


"""simple docstring"""







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):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Any   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				0




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"   )
						self.assertIsInstance(UpperCAmelCase__			,			UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	Union[str, Any]:
						with tempfile.TemporaryDirectory() as tmpdirname:
											__SCREAMING_SNAKE_CASE						   =				Path(UpperCAmelCase__   ) / "preprocessor_config.json"
											__SCREAMING_SNAKE_CASE						   =				Path(UpperCAmelCase__   ) / "config.json"
											json.dump(
											    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}			,			open(UpperCAmelCase__			,			"w"   )			,			)
											json.dump({"model_type": "clip"}			,			open(UpperCAmelCase__			,			"w"   )   )

											__SCREAMING_SNAKE_CASE						   =				AutoImageProcessor.from_pretrained(UpperCAmelCase__   )
											self.assertIsInstance(UpperCAmelCase__			,			UpperCAmelCase__   )




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

											__SCREAMING_SNAKE_CASE						   =				AutoImageProcessor.from_pretrained(UpperCAmelCase__   )
											self.assertIsInstance(UpperCAmelCase__			,			UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Tuple:
						with tempfile.TemporaryDirectory() as tmpdirname:
											__SCREAMING_SNAKE_CASE						   =				CLIPConfig()

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

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

											config_dict.pop("image_processor_type"   )
											__SCREAMING_SNAKE_CASE						   =				CLIPImageProcessor(**UpperCAmelCase__   )

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

											__SCREAMING_SNAKE_CASE						   =				AutoImageProcessor.from_pretrained(UpperCAmelCase__   )

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

						self.assertIsInstance(UpperCAmelCase__			,			UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	Any:
						with tempfile.TemporaryDirectory() as tmpdirname:
											__SCREAMING_SNAKE_CASE						   =				Path(UpperCAmelCase__   ) / "preprocessor_config.json"
											json.dump(
											    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}			,			open(UpperCAmelCase__			,			"w"   )			,			)

											__SCREAMING_SNAKE_CASE						   =				AutoImageProcessor.from_pretrained(UpperCAmelCase__   )
											self.assertIsInstance(UpperCAmelCase__			,			UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				str   )       ->	str:
						with self.assertRaisesRegex(
						    UpperCAmelCase__			,			"clip-base is not a local folder and is not a valid model identifier"   ):
											__SCREAMING_SNAKE_CASE						   =				AutoImageProcessor.from_pretrained("clip-base"   )




	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	List[str]:
						with self.assertRaisesRegex(
						    UpperCAmelCase__			,			R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"   ):
											__SCREAMING_SNAKE_CASE						   =				AutoImageProcessor.from_pretrained(UpperCAmelCase__			,			revision="aaaaaa"   )




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




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

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

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




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	List[Any]:
						try:
											AutoConfig.register("custom"			,			UpperCAmelCase__   )
											AutoImageProcessor.register(UpperCAmelCase__			,			UpperCAmelCase__   )
											# Trying to register something existing in the Transformers library will raise an error
											with self.assertRaises(UpperCAmelCase__   ):
																AutoImageProcessor.register(UpperCAmelCase__			,			UpperCAmelCase__   )

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

																__SCREAMING_SNAKE_CASE						   =				CustomImageProcessor.from_pretrained(UpperCAmelCase__   )

											# 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(UpperCAmelCase__   )
																__SCREAMING_SNAKE_CASE						   =				AutoImageProcessor.from_pretrained(UpperCAmelCase__   )
																self.assertIsInstance(UpperCAmelCase__			,			UpperCAmelCase__   )

						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			:				Optional[Any]   )       ->	List[Any]:




						class  UpperCamelCase_	( UpperCamelCase):

							"""simple docstring"""





							snake_case__				:  Any    					=			True

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

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






"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  stooge(snake_case__		,   0		,   len(snake_case__    ) - 1    )
  return arr


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Any		,   snake_case__							:					int    ):
  """simple docstring"""

  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case		,					_snake_case							:		Tuple								=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case							:		Dict								=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )

    # Recursively sort last 2/3 elements
    stooge(snake_case__		,   i + t		,   (snake_case__)    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )


if __name__ == "__main__":
     A_						=				input('''Enter numbers separated by a comma:\n''').strip()
     A_						=				[int(item) for item in user_input.split(''',''')]
     print(stooge_sort(unsorted))


