

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AlignProcessor, EfficientNetImageProcessor



@require_vision
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						tempfile.mkdtemp()

     __lowerCAmelCase  :      Optional[int]    =						[
         """[UNK]""",
         """[CLS]""",
         """[SEP]""",
         """[PAD]""",
         """[MASK]""",
         """want""",
         """##want""",
         """##ed""",
         """wa""",
         """un""",
         """runn""",
         """##ing""",
         """,""",
         """low""",
         """lowest""",
     ]
     __lowerCAmelCase  :      List[str]    =						os.path.join(self.tmpdirname	,							VOCAB_FILES_NAMES["""vocab_file"""]   )
     with open(self.vocab_file	,							"""w"""	,							encoding="""utf-8"""   ) as vocab_writer:
        vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]   )   )

     __lowerCAmelCase  :      Any    =						{
         """do_resize""": True,
         """size""": 20,
         """do_center_crop""": True,
         """crop_size""": 18,
         """do_normalize""": True,
         """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
         """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
     }
     __lowerCAmelCase  :      Tuple    =						os.path.join(self.tmpdirname	,							lowerCAmelCase   )
     with open(self.image_processor_file	,							"""w"""	,							encoding="""utf-8"""   ) as fp:
        json.dump(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							**lowerCAmelCase			:   str   )							->     int:






     """simple docstring"""







     return BertTokenizer.from_pretrained(self.tmpdirname	,							**lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							**lowerCAmelCase			:   Any   )							->     Dict:






     """simple docstring"""







     return BertTokenizerFast.from_pretrained(self.tmpdirname	,							**lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							**lowerCAmelCase			:   List[str]   )							->     Any:






     """simple docstring"""







     return EfficientNetImageProcessor.from_pretrained(self.tmpdirname	,							**lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     str:






     """simple docstring"""







     shutil.rmtree(self.tmpdirname   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						[np.random.randint(2_55	,							size=(3, 30, 4_00)	,							dtype=np.uinta   )]
     __lowerCAmelCase  :      Any    =						[Image.fromarray(np.moveaxis(lowerCAmelCase	,							0	,							-1   )   ) for x in image_inputs]
     return image_inputs







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.get_tokenizer()
     __lowerCAmelCase  :      List[str]    =						self.get_rust_tokenizer()
     __lowerCAmelCase  :      Optional[int]    =						self.get_image_processor()

     __lowerCAmelCase  :      Dict    =						AlignProcessor(tokenizer=lowerCAmelCase	,							image_processor=lowerCAmelCase   )
     processor_slow.save_pretrained(self.tmpdirname   )
     __lowerCAmelCase  :      str    =						AlignProcessor.from_pretrained(self.tmpdirname	,							use_fast=lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						AlignProcessor(tokenizer=lowerCAmelCase	,							image_processor=lowerCAmelCase   )
     processor_fast.save_pretrained(self.tmpdirname   )
     __lowerCAmelCase  :      List[Any]    =						AlignProcessor.from_pretrained(self.tmpdirname   )

     self.assertEqual(processor_slow.tokenizer.get_vocab()	,							tokenizer_slow.get_vocab()   )
     self.assertEqual(processor_fast.tokenizer.get_vocab()	,							tokenizer_fast.get_vocab()   )
     self.assertEqual(tokenizer_slow.get_vocab()	,							tokenizer_fast.get_vocab()   )
     self.assertIsInstance(processor_slow.tokenizer	,							lowerCAmelCase   )
     self.assertIsInstance(processor_fast.tokenizer	,							lowerCAmelCase   )

     self.assertEqual(processor_slow.image_processor.to_json_string()	,							image_processor.to_json_string()   )
     self.assertEqual(processor_fast.image_processor.to_json_string()	,							image_processor.to_json_string()   )
     self.assertIsInstance(processor_slow.image_processor	,							lowerCAmelCase   )
     self.assertIsInstance(processor_fast.image_processor	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						AlignProcessor(tokenizer=self.get_tokenizer()	,							image_processor=self.get_image_processor()   )
     processor.save_pretrained(self.tmpdirname   )

     __lowerCAmelCase  :      List[str]    =						self.get_tokenizer(bos_token="""(BOS)"""	,							eos_token="""(EOS)"""   )
     __lowerCAmelCase  :      List[Any]    =						self.get_image_processor(do_normalize=lowerCAmelCase	,							padding_value=1.0   )

