


import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class 					_a							(				unittest.TestCase):




	def 							UpperCAmelCase__(					self						:		str		)->       int:
			lowerCAmelCase__   :			Optional[int]         =					"""ylacombe/bark-small"""
			lowerCAmelCase__   :			Optional[Any]         =					tempfile.mkdtemp()
			lowerCAmelCase__   :			int         =					"""en_speaker_1"""
			lowerCAmelCase__   :			List[Any]         =					"""This is a test string"""
			lowerCAmelCase__   :			Any         =					"""speaker_embeddings_path.json"""
			lowerCAmelCase__   :			List[Any]         =					"""speaker_embeddings"""




	def 							UpperCAmelCase__(					self						:		str		,					**_SCREAMING_SNAKE_CASE						:		Dict		)->       Optional[Any]:
			return AutoTokenizer.from_pretrained(self.checkpoint		,					**a__		)




	def 							UpperCAmelCase__(					self						:		List[str]		)->       int:
			shutil.rmtree(self.tmpdirname		)




	def 							UpperCAmelCase__(					self						:		List[str]		)->       Optional[Any]:
			lowerCAmelCase__   :			List[Any]         =					self.get_tokenizer()

			lowerCAmelCase__   :			int         =					BarkProcessor(tokenizer=a__		)

			processor.save_pretrained(self.tmpdirname		)
			lowerCAmelCase__   :			str         =					BarkProcessor.from_pretrained(self.tmpdirname		)

			self.assertEqual(processor.tokenizer.get_vocab()		,					tokenizer.get_vocab()		)




	@slow
	def 							UpperCAmelCase__(					self						:		int		)->       Optional[int]:
			lowerCAmelCase__   :			Optional[int]         =					BarkProcessor.from_pretrained(
			    pretrained_processor_name_or_path=self.checkpoint		,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,					)
			processor.save_pretrained(
			    self.tmpdirname		,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,					speaker_embeddings_directory=self.speaker_embeddings_directory		,					)

			lowerCAmelCase__   :			Tuple         =					self.get_tokenizer(bos_token='''(BOS)'''		,					eos_token='''(EOS)'''		)

			lowerCAmelCase__   :			List[Any]         =					BarkProcessor.from_pretrained(
			    self.tmpdirname		,					self.speaker_embeddings_dict_path		,					bos_token='''(BOS)'''		,					eos_token='''(EOS)'''		,					)

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




	def 							UpperCAmelCase__(					self						:		Optional[int]		)->       Dict:
			lowerCAmelCase__   :			List[str]         =					BarkProcessor.from_pretrained(
			    pretrained_processor_name_or_path=self.checkpoint		,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,					)

			lowerCAmelCase__   :			Union[str, Any]         =					35
			lowerCAmelCase__   :			Union[str, Any]         =					2
			lowerCAmelCase__   :			Optional[int]         =					8

			lowerCAmelCase__   :			Dict         =					{
			    """semantic_prompt""": np.ones(a__		),
			    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)		),
			    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)		),
			}

			# test providing already loaded voice_preset
			lowerCAmelCase__   :			Dict         =					processor(text=self.input_string		,					voice_preset=a__		)

			lowerCAmelCase__   :			Tuple         =					inputs["""history_prompt"""]
			for key in voice_preset:
					self.assertListEqual(voice_preset[key].tolist()		,					processed_voice_preset.get(a__		,					np.array([]		)		).tolist()		)

			# test loading voice preset from npz file
			lowerCAmelCase__   :			List[Any]         =					os.path.join(self.tmpdirname		,					'''file.npz'''		)
			np.savez(a__		,					**a__		)
			lowerCAmelCase__   :			List[Any]         =					processor(text=self.input_string		,					voice_preset=a__		)
			lowerCAmelCase__   :			Optional[int]         =					inputs["""history_prompt"""]

			for key in voice_preset:
					self.assertListEqual(voice_preset[key].tolist()		,					processed_voice_preset.get(a__		,					np.array([]		)		).tolist()		)

			# test loading voice preset from the hub
			lowerCAmelCase__   :			str         =					processor(text=self.input_string		,					voice_preset=self.voice_preset		)




	def 							UpperCAmelCase__(					self						:		Any		)->       Dict:
			lowerCAmelCase__   :			int         =					self.get_tokenizer()

			lowerCAmelCase__   :			List[Any]         =					BarkProcessor(tokenizer=a__		)

			lowerCAmelCase__   :			Dict         =					processor(text=self.input_string		)

			lowerCAmelCase__   :			Tuple         =					tokenizer(
			    self.input_string		,					padding='''max_length'''		,					max_length=256		,					add_special_tokens=a__		,					return_attention_mask=a__		,					return_token_type_ids=a__		,					)

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





"""simple docstring"""




import numpy as np
import qiskit





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int = 8			,_lowerCamelCase						:       int | None = None	)       ->      str:
  _lowerCAmelCase						:       int  			=     np.random.default_rng(seed=_lowerCamelCase	)

  # 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						:       Dict  			=     rng.integers(2			,size=_lowerCamelCase	)
  # The set of states Alice will prepare.
  _lowerCAmelCase						:       Tuple  			=     rng.integers(2			,size=_lowerCamelCase	)
  # Measurement basis for Bob's qubits.
  _lowerCAmelCase						:       Union[str, Any]  			=     rng.integers(2			,size=_lowerCamelCase	)

  # Quantum Circuit to simulate BB84
  _lowerCAmelCase						:       Dict  			=     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.
  _lowerCAmelCase						:       int  			=     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						:       List[str]  			=     qiskit.execute(_lowerCamelCase			,_lowerCamelCase			,shots=1			,seed_simulator=_lowerCamelCase	)
  # Returns the result of measurement.
  _lowerCAmelCase						:       List[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.
  _lowerCAmelCase						:       str  			=     """""".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.
  _lowerCAmelCase						:       List[Any]  			=     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()




