





'''simple docstring'''


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    A_				     =							"platform"

    import jax
    import jax.numpy as jnp

    from transformers import AutoTokenizer
    from transformers.models.mbart.modeling_flax_mbart import (
        FlaxMBartForConditionalGeneration,
        FlaxMBartForQuestionAnswering,
        FlaxMBartForSequenceClassification,
        FlaxMBartModel,
        shift_tokens_right,
    )




def       A							(      _UpperCAmelCase   :				Dict						,_UpperCAmelCase   :				str						,_UpperCAmelCase   :				Tuple=None						,_UpperCAmelCase   :				Tuple=None						,_UpperCAmelCase   :				Any=None						,_UpperCAmelCase   :				Optional[int]=None						,_UpperCAmelCase   :				List[Any]=None						,_UpperCAmelCase   :				int=None						,)  ->				Any:


  '''simple docstring'''




  if attention_mask is None:
    __lowerCAmelCase		:    str			     =  np.where(input_ids != config.pad_token_id						,1						,0					)
  if decoder_attention_mask is None:
    __lowerCAmelCase		:    Optional[int]			     =  np.where(decoder_input_ids != config.pad_token_id						,1						,0					)
  if head_mask is None:
    __lowerCAmelCase		:    Union[str, Any]			     =  np.ones((config.encoder_layers, config.encoder_attention_heads)					)
  if decoder_head_mask is None:
    __lowerCAmelCase		:    List[Any]			     =  np.ones((config.decoder_layers, config.decoder_attention_heads)					)
  if cross_attn_head_mask is None:
    __lowerCAmelCase		:    int			     =  np.ones((config.decoder_layers, config.decoder_attention_heads)					)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
  }


class 		UpperCamelCase__     (       unittest.TestCase							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE=13     , SCREAMING_SNAKE_CASE=7     , SCREAMING_SNAKE_CASE=True     , SCREAMING_SNAKE_CASE=False     , SCREAMING_SNAKE_CASE=99     , SCREAMING_SNAKE_CASE=16     , SCREAMING_SNAKE_CASE=2     , SCREAMING_SNAKE_CASE=4     , SCREAMING_SNAKE_CASE=4     , SCREAMING_SNAKE_CASE="gelu"     , SCREAMING_SNAKE_CASE=0.1     , SCREAMING_SNAKE_CASE=0.1     , SCREAMING_SNAKE_CASE=32     , SCREAMING_SNAKE_CASE=2     , SCREAMING_SNAKE_CASE=1     , SCREAMING_SNAKE_CASE=0     , SCREAMING_SNAKE_CASE=2     , SCREAMING_SNAKE_CASE=0.0_2     , )				->  List[Any]:
        __lowerCAmelCase		:    Any			     =  parent
        __lowerCAmelCase		:    Optional[int]			     =  batch_size
        __lowerCAmelCase		:    List[str]			     =  seq_length
        __lowerCAmelCase		:    Dict			     =  is_training
        __lowerCAmelCase		:    int			     =  use_labels
        __lowerCAmelCase		:    Union[str, Any]			     =  vocab_size
        __lowerCAmelCase		:    str			     =  hidden_size
        __lowerCAmelCase		:    Any			     =  num_hidden_layers
        __lowerCAmelCase		:    List[str]			     =  num_attention_heads
        __lowerCAmelCase		:    Dict			     =  intermediate_size
        __lowerCAmelCase		:    List[str]			     =  hidden_act
        __lowerCAmelCase		:    Tuple			     =  hidden_dropout_prob
        __lowerCAmelCase		:    int			     =  attention_probs_dropout_prob
        __lowerCAmelCase		:    Tuple			     =  max_position_embeddings
        __lowerCAmelCase		:    str			     =  eos_token_id
        __lowerCAmelCase		:    List[str]			     =  pad_token_id
        __lowerCAmelCase		:    Union[str, Any]			     =  bos_token_id
        __lowerCAmelCase		:    str			     =  decoder_start_token_id
        __lowerCAmelCase		:    Optional[int]			     =  initializer_range




      def 	snake_case  (     self )				->  Any:
        __lowerCAmelCase		:    Union[str, Any]			     =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     , self.vocab_size )     , 3     , self.vocab_size )
        __lowerCAmelCase		:    Union[str, Any]			     =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     , dtype=np.intaa ))     , -1 )

