretrained(__A	)
               # copy relevant files
               copyfile(__A							,	os.path.join(__A							,	'vocab.json'	)	)
               # create emtpy sample processor
               with open(os.path.join(__A							,	__A	)							,	'w'	) as f:
                      f.write('{}'	)

               __UpperCamelCase       =  AutoProcessor.from_pretrained(__A	)

        self.assertIsInstance(__A							,	__A	)






 def        _lowerCamelCase					(       self       :							int	):
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(__A	):
               __UpperCamelCase       =  AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'	)
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(__A	):
               __UpperCamelCase       =  AutoProcessor.from_pretrained(
                   'hf-internal-testing/test_dynamic_processor'							,	trust_remote_code=__A	)

        __UpperCamelCase       =  AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'							,	trust_remote_code=__A	)
        self.assertTrue(processor.special_attribute_present	)
        self.assertEqual(processor.__class__.__name__							,	'NewProcessor'	)

        __UpperCamelCase       =  processor.feature_extractor
        self.assertTrue(feature_extractor.special_attribute_present	)
        self.assertEqual(feature_extractor.__class__.__name__							,	'NewFeatureExtractor'	)

        __UpperCamelCase       =  processor.tokenizer
        self.assertTrue(tokenizer.special_attribute_present	)
        if is_tokenizers_available():
               self.assertEqual(tokenizer.__class__.__name__							,	'NewTokenizerFast'	)

               # Test we can also load the slow version
               __UpperCamelCase       =  AutoProcessor.from_pretrained(
                   'hf-internal-testing/test_dynamic_processor'							,	trust_remote_code=__A							,	use_fast=__A	)
               __UpperCamelCase       =  new_processor.tokenizer
               self.assertTrue(new_tokenizer.special_attribute_present	)
               self.assertEqual(new_tokenizer.__class__.__name__							,	'NewTokenizer'	)
        else:
               self.assertEqual(tokenizer.__class__.__name__							,	'NewTokenizer'	)






 def        _lowerCamelCase					(       self       :							Dict	):
        try:
               AutoConfig.register('custom'							,	__A	)
               AutoFeatureExtractor.register(__A							,	__A	)
               AutoTokenizer.register(__A							,	slow_tokenizer_class=__A	)
               AutoProcessor.register(__A							,	__A	)
               # Trying to register something existing in the Transformers library will raise an error
               with self.assertRaises(__A	):
                      AutoProcessor.register(__A							,	__A	)

               # Now that the config is registered, it can be used as any other config with the auto-API
               __UpperCamelCase       =  CustomFeatureExtractor.from_pretrained(__A	)

               with tempfile.TemporaryDirectory() as tmp_dir:
                      __UpperCamelCase       =  os.path.join(__A							,	'vocab.txt'	)
                      with open(__A							,	'w'							,	encoding='utf-8'	) as vocab_writer:
                             vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]	)	)
                      __UpperCamelCase       =  CustomTokenizer(__A	)

               __UpperCamelCase       =  CustomProcessor(__A							,	__A	)

               with tempfile.TemporaryDirectory() as tmp_dir:
                      processor.save_pretrained(__A	)
                      __UpperCamelCase       =  AutoProcessor.from_pretrained(__A	)
                      self.assertIsInstance(__A							,	__A	)

        finally:
               if "custom" in CONFIG_MAPPING._extra_content:
                      del CONFIG_MAPPING._extra_content["custom"]
               if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                      del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
               if CustomConfig in TOKENIZER_MAPPING._extra_content:
                      del TOKENIZER_MAPPING._extra_content[CustomConfig]
               if CustomConfig in PROCESSOR_MAPPING._extra_content:
                      del PROCESSOR_MAPPING._extra_content[CustomConfig]






 def        _lowerCamelCase					(       self       :							Optional[int]	):

        class      snake_case     (    __lowerCamelCase       ):




         """simple docstring"""



         SCREAMING_SNAKE_CASE_	:					str					=False






        class      snake_case     (    __lowerCamelCase       ):




         """simple docstring"""



         SCREAMING_SNAKE_CASE_	:					str					=False






        class      snake_case     (    __lowerCamelCase       ):




         """simple docstring"""



         SCREAMING_SNAKE_CASE_	:					Optional[Any]					="AutoFeatureExtractor"
         SCREAMING_SNAKE_CASE_	:					List[Any]					="AutoTokenizer"
         SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=False

        try:
               AutoConfig.register('custom'							,	__A	)
               AutoFeatureExtractor.register(__A							,	__A	)
               AutoTokenizer.register(__A							,	slow_tokenizer_class=__A	)
               AutoProcessor.register(__A							,	__A	)
               # If remote code is not set, the default is to use local classes.
               __UpperCamelCase       =  AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'	)
               self.assertEqual(processor.__class__.__name__							,	'NewProcessor'	)
               self.assertFalse(processor.special_attribute_present	)
               self.assertFalse(processor.feature_extractor.special_attribute_present	)
               self.assertFalse(processor.tokenizer.special_attribute_present	)

               # If remote code is disabled, we load the local ones.
               __UpperCamelCase       =  AutoProcessor.from_pretrained(
                   'hf-internal-testing/test_dynamic_processor'							,	trust_remote_code=__A	)
               self.assertEqual(processor.__class__.__name__							,	'NewProcessor'	)
               self.assertFalse(processor.special_attribute_present	)
               self.assertFalse(processor.feature_extractor.special_attribute_present	)
               self.assertFalse(processor.tokenizer.special_attribute_present	)

