





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     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						Any   =						0




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						List[str]   =						AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""		)
         self.assertIsInstance(A							,				A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						List[Any]   =						Path(A		) / """preprocessor_config.json"""
           __snake_case:						Dict   =						Path(A		) / """config.json"""
           json.dump(
               {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}							,				open(A							,				"""w"""		)							,				)
           json.dump({"""model_type""": """clip"""}							,				open(A							,				"""w"""		)		)

           __snake_case:						Dict   =						AutoImageProcessor.from_pretrained(A		)
           self.assertIsInstance(A							,				A		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         # Ensure we can load the image processor from the feature extractor config
         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						Optional[Any]   =						Path(A		) / """preprocessor_config.json"""
           __snake_case:						List[Any]   =						Path(A		) / """config.json"""
           json.dump(
               {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}							,				open(A							,				"""w"""		)							,				)
           json.dump({"""model_type""": """clip"""}							,				open(A							,				"""w"""		)		)

           __snake_case:						Any   =						AutoImageProcessor.from_pretrained(A		)
           self.assertIsInstance(A							,				A		)




       def 							UpperCAmelCase__ (  self :    str		):
         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						Optional[Any]   =						CLIPConfig()

           # Create a dummy config file with image_proceesor_type
           __snake_case:						Union[str, Any]   =						Path(A		) / """preprocessor_config.json"""
           __snake_case:						str   =						Path(A		) / """config.json"""
           json.dump(
               {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}							,				open(A							,				"""w"""		)							,				)
           json.dump({"""model_type""": """clip"""}							,				open(A							,				"""w"""		)		)

           # remove image_processor_type to make sure config.json alone is enough to load image processor locally
           __snake_case:						List[Any]   =						AutoImageProcessor.from_pretrained(A		).to_dict()

           config_dict.pop("""image_processor_type"""		)
           __snake_case:						Optional[Any]   =						CLIPImageProcessor(**A		)

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

           __snake_case:						Tuple   =						AutoImageProcessor.from_pretrained(A		)

           # make sure private variable is not incorrectly saved
           __snake_case:						Union[str, Any]   =						json.loads(config.to_json_string()		)
           self.assertTrue("""_processor_class""" not in dict_as_saved		)

         self.assertIsInstance(A							,				A		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						Any   =						Path(A		) / """preprocessor_config.json"""
           json.dump(
               {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}							,				open(A							,				"""w"""		)							,				)

           __snake_case:						int   =						AutoImageProcessor.from_pretrained(A		)
           self.assertIsInstance(A							,				A		)




       def 							UpperCAmelCase__ (  self :    Tuple		):
         with self.assertRaisesRegex(
             A							,				"""clip-base is not a local folder and is not a valid model identifier"""		):
           __snake_case:						Tuple   =						AutoImageProcessor.from_pretrained("""clip-base"""		)




       def 							UpperCAmelCase__ (  self :    str		):
         with self.assertRaisesRegex(
             A							,				r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""		):
           __snake_case:						Tuple   =						AutoImageProcessor.from_pretrained(A							,				revision="""aaaaaa"""		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         with self.assertRaisesRegex(
             A							,				"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""							,				):
           __snake_case:						List[Any]   =						AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""		)




       def 							UpperCAmelCase__ (  self :    str		):
         # If remote code is not set, we will time out when asking whether to load the model.
         with self.assertRaises(A		):
           __snake_case:						List[str]   =						AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""		)
         # If remote code is disabled, we can't load this config.
         with self.assertRaises(A		):
           __snake_case:						Union[str, Any]   =						AutoImageProcessor.from_pretrained(
               """hf-internal-testing/test_dynamic_image_processor"""							,				trust_remote_code=A		)

         __snake_case:						Any   =						AutoImageProcessor.from_pretrained(
             """hf-internal-testing/test_dynamic_image_processor"""							,				trust_remote_code=A		)
         self.assertEqual(image_processor.__class__.__name__							,				"""NewImageProcessor"""		)

         # Test image processor can be reloaded.
         with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(A		)
           __snake_case:						Optional[int]   =						AutoImageProcessor.from_pretrained(A							,				trust_remote_code=A		)
         self.assertEqual(reloaded_image_processor.__class__.__name__							,				"""NewImageProcessor"""		)




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

           with tempfile.TemporaryDirectory() as tmpdirname:
             __snake_case:						Optional[Any]   =						Path(A		) / """preprocessor_config.json"""
             __snake_case:						Optional[int]   =						Path(A		) / """config.json"""
             json.dump(
                 {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}							,				open(A							,				"""w"""		)							,				)
             json.dump({"""model_type""": """clip"""}							,				open(A							,				"""w"""		)		)

             __snake_case:						List[str]   =						CustomImageProcessor.from_pretrained(A		)

           # 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(A		)
             __snake_case:						Optional[int]   =						AutoImageProcessor.from_pretrained(A		)
             self.assertIsInstance(A							,				A		)

         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 							UpperCAmelCase__ (  self :    Tuple		):







         class     __snake_case       (							__lowerCamelCase			):





                '''simple docstring'''







                lowerCAmelCase__      		=     True

         try:
           AutoConfig.register("""custom"""							,				A		)
           AutoImageProcessor.register(A							,				A		)
           # If remote code is not set, the default is to use local
           __snake_case:						List[Any]   =						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.
           __snake_case:						str   =						AutoImageProcessor.from_pretrained(
               """hf-internal-testing/test_dynamic_image_processor"""							,				trust_remote_code=A		)
           self.assertEqual(image_processor.__class__.__name__							,				"""NewImageProcessor"""		)
           self.assertTrue(image_processor.is_local		)

           # If remote is enabled, we load from the Hub
           __snake_case:						Tuple   =						AutoImageProcessor.from_pretrained(
               """hf-internal-testing/test_dynamic_image_processor"""							,				trust_remote_code=A		)
           self.assertEqual(image_processor.__class__.__name__							,				"""NewImageProcessor"""		)
           self.assertTrue(not hasattr(A							,				"""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 			A__							(    SCREAMING_SNAKE_CASE__)   ->    bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5					,					int(math.sqrt(SCREAMING_SNAKE_CASE__) + 1)					,					6):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def 			A__							(    SCREAMING_SNAKE_CASE__ = 1_0001)   ->    int:

  try:
    __snake_case:						List[str]   =						int(SCREAMING_SNAKE_CASE__)
  except (TypeError, ValueError):
    raise TypeError("""Parameter nth must be int or castable to int.""") from None
  if nth <= 0:
    raise ValueError("""Parameter nth must be greater than or equal to one.""")
  __snake_case:						list[int]   =						[]
  __snake_case:						List[str]   =						2
  while len(SCREAMING_SNAKE_CASE__) < nth:
    if is_prime(SCREAMING_SNAKE_CASE__):
      primes.append(SCREAMING_SNAKE_CASE__)
      num += 1
    else:
      num += 1
  return primes[len(SCREAMING_SNAKE_CASE__) - 1]


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






