




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   ):



 '''simple docstring'''


 def 				a__ (				self					:  Optional[Any]  )   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    0

 def 				a__ (				self					:  Optional[Any]  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'  )
       self.assertIsInstance(snake_case_						,							snake_case_  )

 def 				a__ (				self					:  Dict  )   ->	Any:

       """simple docstring"""






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

             lowerCamelCase_		     =    AutoImageProcessor.from_pretrained(snake_case_  )
             self.assertIsInstance(snake_case_						,							snake_case_  )

 def 				a__ (				self					:  Optional[Any]  )   ->	List[Any]:

       """simple docstring"""






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

             lowerCamelCase_		     =    AutoImageProcessor.from_pretrained(snake_case_  )
             self.assertIsInstance(snake_case_						,							snake_case_  )

 def 				a__ (				self					:  Optional[int]  )   ->	List[str]:

       """simple docstring"""






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

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

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

             config_dict.pop('image_processor_type'  )
             lowerCamelCase_		     =    CLIPImageProcessor(**snake_case_  )

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

             lowerCamelCase_		     =    AutoImageProcessor.from_pretrained(snake_case_  )

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

       self.assertIsInstance(snake_case_						,							snake_case_  )

 def 				a__ (				self					:  Any  )   ->	str:

       """simple docstring"""






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

             lowerCamelCase_		     =    AutoImageProcessor.from_pretrained(snake_case_  )
             self.assertIsInstance(snake_case_						,							snake_case_  )

 def 				a__ (				self					:  Optional[int]  )   ->	List[Any]:

       """simple docstring"""






       with self.assertRaisesRegex(
           snake_case_						,							'clip-base is not a local folder and is not a valid model identifier'  ):
             lowerCamelCase_		     =    AutoImageProcessor.from_pretrained('clip-base'  )

 def 				a__ (				self					:  Any  )   ->	int:

       """simple docstring"""






       with self.assertRaisesRegex(
           snake_case_						,							r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'  ):
             lowerCamelCase_		     =    AutoImageProcessor.from_pretrained(snake_case_						,							revision='aaaaaa'  )

 def 				a__ (				self					:  Any  )   ->	str:

       """simple docstring"""






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

 def 				a__ (				self					:  List[str]  )   ->	List[Any]:

       """simple docstring"""






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

       lowerCamelCase_		     =    AutoImageProcessor.from_pretrained(
           'hf-internal-testing/test_dynamic_image_processor'						,							trust_remote_code=snake_case_  )
       self.assertEqual(image_processor.__class__.__name__						,							'NewImageProcessor'  )

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

 def 				a__ (				self					:  str  )   ->	Optional[Any]:

       """simple docstring"""






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

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

                   lowerCamelCase_		     =    CustomImageProcessor.from_pretrained(snake_case_  )

             # 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(snake_case_  )
                   lowerCamelCase_		     =    AutoImageProcessor.from_pretrained(snake_case_  )
                   self.assertIsInstance(snake_case_						,							snake_case_  )

       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 				a__ (				self					:  Tuple  )   ->	Dict:

       """simple docstring"""






       class   A(						_a   ):



        '''simple docstring'''


        UpperCamelCase						   =						True

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

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





import string
from math import logaa





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    List[str]    						=      document.translate(
          str.maketrans(""""""     ,   """"""     ,   string.punctuation		)		).replace("""\n"""     ,   """"""		)
      snake_case__  :    List[str]    						=      document_without_punctuation.split(""" """		)  # word tokenization
      return len([word for word in tokenize_document if word.lower() == term.lower()]		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> tuple[int, int]:
      snake_case__  :    Dict    						=      corpus.lower().translate(
          str.maketrans(""""""     ,   """"""     ,   string.punctuation		)		)  # strip all punctuation and replace it with ''
      snake_case__  :    Any    						=      corpus_without_punctuation.split("""\n"""		)
      snake_case__  :    int    						=      term.lower()
      return (len([doc for doc in docs if term in doc]		), len(_lowerCAmelCase		))





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> float:
      if smoothing:
            if n == 0:
                  raise ValueError("""log10(0) is undefined."""		)
            return round(1 + logaa(n / (1 + df)		)     ,   3		)

      if df == 0:
            raise ZeroDivisionError("""df must be > 0"""		)
      elif n == 0:
            raise ValueError("""log10(0) is undefined."""		)
      return round(logaa(n / df		)     ,   3		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> float:
      return round(tf * idf     ,   3		)
