





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        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Optional[int]:
          lowerCamelCase__       :						Tuple        =							0







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->int:
          lowerCamelCase__       :						int        =							AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''      )
          self.assertIsInstance(A		, A      )







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

             lowerCamelCase__       :						Dict        =							AutoImageProcessor.from_pretrained(A      )
             self.assertIsInstance(A		, A      )







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

             lowerCamelCase__       :						str        =							AutoImageProcessor.from_pretrained(A      )
             self.assertIsInstance(A		, A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[Any]:
          with tempfile.TemporaryDirectory() as tmpdirname:
             lowerCamelCase__       :						List[Any]        =							CLIPConfig()

             # Create a dummy config file with image_proceesor_type
             lowerCamelCase__       :						Optional[Any]        =							Path(A      ) / '''preprocessor_config.json'''
             lowerCamelCase__       :						str        =							Path(A      ) / '''config.json'''
             json.dump(
                 {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}		, open(A		, '''w'''      )		, )
             json.dump({'''model_type''': '''clip'''}		, open(A		, '''w'''      )      )

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

             config_dict.pop('''image_processor_type'''      )
             lowerCamelCase__       :						Tuple        =							CLIPImageProcessor(**A      )

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

             lowerCamelCase__       :						Union[str, Any]        =							AutoImageProcessor.from_pretrained(A      )

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

          self.assertIsInstance(A		, A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[Any]:
          with tempfile.TemporaryDirectory() as tmpdirname:
             lowerCamelCase__       :						str        =							Path(A      ) / '''preprocessor_config.json'''
             json.dump(
                 {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}		, open(A		, '''w'''      )		, )

             lowerCamelCase__       :						List[str]        =							AutoImageProcessor.from_pretrained(A      )
             self.assertIsInstance(A		, A      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Dict:
          with self.assertRaisesRegex(
              A		, '''clip-base is not a local folder and is not a valid model identifier'''      ):
             lowerCamelCase__       :						List[str]        =							AutoImageProcessor.from_pretrained('''clip-base'''      )







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







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[Any]:
          with self.assertRaisesRegex(
              A		, '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''		, ):
             lowerCamelCase__       :						int        =							AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->List[str]:
          # If remote code is not set, we will time out when asking whether to load the model.
          with self.assertRaises(A      ):
             lowerCamelCase__       :						Dict        =							AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''      )
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(A      ):
             lowerCamelCase__       :						Dict        =							AutoImageProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_image_processor'''		, trust_remote_code=A      )

          lowerCamelCase__       :						Tuple        =							AutoImageProcessor.from_pretrained(
              '''hf-internal-testing/test_dynamic_image_processor'''		, trust_remote_code=A      )
          self.assertEqual(image_processor.__class__.__name__		, '''NewImageProcessor'''      )

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







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

             with tempfile.TemporaryDirectory() as tmpdirname:
                lowerCamelCase__       :						Any        =							Path(A      ) / '''preprocessor_config.json'''
                lowerCamelCase__       :						Union[str, Any]        =							Path(A      ) / '''config.json'''
                json.dump(
                    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}		, open(A		, '''w'''      )		, )
                json.dump({'''model_type''': '''clip'''}		, open(A		, '''w'''      )      )

                lowerCamelCase__       :						Tuple        =							CustomImageProcessor.from_pretrained(A      )

             # 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(A      )
                lowerCamelCase__       :						Optional[Any]        =							AutoImageProcessor.from_pretrained(A      )
                self.assertIsInstance(A		, A      )

          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 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Dict:


          class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
                 _UpperCAmelCase				:  Optional[Any]			   =   True

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

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










import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
       # Create universe of discourse in Python using linspace ()
       _A :   Optional[int]  			=  np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

       # Create two fuzzy sets by defining any membership function
       # (trapmf(), gbellmf(), gaussmf(), etc).
       _A :   Any  			=  [0, 25, 50]
       _A :   Dict  			=  [25, 50, 75]
       _A :   Any  			=  fuzz.membership.trimf(X, abca)
       _A :   List[Any]  			=  fuzz.membership.trimf(X, abca)

       # Compute the different operations using inbuilt functions.
       _A :   List[str]  			=  np.ones(75)
       _A :   Optional[int]  			=  np.zeros((75,))
       # 1. Union = max(µA(x), µB(x))
       _A :   Optional[Any]  			=  fuzz.fuzzy_or(X, young, X, middle_aged)[1]
       # 2. Intersection = min(µA(x), µB(x))
       _A :   Union[str, Any]  			=  fuzz.fuzzy_and(X, young, X, middle_aged)[1]
       # 3. Complement (A) = (1- min(µA(x))
       _A :   List[Any]  			=  fuzz.fuzzy_not(young)
       # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
       _A :   Tuple  			=  fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
       # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
       _A :   List[Any]  			=  young + middle_aged - (young * middle_aged)
       # 6. Algebraic Product = (µA(x) * µB(x))
       _A :   Optional[Any]  			=  young * middle_aged
       # 7. Bounded Sum = min[1,(µA(x), µB(x))]
       _A :   Dict  			=  fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
       # 8. Bounded difference = min[0,(µA(x), µB(x))]
       _A :   List[str]  			=  fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

       # max-min composition
       # max-product composition

       # Plot each set A, set B and each operation result using plot() and subplot().
       from matplotlib import pyplot as plt

       plt.figure()

       plt.subplot(4, 3, 1)
       plt.plot(X, young)
       plt.title('Young')
       plt.grid(True)

       plt.subplot(4, 3, 2)
       plt.plot(X, middle_aged)
       plt.title('Middle aged')
       plt.grid(True)

       plt.subplot(4, 3, 3)
       plt.plot(X, union)
       plt.title('union')
       plt.grid(True)

       plt.subplot(4, 3, 4)
       plt.plot(X, intersection)
       plt.title('intersection')
       plt.grid(True)

       plt.subplot(4, 3, 5)
       plt.plot(X, complement_a)
       plt.title('complement_a')
       plt.grid(True)

       plt.subplot(4, 3, 6)
       plt.plot(X, difference)
       plt.title('difference a/b')
       plt.grid(True)

       plt.subplot(4, 3, 7)
       plt.plot(X, alg_sum)
       plt.title('alg_sum')
       plt.grid(True)

       plt.subplot(4, 3, 8)
       plt.plot(X, alg_product)
       plt.title('alg_product')
       plt.grid(True)

       plt.subplot(4, 3, 9)
       plt.plot(X, bdd_sum)
       plt.title('bdd_sum')
       plt.grid(True)

       plt.subplot(4, 3, 10)
       plt.plot(X, bdd_difference)
       plt.title('bdd_difference')
       plt.grid(True)

       plt.subplots_adjust(hspace=0.5)
       plt.show()