        __lowerCAmelCase		:    Optional[Any]			     =  shift_tokens_right(SCREAMING_SNAKE_CASE     , 1 )

        __lowerCAmelCase		:    int			     =  MBartConfig(
            vocab_size=self.vocab_size     , d_model=self.hidden_size     , encoder_layers=self.num_hidden_layers     , decoder_layers=self.num_hidden_layers     , encoder_attention_heads=self.num_attention_heads     , decoder_attention_heads=self.num_attention_heads     , encoder_ffn_dim=self.intermediate_size     , decoder_ffn_dim=self.intermediate_size     , dropout=self.hidden_dropout_prob     , attention_dropout=self.attention_probs_dropout_prob     , max_position_embeddings=self.max_position_embeddings     , eos_token_id=self.eos_token_id     , bos_token_id=self.bos_token_id     , pad_token_id=self.pad_token_id     , decoder_start_token_id=self.decoder_start_token_id     , initializer_range=self.initializer_range     , use_cache=SCREAMING_SNAKE_CASE     , )
        __lowerCAmelCase		:    Optional[int]			     =  prepare_mbart_inputs_dict(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return config, inputs_dict




      def 	snake_case  (     self )				->  List[Any]:
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Any			     =  self.prepare_config_and_inputs()
        return config, inputs_dict




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Any:
        __lowerCAmelCase		:    List[Any]			     =  20
        __lowerCAmelCase		:    Optional[Any]			     =  model_class_name(SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    List[Any]			     =  model.encode(inputs_dict['input_ids'] )

        __lowerCAmelCase     ,					__lowerCAmelCase		:    int			     =  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        __lowerCAmelCase		:    List[str]			     =  model.init_cache(decoder_input_ids.shape[0]     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Dict			     =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     , dtype='i4' )

        __lowerCAmelCase		:    Tuple			     =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]     , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     , )
        __lowerCAmelCase		:    List[Any]			     =  model.decode(
            decoder_input_ids[:, :-1]     , SCREAMING_SNAKE_CASE     , decoder_attention_mask=SCREAMING_SNAKE_CASE     , past_key_values=SCREAMING_SNAKE_CASE     , decoder_position_ids=SCREAMING_SNAKE_CASE     , )

        __lowerCAmelCase		:    List[str]			     =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     , dtype='i4' )
        __lowerCAmelCase		:    List[str]			     =  model.decode(
            decoder_input_ids[:, -1:]     , SCREAMING_SNAKE_CASE     , decoder_attention_mask=SCREAMING_SNAKE_CASE     , past_key_values=outputs_cache.past_key_values     , decoder_position_ids=SCREAMING_SNAKE_CASE     , )

        __lowerCAmelCase		:    Optional[int]			     =  model.decode(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    Any			     =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
        self.parent.assertTrue(diff < 1e-3     , msg=F"""Max diff is {diff}""" )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  List[Any]:
        __lowerCAmelCase		:    Optional[int]			     =  20
        __lowerCAmelCase		:    Dict			     =  model_class_name(SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    int			     =  model.encode(inputs_dict['input_ids'] )

        __lowerCAmelCase     ,					__lowerCAmelCase		:    Dict			     =  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        __lowerCAmelCase		:    Any			     =  jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
            ]     , axis=-1     , )

        __lowerCAmelCase		:    int			     =  model.init_cache(decoder_input_ids.shape[0]     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    int			     =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]     , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     , )

        __lowerCAmelCase		:    Union[str, Any]			     =  model.decode(
            decoder_input_ids[:, :-1]     , SCREAMING_SNAKE_CASE     , decoder_attention_mask=SCREAMING_SNAKE_CASE     , past_key_values=SCREAMING_SNAKE_CASE     , decoder_position_ids=SCREAMING_SNAKE_CASE     , )
        __lowerCAmelCase		:    List[str]			     =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     , dtype='i4' )
        __lowerCAmelCase		:    List[Any]			     =  model.decode(
            decoder_input_ids[:, -1:]     , SCREAMING_SNAKE_CASE     , past_key_values=outputs_cache.past_key_values     , decoder_attention_mask=SCREAMING_SNAKE_CASE     , decoder_position_ids=SCREAMING_SNAKE_CASE     , )

        __lowerCAmelCase		:    List[Any]			     =  model.decode(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , decoder_attention_mask=SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    Optional[Any]			     =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
        self.parent.assertTrue(diff < 1e-3     , msg=F"""Max diff is {diff}""" )



@require_flax
class 		UpperCamelCase__     (       unittest.TestCase							):
      '''simple docstring'''






