





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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  0





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[str]			      =  AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"  )
       self.assertIsInstance(A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   Tuple			      =  Path(A_  ) / "preprocessor_config.json"
        UpperCamelCase      :   List[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"  )  )

        UpperCamelCase      :   Optional[Any]			      =  AutoImageProcessor.from_pretrained(A_  )
        self.assertIsInstance(A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   Tuple			      =  Path(A_  ) / "preprocessor_config.json"
        UpperCamelCase      :   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"  )  )

        UpperCamelCase      :   int			      =  AutoImageProcessor.from_pretrained(A_  )
        self.assertIsInstance(A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   List[Any]			      =  CLIPConfig()

        # Create a dummy config file with image_proceesor_type
        UpperCamelCase      :   Optional[int]			      =  Path(A_  ) / "preprocessor_config.json"
        UpperCamelCase      :   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
        UpperCamelCase      :   str			      =  AutoImageProcessor.from_pretrained(A_  ).to_dict()

        config_dict.pop("image_processor_type"  )
        UpperCamelCase      :   Optional[Any]			      =  CLIPImageProcessor(**A_  )

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

        UpperCamelCase      :   int			      =  AutoImageProcessor.from_pretrained(A_  )

        # 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(A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   Union[str, Any]			      =  Path(A_  ) / "preprocessor_config.json"
        json.dump(
            {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}			,				open(A_			,				"w"  )			,				)

        UpperCamelCase      :   Any			      =  AutoImageProcessor.from_pretrained(A_  )
        self.assertIsInstance(A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       with self.assertRaisesRegex(
           A_			,				"clip-base is not a local folder and is not a valid model identifier"  ):
        UpperCamelCase      :   Union[str, Any]			      =  AutoImageProcessor.from_pretrained("clip-base"  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       with self.assertRaisesRegex(
           A_			,				R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"  ):
        UpperCamelCase      :   Union[str, Any]			      =  AutoImageProcessor.from_pretrained(A_			,				revision="aaaaaa"  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       with self.assertRaisesRegex(
           A_			,				"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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       with self.assertRaises(A_  ):
        UpperCamelCase      :   Tuple			      =  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_  ):
        UpperCamelCase      :   List[str]			      =  AutoImageProcessor.from_pretrained(
            "hf-internal-testing/test_dynamic_image_processor"			,				trust_remote_code=A_  )

       UpperCamelCase      :   Optional[int]			      =  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_  )
        UpperCamelCase      :   str			      =  AutoImageProcessor.from_pretrained(A_			,				trust_remote_code=A_  )
       self.assertEqual(reloaded_image_processor.__class__.__name__			,				"NewImageProcessor"  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       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:
         UpperCamelCase      :   Tuple			      =  Path(A_  ) / "preprocessor_config.json"
         UpperCamelCase      :   str			      =  Path(A_  ) / "config.json"
         json.dump(
             {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}			,				open(A_			,				"w"  )			,				)
         json.dump({"model_type": "clip"}			,				open(A_			,				"w"  )  )

         UpperCamelCase      :   str			      =  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_  )
         UpperCamelCase      :   Union[str, 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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       class    A__  (			__snake_case     ):
             _UpperCAmelCase      :str		       =  True

       try:
        AutoConfig.register("custom"			,				A_  )
        AutoImageProcessor.register(A_			,				A_  )
        # If remote code is not set, the default is to use local
        UpperCamelCase      :   Optional[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.
        UpperCamelCase      :   Optional[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
        UpperCamelCase      :   Union[str, Any]			      =  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 qiskit



def 						A_							(	_lowerCAmelCase = 8				,  _lowerCAmelCase = None	)		->					str:
 UpperCamelCase      :   Tuple			      =  np.random.default_rng(seed=_lowerCAmelCase	)

 # Roughly 25% of the qubits will contribute to the key.
 # So we take more than we need.
 UpperCamelCase      :   List[str]			      =  6 * key_len
 # Measurement basis for Alice's qubits.
 UpperCamelCase      :   List[Any]			      =  rng.integers(2				,  size=_lowerCAmelCase	)
 # The set of states Alice will prepare.
 UpperCamelCase      :   List[Any]			      =  rng.integers(2				,  size=_lowerCAmelCase	)
 # Measurement basis for Bob's qubits.
 UpperCamelCase      :   Optional[int]			      =  rng.integers(2				,  size=_lowerCAmelCase	)

 # Quantum Circuit to simulate BB84
 UpperCamelCase      :   List[Any]			      =  qiskit.QuantumCircuit(_lowerCAmelCase				,  name="BB84"	)

 # Alice prepares her qubits according to rules above.
 for index, _ in enumerate(_lowerCAmelCase	):
  if alice_state[index] == 1:
   bbaa_circ.x(_lowerCAmelCase	)
  if alice_basis[index] == 1:
   bbaa_circ.h(_lowerCAmelCase	)
 bbaa_circ.barrier()

 # Bob measures the received qubits according to rules above.
 for index, _ in enumerate(_lowerCAmelCase	):
  if bob_basis[index] == 1:
   bbaa_circ.h(_lowerCAmelCase	)

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 UpperCamelCase      :   Union[str, Any]			      =  qiskit.Aer.get_backend("aer_simulator"	)
 # We only need to run one shot because the key is unique.
 # Multiple shots will produce the same key.
 UpperCamelCase      :   Tuple			      =  qiskit.execute(_lowerCAmelCase				,  _lowerCAmelCase				,  shots=1				,  seed_simulator=_lowerCAmelCase	)
 # Returns the result of measurement.
 UpperCamelCase      :   Optional[Any]			      =  job.result().get_counts(_lowerCAmelCase	).most_frequent()

 # Extracting the generated key from the simulation results.
 # Only keep measurement results where Alice and Bob chose the same basis.
 UpperCamelCase      :   Tuple			      =  "".join(
     [
         result_bit
         for alice_basis_bit, bob_basis_bit, result_bit in zip(
             _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)
         if alice_basis_bit == bob_basis_bit
     ]	)

 # Get final key. Pad with 0 if too short, otherwise truncate.
 UpperCamelCase      :   Tuple			      =  gen_key[:key_len] if len(_lowerCAmelCase	) >= key_len else gen_key.ljust(_lowerCAmelCase				,  "0"	)
 return key


if __name__ == "__main__":
    print(f"""The generated key is : {bbaa(8, seed=0)}""")
    from doctest import testmod

    testmod()
