
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  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       0


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  str					=       AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''     )
      self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as tmpdirname:
         _A:  Dict					=       Path(lowerCAmelCase_     ) / '''preprocessor_config.json'''
         _A:  int					=       Path(lowerCAmelCase_     ) / '''config.json'''
         json.dump(
             {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(lowerCAmelCase_			,			'''w'''     )			,			)
         json.dump({'''model_type''': '''clip'''}			,			open(lowerCAmelCase_			,			'''w'''     )     )

         _A:  int					=       AutoImageProcessor.from_pretrained(lowerCAmelCase_     )
         self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      # Ensure we can load the image processor from the feature extractor config
      with tempfile.TemporaryDirectory() as tmpdirname:
         _A:  List[Any]					=       Path(lowerCAmelCase_     ) / '''preprocessor_config.json'''
         _A:  List[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'''     )     )

         _A:  Union[str, Any]					=       AutoImageProcessor.from_pretrained(lowerCAmelCase_     )
         self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as tmpdirname:
         _A:  Dict					=       CLIPConfig()

         # Create a dummy config file with image_proceesor_type
         _A:  Union[str, Any]					=       Path(lowerCAmelCase_     ) / '''preprocessor_config.json'''
         _A:  str					=       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
         _A:  List[str]					=       AutoImageProcessor.from_pretrained(lowerCAmelCase_     ).to_dict()

         config_dict.pop('''image_processor_type'''     )
         _A:  Union[str, Any]					=       CLIPImageProcessor(**lowerCAmelCase_     )

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

         _A:  Optional[int]					=       AutoImageProcessor.from_pretrained(lowerCAmelCase_     )

         # make sure private variable is not incorrectly saved
         _A:  Optional[int]					=       json.loads(config.to_json_string()     )
         self.assertTrue('''_processor_class''' not in dict_as_saved     )

      self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as tmpdirname:
         _A:  List[str]					=       Path(lowerCAmelCase_     ) / '''preprocessor_config.json'''
         json.dump(
             {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(lowerCAmelCase_			,			'''w'''     )			,			)

         _A:  Union[str, Any]					=       AutoImageProcessor.from_pretrained(lowerCAmelCase_     )
         self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      with self.assertRaisesRegex(
          lowerCAmelCase_			,			'''clip-base is not a local folder and is not a valid model identifier'''     ):
         _A:  Any					=       AutoImageProcessor.from_pretrained('''clip-base'''     )


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      with self.assertRaisesRegex(
          lowerCAmelCase_			,			R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''     ):
         _A:  int					=       AutoImageProcessor.from_pretrained(lowerCAmelCase_			,			revision='''aaaaaa'''     )


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      with self.assertRaisesRegex(
          lowerCAmelCase_			,			'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,			):
         _A:  Tuple					=       AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''     )


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      # If remote code is not set, we will time out when asking whether to load the model.
      with self.assertRaises(lowerCAmelCase_     ):
         _A:  Optional[int]					=       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_     ):
         _A:  Optional[int]					=       AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=lowerCAmelCase_     )

      _A:  Union[str, Any]					=       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_     )
         _A:  int					=       AutoImageProcessor.from_pretrained(lowerCAmelCase_			,			trust_remote_code=lowerCAmelCase_     )
      self.assertEqual(reloaded_image_processor.__class__.__name__			,			'''NewImageProcessor'''     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      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:
            _A:  Optional[int]					=       Path(lowerCAmelCase_     ) / '''preprocessor_config.json'''
            _A:  List[str]					=       Path(lowerCAmelCase_     ) / '''config.json'''
            json.dump(
                {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,			open(lowerCAmelCase_			,			'''w'''     )			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(lowerCAmelCase_			,			'''w'''     )     )

            _A:  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_     )
            _A:  Optional[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  __magic_name__				(					self     :							int     ):

      """simple docstring"""






      class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





         '''simple docstring'''
         __UpperCamelCase		:    int 				=    True

      try:
         AutoConfig.register('''custom'''			,			lowerCAmelCase_     )
         AutoImageProcessor.register(lowerCAmelCase_			,			lowerCAmelCase_     )
         # If remote code is not set, the default is to use local
         _A:  int					=       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.
         _A:  Tuple					=       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
         _A:  int					=       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 __future__ import annotations






class UpperCAmelCase   :





   '''simple docstring'''


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int     ):

      """simple docstring"""
      _A:  List[str]					=       data
      _A:  Node | None					=       None
      _A:  Node | None					=       None






def   lowerCamelCase__							(					a      )      ->   None:  # In Order traversal of the tree
   if tree:
      display(tree.left      )
      print(tree.data      )
      display(tree.right      )






def   lowerCamelCase__							(					a      )      ->   int:
   return 1 + max(depth_of_tree(tree.left      )		,		depth_of_tree(tree.right      )      ) if tree else 0






def   lowerCamelCase__							(					a      )      ->   bool:
   if not tree:
      return True
   if tree.left and tree.right:
      return is_full_binary_tree(tree.left      ) and is_full_binary_tree(tree.right      )
   else:
      return not tree.left and not tree.right






def   lowerCamelCase__							(					)      ->   None:  # Main function for testing.
   _A:  Optional[int]					=       Node(1      )
   _A:  int					=       Node(2      )
   _A:  str					=       Node(3      )
   _A:  Union[str, Any]					=       Node(4      )
   _A:  Dict					=       Node(5      )
   _A:  int					=       Node(6      )
   _A:  Optional[Any]					=       Node(7      )
   _A:  List[str]					=       Node(8      )
   _A:  int					=       Node(9      )

   print(is_full_binary_tree(a      )      )
   print(depth_of_tree(a      )      )
   print('''Tree is: '''      )
   display(a      )


if __name__ == "__main__":
   main()