      _snake_case           =				99




      def 	snake_case  (     self )				->  List[str]:
        __lowerCAmelCase		:    str			     =  np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]     , dtype=np.intaa     , )

        __lowerCAmelCase		:    Union[str, Any]			     =  input_ids.shape[0]
        __lowerCAmelCase		:    str			     =  MBartConfig(
            vocab_size=self.vocab_size     , d_model=24     , encoder_layers=2     , decoder_layers=2     , encoder_attention_heads=2     , decoder_attention_heads=2     , encoder_ffn_dim=32     , decoder_ffn_dim=32     , max_position_embeddings=48     , eos_token_id=2     , pad_token_id=1     , bos_token_id=0     , )
        return config, input_ids, batch_size




      def 	snake_case  (     self )				->  Optional[int]:
        __lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase		:    Optional[int]			     =  self._get_config_and_data()
        __lowerCAmelCase		:    List[str]			     =  FlaxMBartForSequenceClassification(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    int			     =  model(input_ids=SCREAMING_SNAKE_CASE     , decoder_input_ids=SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    List[Any]			     =  (batch_size, config.num_labels)
        self.assertEqual(outputs['logits'].shape     , SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self )				->  Tuple:
        __lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase		:    Union[str, Any]			     =  self._get_config_and_data()
        __lowerCAmelCase		:    str			     =  FlaxMBartForQuestionAnswering(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Dict			     =  model(input_ids=SCREAMING_SNAKE_CASE )

        self.assertEqual(outputs['start_logits'].shape     , input_ids.shape )
        self.assertEqual(outputs['end_logits'].shape     , input_ids.shape )




      def 	snake_case  (     self )				->  int:
        __lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase		:    List[str]			     =  self._get_config_and_data()
        __lowerCAmelCase		:    List[Any]			     =  FlaxMBartForConditionalGeneration(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    List[Any]			     =  lm_model(input_ids=SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[int]			     =  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['logits'].shape     , SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self )				->  Tuple:
        __lowerCAmelCase		:    List[str]			     =  MBartConfig(
            vocab_size=self.vocab_size     , d_model=14     , encoder_layers=2     , decoder_layers=2     , encoder_attention_heads=2     , decoder_attention_heads=2     , encoder_ffn_dim=8     , decoder_ffn_dim=8     , max_position_embeddings=48     , )
        __lowerCAmelCase		:    Dict			     =  FlaxMBartForConditionalGeneration(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[Any]			     =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     , dtype=np.intaa )
        __lowerCAmelCase		:    Dict			     =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     , dtype=np.intaa )
        __lowerCAmelCase		:    int			     =  lm_model(input_ids=SCREAMING_SNAKE_CASE     , decoder_input_ids=SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[Any]			     =  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['logits'].shape     , SCREAMING_SNAKE_CASE )







      def 	snake_case  (     self )				->  int:
        __lowerCAmelCase		:    Dict			     =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     , dtype=np.intaa )
        __lowerCAmelCase		:    int			     =  shift_tokens_right(SCREAMING_SNAKE_CASE     , 1 )
        __lowerCAmelCase		:    int			     =  np.equal(SCREAMING_SNAKE_CASE     , 1 ).astype(np.floataa ).sum()
        __lowerCAmelCase		:    Dict			     =  np.equal(SCREAMING_SNAKE_CASE     , 1 ).astype(np.floataa ).sum()
        self.assertEqual(shifted.shape     , input_ids.shape )
        self.assertEqual(SCREAMING_SNAKE_CASE     , n_pad_before - 1 )
        self.assertTrue(np.equal(shifted[:, 0]     , 2 ).all() )



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






      _snake_case           =				True
      _snake_case           =				(
          (
              FlaxMBartModel,
              FlaxMBartForConditionalGeneration,
              FlaxMBartForSequenceClassification,
              FlaxMBartForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      _snake_case           =				(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




      def 	snake_case  (     self )				->  List[Any]:
        __lowerCAmelCase		:    Tuple			     =  FlaxMBartModelTester(self )




      def 	snake_case  (     self )				->  Any:
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Any			     =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self )				->  Union[str, Any]:
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Any			     =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self )				->  Any:
        __lowerCAmelCase     ,					__lowerCAmelCase		:    List[str]			     =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
            __lowerCAmelCase		:    str			     =  self._prepare_for_class(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
            __lowerCAmelCase		:    Optional[Any]			     =  model_class(SCREAMING_SNAKE_CASE )