     __lowerCAmelCase  :      int    =						AlignProcessor.from_pretrained(
         self.tmpdirname	,							bos_token="""(BOS)"""	,							eos_token="""(EOS)"""	,							do_normalize=lowerCAmelCase	,							padding_value=1.0   )

     self.assertEqual(processor.tokenizer.get_vocab()	,							tokenizer_add_kwargs.get_vocab()   )
     self.assertIsInstance(processor.tokenizer	,							lowerCAmelCase   )

     self.assertEqual(processor.image_processor.to_json_string()	,							image_processor_add_kwargs.to_json_string()   )
     self.assertIsInstance(processor.image_processor	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.get_image_processor()
     __lowerCAmelCase  :      Tuple    =						self.get_tokenizer()

     __lowerCAmelCase  :      Optional[int]    =						AlignProcessor(tokenizer=lowerCAmelCase	,							image_processor=lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						self.prepare_image_inputs()

     __lowerCAmelCase  :      Optional[int]    =						image_processor(lowerCAmelCase	,							return_tensors="""np"""   )
     __lowerCAmelCase  :      Any    =						processor(images=lowerCAmelCase	,							return_tensors="""np"""   )

     for key in input_image_proc.keys():
        self.assertAlmostEqual(input_image_proc[key].sum()	,							input_processor[key].sum()	,							delta=1e-2   )







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






     """simple docstring"""







     __lowerCAmelCase  :      int    =						self.get_image_processor()
     __lowerCAmelCase  :      List[Any]    =						self.get_tokenizer()

     __lowerCAmelCase  :      str    =						AlignProcessor(tokenizer=lowerCAmelCase	,							image_processor=lowerCAmelCase   )

     __lowerCAmelCase  :      List[Any]    =						"""lower newer"""

     __lowerCAmelCase  :      str    =						processor(text=lowerCAmelCase   )

     __lowerCAmelCase  :      Tuple    =						tokenizer(lowerCAmelCase	,							padding="""max_length"""	,							max_length=64   )

     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]	,							encoded_processor[key]   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.get_image_processor()
     __lowerCAmelCase  :      int    =						self.get_tokenizer()

     __lowerCAmelCase  :      str    =						AlignProcessor(tokenizer=lowerCAmelCase	,							image_processor=lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						"""lower newer"""
     __lowerCAmelCase  :      List[str]    =						self.prepare_image_inputs()

     __lowerCAmelCase  :      List[str]    =						processor(text=lowerCAmelCase	,							images=lowerCAmelCase   )

     self.assertListEqual(list(inputs.keys()   )	,							["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]   )

     # test if it raises when no input is passed
     with pytest.raises(lowerCAmelCase   ):
        processor()







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.get_image_processor()
     __lowerCAmelCase  :      List[str]    =						self.get_tokenizer()

     __lowerCAmelCase  :      List[str]    =						AlignProcessor(tokenizer=lowerCAmelCase	,							image_processor=lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     __lowerCAmelCase  :      Dict    =						processor.batch_decode(lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						tokenizer.batch_decode(lowerCAmelCase   )

     self.assertListEqual(lowerCAmelCase	,							lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.get_image_processor()
     __lowerCAmelCase  :      Dict    =						self.get_tokenizer()

     __lowerCAmelCase  :      Any    =						AlignProcessor(tokenizer=lowerCAmelCase	,							image_processor=lowerCAmelCase   )

     __lowerCAmelCase  :      List[Any]    =						"""lower newer"""
     __lowerCAmelCase  :      Union[str, Any]    =						self.prepare_image_inputs()

     __lowerCAmelCase  :      List[Any]    =						processor(text=lowerCAmelCase	,							images=lowerCAmelCase   )

     self.assertListEqual(list(inputs.keys()   )	,							processor.model_input_names   )





import numpy as np
import qiskit





def      snake_case_				(__A					:						int = 8 ,       __A					:						int | None = None					)      ->     str:
   __lowerCAmelCase  :      List[Any]    =						np.random.default_rng(seed=__A					)

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

   # Quantum Circuit to simulate BB84
   __lowerCAmelCase  :      int    =						qiskit.QuantumCircuit(__A ,       name="""BB84"""					)

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

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

   bbaa_circ.barrier()
   bbaa_circ.measure_all()

   # Simulate the quantum circuit.
   __lowerCAmelCase  :      Optional[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.
   __lowerCAmelCase  :      Optional[Any]    =						qiskit.execute(__A ,       __A ,       shots=1 ,       seed_simulator=__A					)
   # Returns the result of measurement.
   __lowerCAmelCase  :      List[Any]    =						job.result().get_counts(__A					).most_frequent()

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

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


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

   testmod()