               # If remote is enabled, we load from the Hub.
               __UpperCamelCase       =  AutoProcessor.from_pretrained(
                   'hf-internal-testing/test_dynamic_processor'							,	trust_remote_code=__A	)
               self.assertEqual(processor.__class__.__name__							,	'NewProcessor'	)
               self.assertTrue(processor.special_attribute_present	)
               self.assertTrue(processor.feature_extractor.special_attribute_present	)
               self.assertTrue(processor.tokenizer.special_attribute_present	)

        finally:
               if "custom" in CONFIG_MAPPING._extra_content:
                      del CONFIG_MAPPING._extra_content["custom"]
               if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                      del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
               if CustomConfig in TOKENIZER_MAPPING._extra_content:
                      del TOKENIZER_MAPPING._extra_content[CustomConfig]
               if CustomConfig in PROCESSOR_MAPPING._extra_content:
                      del PROCESSOR_MAPPING._extra_content[CustomConfig]






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert'	)
        self.assertEqual(processor.__class__.__name__							,	'BertTokenizerFast'	)






 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext'	)
        self.assertEqual(processor.__class__.__name__							,	'ConvNextImageProcessor'	)






@is_staging_test
class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]






 @classmethod
 def        _lowerCamelCase					(       cls       :							Optional[Any]	):
        __UpperCamelCase       =  TOKEN
        HfFolder.save_token(__A	)






 @classmethod
 def        _lowerCamelCase					(       cls       :							int	):
        try:
               delete_repo(token=cls._token							,	repo_id='test-processor'	)
        except HTTPError:
               pass

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

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






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  WavaVecaProcessor.from_pretrained(__A	)
        with tempfile.TemporaryDirectory() as tmp_dir:
               processor.save_pretrained(
                   os.path.join(__A							,	'test-processor'	)							,	push_to_hub=__A							,	use_auth_token=self._token	)

               __UpperCamelCase       =  WavaVecaProcessor.from_pretrained(f'''{USER}/test-processor'''	)
               for k, v in processor.feature_extractor.__dict__.items():
                      self.assertEqual(__A							,	getattr(new_processor.feature_extractor							,	__A	)	)
               self.assertDictEqual(new_processor.tokenizer.get_vocab()							,	processor.tokenizer.get_vocab()	)






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  WavaVecaProcessor.from_pretrained(__A	)

        with tempfile.TemporaryDirectory() as tmp_dir:
               processor.save_pretrained(
                   os.path.join(__A							,	'test-processor-org'	)							,	push_to_hub=__A							,	use_auth_token=self._token							,	organization='valid_org'							,	)

               __UpperCamelCase       =  WavaVecaProcessor.from_pretrained('valid_org/test-processor-org'	)
               for k, v in processor.feature_extractor.__dict__.items():
                      self.assertEqual(__A							,	getattr(new_processor.feature_extractor							,	__A	)	)
               self.assertDictEqual(new_processor.tokenizer.get_vocab()							,	processor.tokenizer.get_vocab()	)






 def        _lowerCamelCase					(       self       :							Tuple	):
        CustomFeatureExtractor.register_for_auto_class()
        CustomTokenizer.register_for_auto_class()
        CustomProcessor.register_for_auto_class()

        __UpperCamelCase       =  CustomFeatureExtractor.from_pretrained(__A	)

        with tempfile.TemporaryDirectory() as tmp_dir:
               __UpperCamelCase       =  os.path.join(__A							,	'vocab.txt'	)
               with open(__A							,	'w'							,	encoding='utf-8'	) as vocab_writer:
                      vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]	)	)
               __UpperCamelCase       =  CustomTokenizer(__A	)

        __UpperCamelCase       =  CustomProcessor(__A							,	__A	)

        with tempfile.TemporaryDirectory() as tmp_dir:
               create_repo(f'''{USER}/test-dynamic-processor'''							,	token=self._token	)
               __UpperCamelCase       =  Repository(__A							,	clone_from=f'''{USER}/test-dynamic-processor'''							,	token=self._token	)
               processor.save_pretrained(__A	)

               # This has added the proper auto_map field to the feature extractor config
               self.assertDictEqual(
                   processor.feature_extractor.auto_map							,	{
                       'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor',
                       'AutoProcessor': 'custom_processing.CustomProcessor',
                   }							,	)

               # This has added the proper auto_map field to the tokenizer config
               with open(os.path.join(__A							,	'tokenizer_config.json'	)	) as f:
                      __UpperCamelCase       =  json.load(__A	)
               self.assertDictEqual(
                   tokenizer_config['auto_map']							,	{
                       'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
                       'AutoProcessor': 'custom_processing.CustomProcessor',
                   }							,	)

               # The code has been copied from fixtures
               self.assertTrue(os.path.isfile(os.path.join(__A							,	'custom_feature_extraction.py'	)	)	)
               self.assertTrue(os.path.isfile(os.path.join(__A							,	'custom_tokenization.py'	)	)	)
               self.assertTrue(os.path.isfile(os.path.join(__A							,	'custom_processing.py'	)	)	)

               repo.push_to_hub()

        __UpperCamelCase       =  AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor'''							,	trust_remote_code=__A	)
        # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
        self.assertEqual(new_processor.__class__.__name__							,	'CustomProcessor'	)







'''simple docstring'''
import random
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  Optional[Any]     ) ->      tuple:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  [], [], []
       for element in data:
              if element < pivot:
                     less.append(__lowercase     )
              elif element > pivot:
                     greater.append(__lowercase     )
              else:
                     equal.append(__lowercase     )
       return less, equal, greater


def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int     ) ->      Dict:



       """simple docstring"""






       if index >= len(__lowercase     ) or index < 0:
              return None

       __UpperCamelCase       =  items[random.randint(0				,  len(__lowercase     ) - 1     )]
       __UpperCamelCase       =  0
       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  _partition(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(__lowercase				,  __lowercase     )
       # must be in larger
       else:
              return quick_select(__lowercase				,  index - (m + count)     )
