





'''simple docstring'''


import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class 		UpperCamelCase__     (       a	,	unittest.TestCase							):
      '''simple docstring'''






      _snake_case           =				BartTokenizer
      _snake_case           =				BartTokenizerFast
      _snake_case           =				True
      _snake_case           =				filter_roberta_detectors




      def 	snake_case  (     self )				->  Dict:
        super().setUp()
        __lowerCAmelCase		:    int			     =  [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        __lowerCAmelCase		:    Any			     =  dict(zip(SCREAMING_SNAKE_CASE     , range(len(SCREAMING_SNAKE_CASE ) ) ) )
        __lowerCAmelCase		:    Union[str, Any]			     =  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        __lowerCAmelCase		:    Any			     =  {'unk_token': '<unk>'}

        __lowerCAmelCase		:    Optional[Any]			     =  os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES['vocab_file'] )
        __lowerCAmelCase		:    Union[str, Any]			     =  os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES['merges_file'] )
        with open(self.vocab_file     , 'w'     , encoding='utf-8' ) as fp:
          fp.write(json.dumps(SCREAMING_SNAKE_CASE ) + '\n' )
        with open(self.merges_file     , 'w'     , encoding='utf-8' ) as fp:
          fp.write('\n'.join(SCREAMING_SNAKE_CASE ) )




      def 	snake_case  (     self     , **SCREAMING_SNAKE_CASE )				->  List[Any]:
        kwargs.update(self.special_tokens_map )
        return self.tokenizer_class.from_pretrained(self.tmpdirname     , **SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self     , **SCREAMING_SNAKE_CASE )				->  List[Any]:
        kwargs.update(self.special_tokens_map )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     , **SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  Union[str, Any]:
        return "lower newer", "lower newer"




      @cached_property
      def 	snake_case  (     self )				->  Union[str, Any]:
        return BartTokenizer.from_pretrained('facebook/bart-large' )




      @cached_property
      def 	snake_case  (     self )				->  Tuple:
        return BartTokenizerFast.from_pretrained('facebook/bart-large' )




      @require_torch
      def 	snake_case  (     self )				->  Tuple:
        __lowerCAmelCase		:    int			     =  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        __lowerCAmelCase		:    Tuple			     =  [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          __lowerCAmelCase		:    Union[str, Any]			     =  tokenizer(SCREAMING_SNAKE_CASE     , max_length=len(SCREAMING_SNAKE_CASE )     , padding=SCREAMING_SNAKE_CASE     , return_tensors='pt' )
          self.assertIsInstance(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )

          self.assertEqual((2, 9)     , batch.input_ids.shape )
          self.assertEqual((2, 9)     , batch.attention_mask.shape )
          __lowerCAmelCase		:    Dict			     =  batch.input_ids.tolist()[0]
          self.assertListEqual(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
          # Test that special tokens are reset




      @require_torch
      def 	snake_case  (     self )				->  Optional[Any]:
        __lowerCAmelCase		:    Tuple			     =  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          __lowerCAmelCase		:    Any			     =  tokenizer(SCREAMING_SNAKE_CASE     , padding=SCREAMING_SNAKE_CASE     , return_tensors='pt' )
          # check if input_ids are returned and no labels
          self.assertIn('input_ids'     , SCREAMING_SNAKE_CASE )
          self.assertIn('attention_mask'     , SCREAMING_SNAKE_CASE )
          self.assertNotIn('labels'     , SCREAMING_SNAKE_CASE )
          self.assertNotIn('decoder_attention_mask'     , SCREAMING_SNAKE_CASE )




      @require_torch
      def 	snake_case  (     self )				->  Dict:
        __lowerCAmelCase		:    str			     =  [
            'Summary of the text.',
            'Another summary.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          __lowerCAmelCase		:    str			     =  tokenizer(text_target=SCREAMING_SNAKE_CASE     , max_length=32     , padding='max_length'     , return_tensors='pt' )
          self.assertEqual(32     , targets['input_ids'].shape[1] )




      @require_torch
      def 	snake_case  (     self )				->  int:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          __lowerCAmelCase		:    Any			     =  tokenizer(
              ['I am a small frog' * 10_24, 'I am a small frog']     , padding=SCREAMING_SNAKE_CASE     , truncation=SCREAMING_SNAKE_CASE     , return_tensors='pt' )
          self.assertIsInstance(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
          self.assertEqual(batch.input_ids.shape     , (2, 10_24) )




