






"""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	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     List[str]:
				UpperCAmelCase_	:					Optional[Any]     	=   0





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





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

							UpperCAmelCase_	:					Optional[Any]     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		)
							self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)





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

							UpperCAmelCase_	:					List[Any]     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		)
							self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Union[str, Any]:
				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_	:					str     	=   CLIPConfig()

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

							# remove image_processor_type to make sure config.json alone is enough to load image processor locally
							UpperCAmelCase_	:					str     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		).to_dict()

							config_dict.pop("image_processor_type"		)
							UpperCAmelCase_	:					Dict     	=   CLIPImageProcessor(**lowerCAmelCase_		)

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

							UpperCAmelCase_	:					List[Any]     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		)

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

				self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)





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

							UpperCAmelCase_	:					Optional[int]     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		)
							self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)





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





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





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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Union[str, Any]:
				# If remote code is not set, we will time out when asking whether to load the model.
				with self.assertRaises(lowerCAmelCase_		):
							UpperCAmelCase_	:					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(lowerCAmelCase_		):
							UpperCAmelCase_	:					Dict     	=   AutoImageProcessor.from_pretrained(
							    "hf-internal-testing/test_dynamic_image_processor"      ,      trust_remote_code=lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   AutoImageProcessor.from_pretrained(
				    "hf-internal-testing/test_dynamic_image_processor"      ,      trust_remote_code=lowerCAmelCase_		)
				self.assertEqual(image_processor.__class__.__name__      ,      "NewImageProcessor"		)

				# Test image processor can be reloaded.
				with tempfile.TemporaryDirectory() as tmp_dir:
							image_processor.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_	:					Union[str, Any]     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_      ,      trust_remote_code=lowerCAmelCase_		)
				self.assertEqual(reloaded_image_processor.__class__.__name__      ,      "NewImageProcessor"		)





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

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

										UpperCAmelCase_	:					Tuple     	=   CustomImageProcessor.from_pretrained(lowerCAmelCase_		)

							# 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(lowerCAmelCase_		)
										UpperCAmelCase_	:					Any     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		)
										self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)

				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 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[str]:







				class      UpperCamelCase_			(__A	):
					__magic_name__       =							True

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

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









from collections import defaultdict


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	bool:
		SCREAMING_SNAKE_CASE_					      =							first_str.lower().strip()
		SCREAMING_SNAKE_CASE_					      =							second_str.lower().strip()

		# Remove whitespace
		SCREAMING_SNAKE_CASE_					      =							first_str.replace(' '    ,						'' )
		SCREAMING_SNAKE_CASE_					      =							second_str.replace(' '    ,						'' )

		# Strings of different lengths are not anagrams
		if len(lowerCAmelCase ) != len(lowerCAmelCase ):
				return False

		# Default values for count should be 0
		SCREAMING_SNAKE_CASE_					      =							defaultdict(lowerCAmelCase )

		# For each character in input strings,
		# increment count in the corresponding
		for i in range(len(lowerCAmelCase ) ):
				count[first_str[i]] += 1
				count[second_str[i]] -= 1

		return all(_count == 0 for _count in count.values() )


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						SCREAMING_SNAKE_CASE:     Dict									= input('''Enter the first string ''').strip()
						SCREAMING_SNAKE_CASE:     str									= input('''Enter the second string ''').strip()

						SCREAMING_SNAKE_CASE:     Optional[Any]									= check_anagrams(input_a, input_b)
						print(f"""{input_a} and {input_b} are {'' if status else 'not '}anagrams.""")