'''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 		__A       (unittest.TestCase   ):






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		0






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"      )
              self.assertIsInstance(UpperCamelCase_				,     UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              with tempfile.TemporaryDirectory() as tmpdirname:
                     __UpperCAmelCase  :				Optional[Any]       				=		Path(UpperCamelCase_      ) / "preprocessor_config.json"
                     __UpperCAmelCase  :				Optional[Any]       				=		Path(UpperCamelCase_      ) / "config.json"
                     json.dump(
                         {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}				,     open(UpperCamelCase_				,     "w"      )				,     )
                     json.dump({"model_type": "clip"}				,     open(UpperCamelCase_				,     "w"      )      )

                     __UpperCAmelCase  :				Any       				=		AutoImageProcessor.from_pretrained(UpperCamelCase_      )
                     self.assertIsInstance(UpperCamelCase_				,     UpperCamelCase_      )






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

                     __UpperCAmelCase  :				Union[str, Any]       				=		AutoImageProcessor.from_pretrained(UpperCamelCase_      )
                     self.assertIsInstance(UpperCamelCase_				,     UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              with tempfile.TemporaryDirectory() as tmpdirname:
                     __UpperCAmelCase  :				str       				=		CLIPConfig()

                     # Create a dummy config file with image_proceesor_type
                     __UpperCAmelCase  :				Union[str, Any]       				=		Path(UpperCamelCase_      ) / "preprocessor_config.json"
                     __UpperCAmelCase  :				List[Any]       				=		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
                     __UpperCAmelCase  :				Dict       				=		AutoImageProcessor.from_pretrained(UpperCamelCase_      ).to_dict()

                     config_dict.pop("image_processor_type"      )
                     __UpperCAmelCase  :				Optional[Any]       				=		CLIPImageProcessor(**UpperCamelCase_      )

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

                     __UpperCAmelCase  :				str       				=		AutoImageProcessor.from_pretrained(UpperCamelCase_      )

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

              self.assertIsInstance(UpperCamelCase_				,     UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              with tempfile.TemporaryDirectory() as tmpdirname:
                     __UpperCAmelCase  :				str       				=		Path(UpperCamelCase_      ) / "preprocessor_config.json"
                     json.dump(
                         {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}				,     open(UpperCamelCase_				,     "w"      )				,     )

                     __UpperCAmelCase  :				Any       				=		AutoImageProcessor.from_pretrained(UpperCamelCase_      )
                     self.assertIsInstance(UpperCamelCase_				,     UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              with self.assertRaisesRegex(
                  UpperCamelCase_				,     "clip-base is not a local folder and is not a valid model identifier"      ):
                     __UpperCAmelCase  :				Any       				=		AutoImageProcessor.from_pretrained("clip-base"      )






       def 			_snake_case						(      self      ):
              with self.assertRaisesRegex(
                  UpperCamelCase_				,     r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"      ):
                     __UpperCAmelCase  :				Dict       				=		AutoImageProcessor.from_pretrained(UpperCamelCase_				,     revision="aaaaaa"      )






       def 			_snake_case						(      self      ):
              with self.assertRaisesRegex(
                  UpperCamelCase_				,     "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."				,     ):
                     __UpperCAmelCase  :				Optional[int]       				=		AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model"      )






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

              __UpperCAmelCase  :				Tuple       				=		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_      )
                     __UpperCAmelCase  :				int       				=		AutoImageProcessor.from_pretrained(UpperCamelCase_				,     trust_remote_code=UpperCamelCase_      )
              self.assertEqual(reloaded_image_processor.__class__.__name__				,     "NewImageProcessor"      )






       def 			_snake_case						(      self      ):
              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:
                            __UpperCAmelCase  :				Union[str, Any]       				=		Path(UpperCamelCase_      ) / "preprocessor_config.json"
                            __UpperCAmelCase  :				Dict       				=		Path(UpperCamelCase_      ) / "config.json"
                            json.dump(
                                {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}				,     open(UpperCamelCase_				,     "w"      )				,     )
                            json.dump({"model_type": "clip"}				,     open(UpperCamelCase_				,     "w"      )      )

                            __UpperCAmelCase  :				Dict       				=		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_      )
                            __UpperCAmelCase  :				Optional[Any]       				=		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 			_snake_case						(      self      ):






              class 		__A       (__magic_name__   ):
                     snake_case       :Tuple				    =					True

              try:
                     AutoConfig.register("custom"				,     UpperCamelCase_      )
                     AutoImageProcessor.register(UpperCamelCase_				,     UpperCamelCase_      )
                     # If remote code is not set, the default is to use local
                     __UpperCAmelCase  :				str       				=		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  :				Dict       				=		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
                     __UpperCAmelCase  :				Union[str, Any]       				=		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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       bool:





       """simple docstring"""


       return not any(
           neighbour == 1 and colored_vertices[i] == color
           for i, neighbour in enumerate(lowerCamelCase__						)						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       bool:





       """simple docstring"""


       if index == len(lowerCamelCase__						):
              return True

       # Recursive Step
       for i in range(lowerCamelCase__						):
              if valid_coloring(graph[index]							,    lowerCamelCase__							,    lowerCamelCase__						):
                     # Color current vertex
                     __UpperCAmelCase  :				List[str]       				=		i
                     # Validate coloring
                     if util_color(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    index + 1						):
                            return True
                     # Backtrack
                     __UpperCAmelCase  :				Any       				=		-1
       return False


def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       list[int]:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		[-1] * len(lowerCamelCase__						)

       if util_color(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    0						):
              return colored_vertices

       return []






