


import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402




class    UpperCAmelCase					(       unittest.TestCase    ):




   def   lowerCamelCase_					(						self							:		Dict						):







      """simple docstring"""







      UpperCamelCase         =					0




   def   lowerCamelCase_					(						self							:		Union[str, Any]						):







      """simple docstring"""







      UpperCamelCase         =					AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""						)
      self.assertIsInstance(__magic_name__       ,       __magic_name__						)




   def   lowerCamelCase_					(						self							:		Any						):







      """simple docstring"""







      with tempfile.TemporaryDirectory() as tmpdirname:
         UpperCamelCase         =					Path(__magic_name__						) / """preprocessor_config.json"""
         UpperCamelCase         =					Path(__magic_name__						) / """config.json"""
         json.dump(
             {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}       ,       open(__magic_name__       ,       """w"""						)       ,       )
         json.dump({"""model_type""": """clip"""}       ,       open(__magic_name__       ,       """w"""						)						)

         UpperCamelCase         =					AutoImageProcessor.from_pretrained(__magic_name__						)
         self.assertIsInstance(__magic_name__       ,       __magic_name__						)




   def   lowerCamelCase_					(						self							:		Union[str, Any]						):







      """simple docstring"""







      with tempfile.TemporaryDirectory() as tmpdirname:
         UpperCamelCase         =					Path(__magic_name__						) / """preprocessor_config.json"""
         UpperCamelCase         =					Path(__magic_name__						) / """config.json"""
         json.dump(
             {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}       ,       open(__magic_name__       ,       """w"""						)       ,       )
         json.dump({"""model_type""": """clip"""}       ,       open(__magic_name__       ,       """w"""						)						)

         UpperCamelCase         =					AutoImageProcessor.from_pretrained(__magic_name__						)
         self.assertIsInstance(__magic_name__       ,       __magic_name__						)




   def   lowerCamelCase_					(						self							:		Optional[int]						):







      """simple docstring"""







      with tempfile.TemporaryDirectory() as tmpdirname:
         UpperCamelCase         =					CLIPConfig()

         # Create a dummy config file with image_proceesor_type
         UpperCamelCase         =					Path(__magic_name__						) / """preprocessor_config.json"""
         UpperCamelCase         =					Path(__magic_name__						) / """config.json"""
         json.dump(
             {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}       ,       open(__magic_name__       ,       """w"""						)       ,       )
         json.dump({"""model_type""": """clip"""}       ,       open(__magic_name__       ,       """w"""						)						)

         # remove image_processor_type to make sure config.json alone is enough to load image processor locally
         UpperCamelCase         =					AutoImageProcessor.from_pretrained(__magic_name__						).to_dict()

         config_dict.pop("""image_processor_type"""						)
         UpperCamelCase         =					CLIPImageProcessor(**__magic_name__						)

         # save in new folder
         model_config.save_pretrained(__magic_name__						)
         config.save_pretrained(__magic_name__						)

         UpperCamelCase         =					AutoImageProcessor.from_pretrained(__magic_name__						)

         # make sure private variable is not incorrectly saved
         UpperCamelCase         =					json.loads(config.to_json_string()						)
         self.assertTrue("""_processor_class""" not in dict_as_saved						)

      self.assertIsInstance(__magic_name__       ,       __magic_name__						)




   def   lowerCamelCase_					(						self							:		Dict						):







      """simple docstring"""







      with tempfile.TemporaryDirectory() as tmpdirname:
         UpperCamelCase         =					Path(__magic_name__						) / """preprocessor_config.json"""
         json.dump(
             {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}       ,       open(__magic_name__       ,       """w"""						)       ,       )

         UpperCamelCase         =					AutoImageProcessor.from_pretrained(__magic_name__						)
         self.assertIsInstance(__magic_name__       ,       __magic_name__						)




   def   lowerCamelCase_					(						self							:		str						):







      """simple docstring"""







      with self.assertRaisesRegex(
          __magic_name__       ,       """clip-base is not a local folder and is not a valid model identifier"""						):
         UpperCamelCase         =					AutoImageProcessor.from_pretrained("""clip-base"""						)




   def   lowerCamelCase_					(						self							:		List[Any]						):







      """simple docstring"""







      with self.assertRaisesRegex(
          __magic_name__       ,       R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""						):
         UpperCamelCase         =					AutoImageProcessor.from_pretrained(__magic_name__       ,       revision="""aaaaaa"""						)




   def   lowerCamelCase_					(						self							:		Any						):







      """simple docstring"""







      with self.assertRaisesRegex(
          __magic_name__       ,       """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""       ,       ):
         UpperCamelCase         =					AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""						)




   def   lowerCamelCase_					(						self							:		Optional[Any]						):







      """simple docstring"""







      with self.assertRaises(__magic_name__						):
         UpperCamelCase         =					AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""						)
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(__magic_name__						):
         UpperCamelCase         =					AutoImageProcessor.from_pretrained(
             """hf-internal-testing/test_dynamic_image_processor"""       ,       trust_remote_code=__magic_name__						)

      UpperCamelCase         =					AutoImageProcessor.from_pretrained(
          """hf-internal-testing/test_dynamic_image_processor"""       ,       trust_remote_code=__magic_name__						)
      self.assertEqual(image_processor.__class__.__name__       ,       """NewImageProcessor"""						)

