
"""simple docstring"""

import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
      import torch

      from transformers import AutoModelForCausalLM

@require_torch
class __lowerCAmelCase	(   unittest.TestCase    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'   )
      __UpperCamelCase       						= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= -1

      __UpperCamelCase       						= ids_tensor((1, 5)			,  vocab_size=model.config.vocab_size   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= model.generate(__UpperCAmelCase			,  max_new_tokens=10			,  do_sample=__UpperCAmelCase   )
      __UpperCamelCase       						= tokenizer.decode(greedy_ids[0]   )

      with CaptureStdout() as cs:
       __UpperCamelCase       						= TextStreamer(__UpperCAmelCase   )
       model.generate(__UpperCAmelCase			,  max_new_tokens=10			,  do_sample=__UpperCAmelCase			,  streamer=__UpperCAmelCase   )
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      __UpperCamelCase       						= cs.out[:-1]

      self.assertEqual(__UpperCAmelCase			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'   )
      __UpperCamelCase       						= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= -1

      __UpperCamelCase       						= ids_tensor((1, 5)			,  vocab_size=model.config.vocab_size   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= model.generate(__UpperCAmelCase			,  max_new_tokens=10			,  do_sample=__UpperCAmelCase   )
      __UpperCamelCase       						= tokenizer.decode(greedy_ids[0]   )

      __UpperCamelCase       						= TextIteratorStreamer(__UpperCAmelCase   )
      __UpperCamelCase       						= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
      __UpperCamelCase       						= Thread(target=model.generate			,  kwargs=__UpperCAmelCase   )
      thread.start()
      __UpperCamelCase       						= ''
      for new_text in streamer:
       streamer_text += new_text

      self.assertEqual(__UpperCAmelCase			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'   )
      __UpperCamelCase       						= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= -1

      __UpperCamelCase       						= ids_tensor((1, 5)			,  vocab_size=model.config.vocab_size   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= model.generate(__UpperCAmelCase			,  max_new_tokens=10			,  do_sample=__UpperCAmelCase   )
      __UpperCamelCase       						= greedy_ids[:, input_ids.shape[1] :]
      __UpperCamelCase       						= tokenizer.decode(new_greedy_ids[0]   )

      with CaptureStdout() as cs:
       __UpperCamelCase       						= TextStreamer(__UpperCAmelCase			,  skip_prompt=__UpperCAmelCase   )
       model.generate(__UpperCAmelCase			,  max_new_tokens=10			,  do_sample=__UpperCAmelCase			,  streamer=__UpperCAmelCase   )
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      __UpperCamelCase       						= cs.out[:-1]

      self.assertEqual(__UpperCAmelCase			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= AutoTokenizer.from_pretrained('distilgpt2'   )
      __UpperCamelCase       						= AutoModelForCausalLM.from_pretrained('distilgpt2'   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= -1

      __UpperCamelCase       						= torch.ones((1, 5)			,  device=__UpperCAmelCase   ).long() * model.config.bos_token_id
      with CaptureStdout() as cs:
       __UpperCamelCase       						= TextStreamer(__UpperCAmelCase			,  skip_special_tokens=__UpperCAmelCase   )
       model.generate(__UpperCAmelCase			,  max_new_tokens=1			,  do_sample=__UpperCAmelCase			,  streamer=__UpperCAmelCase   )

      # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
      # re-tokenized, must only contain one token
      __UpperCamelCase       						= cs.out[:-1]  # Remove the final "\n"
      __UpperCamelCase       						= tokenizer(__UpperCAmelCase			,  return_tensors='pt'   )
      self.assertEqual(streamer_text_tokenized.input_ids.shape			,  (1, 1)   )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'   )
      __UpperCamelCase       						= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= -1

      __UpperCamelCase       						= ids_tensor((1, 5)			,  vocab_size=model.config.vocab_size   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= TextIteratorStreamer(__UpperCAmelCase			,  timeout=0.0_0_1   )
      __UpperCamelCase       						= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
      __UpperCamelCase       						= Thread(target=model.generate			,  kwargs=__UpperCAmelCase   )
      thread.start()

      # The streamer will timeout after 0.001 seconds, so an exception will be raised
      with self.assertRaises(__UpperCAmelCase   ):
       __UpperCamelCase       						= ''
       for new_text in streamer:
        streamer_text += new_text





"""simple docstring"""

import numpy as np
import qiskit






def 							A		(       snake_case   :int = 8       ,      snake_case   :int | None = None	)   ->			str:
 __UpperCamelCase       						= np.random.default_rng(seed=snake_case	)

 # Roughly 25% of the qubits will contribute to the key.
 # So we take more than we need.
 __UpperCamelCase       						= 6 * key_len
 # Measurement basis for Alice's qubits.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)
 # The set of states Alice will prepare.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)
 # Measurement basis for Bob's qubits.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)

 # Quantum Circuit to simulate BB84
 __UpperCamelCase       						= qiskit.QuantumCircuit(snake_case       ,      name='BB84'	)

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

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

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 __UpperCamelCase       						= 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       						= qiskit.execute(snake_case       ,      snake_case       ,      shots=1       ,      seed_simulator=snake_case	)
 # Returns the result of measurement.
 __UpperCamelCase       						= job.result().get_counts(snake_case	).most_frequent()

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

 # Get final key. Pad with 0 if too short, otherwise truncate.
 __UpperCamelCase       						= gen_key[:key_len] if len(snake_case	) >= key_len else gen_key.ljust(snake_case       ,      '0'	)
 return key


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

      testmod()



