





'''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


_SCREAMING_SNAKE_CASE	 =							get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_SCREAMING_SNAKE_CASE	 =							get_tests_dir("fixtures/vocab.json")
_SCREAMING_SNAKE_CASE	 =							get_tests_dir("fixtures")

class    lowerCAmelCase_		(		unittest.TestCase		):
 __lowerCamelCase						:							Any         =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      0




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"		)
     self.assertIsInstance(_lowerCAmelCase			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[str]:
     with tempfile.TemporaryDirectory() as tmpdirname:
         _lowerCAmelCase				=      WavaVecaConfig()
         _lowerCAmelCase				=      AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"		)

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

         _lowerCAmelCase				=      AutoProcessor.from_pretrained(_lowerCAmelCase		)

     self.assertIsInstance(_lowerCAmelCase			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							int:
     with tempfile.TemporaryDirectory() as tmpdirname:
         # copy relevant files
         copyfile(_lowerCAmelCase			,     os.path.join(_lowerCAmelCase			,     _lowerCAmelCase		)		)
         copyfile(_lowerCAmelCase			,     os.path.join(_lowerCAmelCase			,     "vocab.json"		)		)

         _lowerCAmelCase				=      AutoProcessor.from_pretrained(_lowerCAmelCase		)

     self.assertIsInstance(_lowerCAmelCase			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							str:
     with tempfile.TemporaryDirectory() as tmpdirname:
         _lowerCAmelCase				=      WavaVecaFeatureExtractor()
         _lowerCAmelCase				=      AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"		)

         _lowerCAmelCase				=      WavaVecaProcessor(_lowerCAmelCase			,     _lowerCAmelCase		)

         # save in new folder
         processor.save_pretrained(_lowerCAmelCase		)

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

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

         _lowerCAmelCase				=      AutoProcessor.from_pretrained(_lowerCAmelCase		)

     self.assertIsInstance(_lowerCAmelCase			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							int:
     with tempfile.TemporaryDirectory() as tmpdirname:
         _lowerCAmelCase				=      WavaVecaFeatureExtractor()
         _lowerCAmelCase				=      AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"		)

         _lowerCAmelCase				=      WavaVecaProcessor(_lowerCAmelCase			,     _lowerCAmelCase		)

         # save in new folder
         processor.save_pretrained(_lowerCAmelCase		)

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

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

         _lowerCAmelCase				=      AutoProcessor.from_pretrained(_lowerCAmelCase		)

     self.assertIsInstance(_lowerCAmelCase			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[str]:
     with tempfile.TemporaryDirectory() as tmpdirname:
         _lowerCAmelCase				=      WavaVecaConfig(processor_class="Wav2Vec2Processor"		)
         model_config.save_pretrained(_lowerCAmelCase		)
         # copy relevant files
         copyfile(_lowerCAmelCase			,     os.path.join(_lowerCAmelCase			,     "vocab.json"		)		)
         # create emtpy sample processor
         with open(os.path.join(_lowerCAmelCase			,     _lowerCAmelCase		)			,     "w"		) as f:
             f.write("{}"		)

         _lowerCAmelCase				=      AutoProcessor.from_pretrained(_lowerCAmelCase		)

     self.assertIsInstance(_lowerCAmelCase			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[str]:
     # If remote code is not set, we will time out when asking whether to load the model.
     with self.assertRaises(_lowerCAmelCase		):
         _lowerCAmelCase				=      AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"		)
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(_lowerCAmelCase		):
         _lowerCAmelCase				=      AutoProcessor.from_pretrained(
             "hf-internal-testing/test_dynamic_processor"			,     trust_remote_code=_lowerCAmelCase		)

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

     _lowerCAmelCase				=      processor.feature_extractor
     self.assertTrue(feature_extractor.special_attribute_present		)
     self.assertEqual(feature_extractor.__class__.__name__			,     "NewFeatureExtractor"		)

     _lowerCAmelCase				=      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
         _lowerCAmelCase				=      AutoProcessor.from_pretrained(
             "hf-internal-testing/test_dynamic_processor"			,     trust_remote_code=_lowerCAmelCase			,     use_fast=_lowerCAmelCase		)
         _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							Dict:
     try:
         AutoConfig.register("custom"			,     _lowerCAmelCase		)
         AutoFeatureExtractor.register(_lowerCAmelCase			,     _lowerCAmelCase		)
         AutoTokenizer.register(_lowerCAmelCase			,     slow_tokenizer_class=_lowerCAmelCase		)
         AutoProcessor.register(_lowerCAmelCase			,     _lowerCAmelCase		)
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(_lowerCAmelCase		):
             AutoProcessor.register(_lowerCAmelCase			,     _lowerCAmelCase		)