      # Test image processor can be reloaded.
      with tempfile.TemporaryDirectory() as tmp_dir:
         image_processor.save_pretrained(__magic_name__						)
         UpperCamelCase         =					AutoImageProcessor.from_pretrained(__magic_name__       ,       trust_remote_code=__magic_name__						)
      self.assertEqual(reloaded_image_processor.__class__.__name__       ,       """NewImageProcessor"""						)




   def   lowerCamelCase_					(						self							:		List[Any]						):







      """simple docstring"""







      try:
         AutoConfig.register("""custom"""       ,       __magic_name__						)
         AutoImageProcessor.register(__magic_name__       ,       __magic_name__						)
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(__magic_name__						):
            AutoImageProcessor.register(__magic_name__       ,       __magic_name__						)

         with tempfile.TemporaryDirectory() as tmpdirname:
            UpperCamelCase         =					Path(__magic_name__						) / """preprocessor_config.json"""
            UpperCamelCase         =					Path(__magic_name__						) / """config.json"""
            json.dump(
                {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}       ,       open(__magic_name__       ,       """w"""						)       ,       )
            json.dump({"""model_type""": """clip"""}       ,       open(__magic_name__       ,       """w"""						)						)

            UpperCamelCase         =					CustomImageProcessor.from_pretrained(__magic_name__						)

         # Now that the config is registered, it can be used as any other config with the auto-API
         with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(__magic_name__						)
            UpperCamelCase         =					AutoImageProcessor.from_pretrained(__magic_name__						)
            self.assertIsInstance(__magic_name__       ,       __magic_name__						)

      finally:
         if "custom" in CONFIG_MAPPING._extra_content:
            del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
            del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





   def   lowerCamelCase_					(						self							:		Tuple						):







      """simple docstring"""







      class    UpperCAmelCase					(       __snake_case    ):
         lowercase						= True

      try:
         AutoConfig.register("""custom"""       ,       __magic_name__						)
         AutoImageProcessor.register(__magic_name__       ,       __magic_name__						)
         # If remote code is not set, the default is to use local
         UpperCamelCase         =					AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""						)
         self.assertEqual(image_processor.__class__.__name__       ,       """NewImageProcessor"""						)
         self.assertTrue(image_processor.is_local						)

         # If remote code is disabled, we load the local one.
         UpperCamelCase         =					AutoImageProcessor.from_pretrained(
             """hf-internal-testing/test_dynamic_image_processor"""       ,       trust_remote_code=__magic_name__						)
         self.assertEqual(image_processor.__class__.__name__       ,       """NewImageProcessor"""						)
         self.assertTrue(image_processor.is_local						)

         # If remote is enabled, we load from the Hub
         UpperCamelCase         =					AutoImageProcessor.from_pretrained(
             """hf-internal-testing/test_dynamic_image_processor"""       ,       trust_remote_code=__magic_name__						)
         self.assertEqual(image_processor.__class__.__name__       ,       """NewImageProcessor"""						)
         self.assertTrue(not hasattr(__magic_name__       ,       """is_local"""						)						)

      finally:
         if "custom" in CONFIG_MAPPING._extra_content:
            del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
            del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]








import math






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					[True] * n
			UpperCamelCase         =					False
			UpperCamelCase         =					False
			UpperCamelCase         =					True

			for i in range(3       ,  int(n**0.5 + 1	)       ,  2	):
						UpperCamelCase         =					i * 2
						while index < n:
									UpperCamelCase         =					False
									UpperCamelCase         =					index + i

			UpperCamelCase         =					[2]

			for i in range(3       ,  SCREAMING_SNAKE_CASE_       ,  2	):
						if is_prime[i]:
									primes.append(SCREAMING_SNAKE_CASE_	)

			return primes






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 999_966_663_333	):





			"""simple docstring"""




			UpperCamelCase         =					math.floor(math.sqrt(SCREAMING_SNAKE_CASE_	)	) + 100
			UpperCamelCase         =					prime_sieve(SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					0
			UpperCamelCase         =					0
			UpperCamelCase         =					primes[prime_index]

			while (last_prime**2) <= limit:
						UpperCamelCase         =					primes[prime_index + 1]

						UpperCamelCase         =					last_prime**2
						UpperCamelCase         =					next_prime**2

						# Get numbers divisible by lps(current)
						UpperCamelCase         =					lower_bound + last_prime
						while upper_bound > current <= limit:
									matches_sum += current
									current += last_prime

						# Reset the upper_bound
						while (upper_bound - next_prime) > limit:
									upper_bound -= next_prime

						# Add the numbers divisible by ups(current)
						UpperCamelCase         =					upper_bound - next_prime
						while current > lower_bound:
									matches_sum += current
									current -= next_prime

						# Remove the numbers divisible by both ups and lps
						UpperCamelCase         =					0
						while upper_bound > current <= limit:
									if current <= lower_bound:
												# Increment the current number
												current += last_prime * next_prime
												continue

									if current > limit:
												break

									# Remove twice since it was added by both ups and lps
									matches_sum -= current * 2

									# Increment the current number
									current += last_prime * next_prime

						# Setup for next pair
						UpperCamelCase         =					next_prime
						prime_index += 1

			return matches_sum


if __name__ == "__main__":
			print(solution())