            @jax.jit
            def encode_jitted(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE=None     , **SCREAMING_SNAKE_CASE ):
              return model.encode(input_ids=SCREAMING_SNAKE_CASE     , attention_mask=SCREAMING_SNAKE_CASE )

            with self.subTest('JIT Enabled' ):
              __lowerCAmelCase		:    List[Any]			     =  encode_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()

            with self.subTest('JIT Disabled' ):
              with jax.disable_jit():
                __lowerCAmelCase		:    Optional[int]			     =  encode_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()

            self.assertEqual(len(SCREAMING_SNAKE_CASE )     , len(SCREAMING_SNAKE_CASE ) )
            for jitted_output, output in zip(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ):
              self.assertEqual(jitted_output.shape     , output.shape )




      def 	snake_case  (     self )				->  int:
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Dict			     =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
            __lowerCAmelCase		:    int			     =  model_class(SCREAMING_SNAKE_CASE )
            __lowerCAmelCase		:    Union[str, Any]			     =  model.encode(inputs_dict['input_ids']     , inputs_dict['attention_mask'] )

            __lowerCAmelCase		:    int			     =  {
                'decoder_input_ids': inputs_dict['decoder_input_ids'],
                'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                'encoder_outputs': encoder_outputs,
            }

            @jax.jit
            def decode_jitted(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ):
              return model.decode(
                  decoder_input_ids=SCREAMING_SNAKE_CASE     , decoder_attention_mask=SCREAMING_SNAKE_CASE     , encoder_outputs=SCREAMING_SNAKE_CASE     , )

            with self.subTest('JIT Enabled' ):
              __lowerCAmelCase		:    Optional[int]			     =  decode_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()

            with self.subTest('JIT Disabled' ):
              with jax.disable_jit():
                __lowerCAmelCase		:    int			     =  decode_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()

            self.assertEqual(len(SCREAMING_SNAKE_CASE )     , len(SCREAMING_SNAKE_CASE ) )
            for jitted_output, output in zip(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ):
              self.assertEqual(jitted_output.shape     , output.shape )







      @slow
      def 	snake_case  (     self )				->  List[Any]:
        for model_class_name in self.all_model_classes:
          __lowerCAmelCase		:    Optional[int]			     =  model_class_name.from_pretrained('facebook/mbart-large-cc25'     , from_pt=SCREAMING_SNAKE_CASE )
          # FlaxMBartForSequenceClassification expects eos token in input_ids
          __lowerCAmelCase		:    Optional[int]			     =  np.ones((1, 1) ) * model.config.eos_token_id
          __lowerCAmelCase		:    Any			     =  model(SCREAMING_SNAKE_CASE )
          self.assertIsNotNone(SCREAMING_SNAKE_CASE )



@require_flax
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__     (       unittest.TestCase							):
      '''simple docstring'''






      _snake_case           =				[
          ''' UN Chief Says There Is No Military Solution in Syria''',
      ]
      _snake_case           =				[
          '''Şeful ONU declară că nu există o soluţie militară în Siria''',
      ]
      _snake_case           =				'''facebook/mbart-large-en-ro'''




      @cached_property
      def 	snake_case  (     self )				->  int:
        return AutoTokenizer.from_pretrained(self.model_name )




      @cached_property
      def 	snake_case  (     self )				->  Dict:
        __lowerCAmelCase		:    List[Any]			     =  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name     , from_pt=SCREAMING_SNAKE_CASE )
        return model




      def 	snake_case  (     self     , **SCREAMING_SNAKE_CASE )				->  Dict:
        __lowerCAmelCase		:    Any			     =  self.translate_src_text(**SCREAMING_SNAKE_CASE )
        self.assertListEqual(self.expected_text     , SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self     , **SCREAMING_SNAKE_CASE )				->  Tuple:
        __lowerCAmelCase		:    str			     =  self.tokenizer(self.src_text     , **SCREAMING_SNAKE_CASE     , return_tensors='np' )
        __lowerCAmelCase		:    Tuple			     =  self.model.generate(
            model_inputs.input_ids     , attention_mask=model_inputs.attention_mask     , decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']     , early_stopping=SCREAMING_SNAKE_CASE     , num_beams=2     , ).sequences
        __lowerCAmelCase		:    Any			     =  self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE     , skip_special_tokens=SCREAMING_SNAKE_CASE )
        return generated_words







      @slow
      def 	snake_case  (     self )				->  str:
        self._assert_generated_batch_equal_expected()












'''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)}'''
    )