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

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

         _lowerCAmelCase				=      CustomProcessor(_lowerCAmelCase			,     _lowerCAmelCase		)

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

     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 							_snake_case   (	self		)    ->							str:

     class    lowerCAmelCase_		(		__magic_name__		):
      __lowerCamelCase						:							Union[str, Any]         =					False







     class    lowerCAmelCase_		(		__magic_name__		):
      __lowerCamelCase						:							Union[str, Any]         =					False







     class    lowerCAmelCase_		(		__magic_name__		):
      __lowerCamelCase						:							Tuple         =					"AutoFeatureExtractor"
      __lowerCamelCase						:							Dict         =					"AutoTokenizer"
      __lowerCamelCase						:							Dict         =					False

     try:
         AutoConfig.register("custom"			,     _lowerCAmelCase		)
         AutoFeatureExtractor.register(_lowerCAmelCase			,     _lowerCAmelCase		)
         AutoTokenizer.register(_lowerCAmelCase			,     slow_tokenizer_class=_lowerCAmelCase		)
         AutoProcessor.register(_lowerCAmelCase			,     _lowerCAmelCase		)
         # If remote code is not set, the default is to use local classes.
         _lowerCAmelCase				=      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.
         _lowerCAmelCase				=      AutoProcessor.from_pretrained(
             "hf-internal-testing/test_dynamic_processor"			,     trust_remote_code=_lowerCAmelCase		)
         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.
         _lowerCAmelCase				=      AutoProcessor.from_pretrained(
             "hf-internal-testing/test_dynamic_processor"			,     trust_remote_code=_lowerCAmelCase		)
         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 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"		)
     self.assertEqual(processor.__class__.__name__			,     "BertTokenizerFast"		)





 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"		)
     self.assertEqual(processor.__class__.__name__			,     "ConvNextImageProcessor"		)







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




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




 @classmethod
 def 							_snake_case   (	cls		)    ->							Optional[Any]:
     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 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      WavaVecaProcessor.from_pretrained(_lowerCAmelCase		)
     with tempfile.TemporaryDirectory() as tmp_dir:
         processor.save_pretrained(
             os.path.join(_lowerCAmelCase			,     "test-processor"		)			,     push_to_hub=_lowerCAmelCase			,     use_auth_token=self._token		)

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




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      WavaVecaProcessor.from_pretrained(_lowerCAmelCase		)

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

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





 def 							_snake_case   (	self		)    ->							Optional[int]:
     CustomFeatureExtractor.register_for_auto_class()
     CustomTokenizer.register_for_auto_class()
     CustomProcessor.register_for_auto_class()

     _lowerCAmelCase				=      CustomFeatureExtractor.from_pretrained(_lowerCAmelCase		)

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

     _lowerCAmelCase				=      CustomProcessor(_lowerCAmelCase			,     _lowerCAmelCase		)

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

         # 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(_lowerCAmelCase			,     "tokenizer_config.json"		)		) as f:
             _lowerCAmelCase				=      json.load(_lowerCAmelCase		)
         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(_lowerCAmelCase			,     "custom_feature_extraction.py"		)		)		)
         self.assertTrue(os.path.isfile(os.path.join(_lowerCAmelCase			,     "custom_tokenization.py"		)		)		)
         self.assertTrue(os.path.isfile(os.path.join(_lowerCAmelCase			,     "custom_processing.py"		)		)		)

         repo.push_to_hub()

     _lowerCAmelCase				=      AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor'''			,     trust_remote_code=_lowerCAmelCase		)
     # 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'''


from math import isqrt, loga


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      [True] * max_number
    for i in range(2		,       isqrt(max_number - 1    ) + 1    ):
        if is_prime[i]:
            for j in range(i**2		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
                _lowerCAmelCase				=      False

    return [i for i in range(2		,       SCREAMING_SNAKE_CASE_    ) if is_prime[i]]


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 800800		,       SCREAMING_SNAKE_CASE_  :					int = 800800    ):



    '''simple docstring'''





    _lowerCAmelCase				=      degree * loga(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      int(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      calculate_prime_numbers(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      len(SCREAMING_SNAKE_CASE_    ) - 1
    while left < right:
        while (
            prime_numbers[right] * loga(prime_numbers[left]    )
            + prime_numbers[left] * loga(prime_numbers[right]    )
            > upper_bound
        ):
            right -= 1
        hybrid_integers_count += right - left
        left += 1

    return hybrid_integers_count


if __name__ == "__main__":
       print(f'''{solution() = }''')