      @require_torch
      def 	snake_case  (     self )				->  List[str]:
        __lowerCAmelCase		:    str			     =  ['A long paragraph for summarization.']
        __lowerCAmelCase		:    Any			     =  [
            'Summary of the text.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          __lowerCAmelCase		:    List[Any]			     =  tokenizer(SCREAMING_SNAKE_CASE     , return_tensors='pt' )
          __lowerCAmelCase		:    Optional[int]			     =  tokenizer(text_target=SCREAMING_SNAKE_CASE     , return_tensors='pt' )
          __lowerCAmelCase		:    Any			     =  inputs['input_ids']
          __lowerCAmelCase		:    Dict			     =  targets['input_ids']
          self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
          self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
          self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
          self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )




      def 	snake_case  (     self )				->  List[Any]:
        pass







      def 	snake_case  (     self )				->  int:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
            __lowerCAmelCase		:    Tuple			     =  self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE     , **SCREAMING_SNAKE_CASE )
            __lowerCAmelCase		:    Dict			     =  self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE     , **SCREAMING_SNAKE_CASE )
            __lowerCAmelCase		:    int			     =  'A, <mask> AllenNLP sentence.'
            __lowerCAmelCase		:    Union[str, Any]			     =  tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE     , add_special_tokens=SCREAMING_SNAKE_CASE     , return_token_type_ids=SCREAMING_SNAKE_CASE )
            __lowerCAmelCase		:    int			     =  tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE     , add_special_tokens=SCREAMING_SNAKE_CASE     , return_token_type_ids=SCREAMING_SNAKE_CASE )

            # token_type_ids should put 0 everywhere
            self.assertEqual(sum(tokens_r['token_type_ids'] )     , sum(tokens_p['token_type_ids'] ) )

            # attention_mask should put 1 everywhere, so sum over length should be 1
            self.assertEqual(
                sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )     , sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )     , )

            __lowerCAmelCase		:    Tuple			     =  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
            __lowerCAmelCase		:    Optional[int]			     =  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

            # Rust correctly handles the space before the mask while python doesnt
            self.assertSequenceEqual(tokens_p['input_ids']     , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
            self.assertSequenceEqual(tokens_r['input_ids']     , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )

            self.assertSequenceEqual(
                SCREAMING_SNAKE_CASE     , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
            self.assertSequenceEqual(
                SCREAMING_SNAKE_CASE     , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )












'''simple docstring'''


import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def       A							(      _UpperCAmelCase   :				float = np.pi / 2						,_UpperCAmelCase   :				float = np.pi / 2						,_UpperCAmelCase   :				float = np.pi / 2					)  ->				qiskit.result.counts.Counts:


  '''simple docstring'''




  __lowerCAmelCase		:    Optional[int]			     =  QuantumRegister(3						,'qr'					)  # Define the number of quantum bits
  __lowerCAmelCase		:    Optional[int]			     =  ClassicalRegister(1						,'cr'					)  # Define the number of classical bits

  __lowerCAmelCase		:    List[Any]			     =  QuantumCircuit(_UpperCAmelCase						,_UpperCAmelCase					)  # Define the quantum circuit.

  # Build the circuit
  quantum_circuit.u(_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase						,0					)  # Quantum State to teleport
  quantum_circuit.h(1					)  # add hadamard gate
  quantum_circuit.cx(
      1						,2					)  # add control gate with qubit 1 as control and 2 as target.
  quantum_circuit.cx(0						,1					)
  quantum_circuit.h(0					)
  quantum_circuit.cz(0						,2					)  # add control z gate.
  quantum_circuit.cx(1						,2					)

  quantum_circuit.measure([2]						,[0]					)  # measure the qubit.

  # Simulate the circuit using qasm simulator
  __lowerCAmelCase		:    List[str]			     =  Aer.get_backend('aer_simulator'					)
  __lowerCAmelCase		:    Optional[Any]			     =  execute(_UpperCAmelCase						,_UpperCAmelCase						,shots=1_0_0_0					)

  return job.result().get_counts(_UpperCAmelCase					)


if __name__ == "__main__":
    print(
        "Total count for teleported state is: "
        F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
    )





