
"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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__   :					List[Any]							=   "platform"

  import jax
  import jax.numpy as jnp

  from transformers import BlenderbotTokenizer
  from transformers.models.blenderbot.modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      shift_tokens_right,
  )




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase=None ,				_UpperCamelCase=None ,				_UpperCamelCase=None ,				_UpperCamelCase=None ,				_UpperCamelCase=None ,				_UpperCamelCase=None ,				):






  """simple docstring"""



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


class 			__magic_name__       :
       def __init__(     self ,				A_ ,				A_=13 ,				A_=7 ,				A_=True ,				A_=False ,				A_=99 ,				A_=16 ,				A_=2 ,				A_=4 ,				A_=4 ,				A_="gelu" ,				A_=0.1 ,				A_=0.1 ,				A_=32 ,				A_=2 ,				A_=1 ,				A_=0 ,				A_=0.02 ,				)     ->	Any:





         """simple docstring"""




         _lowercase:			List[str]       =     parent
         _lowercase:			int       =     batch_size
         _lowercase:			Tuple       =     seq_length
         _lowercase:			Tuple       =     is_training
         _lowercase:			Dict       =     use_labels
         _lowercase:			Dict       =     vocab_size
         _lowercase:			Any       =     hidden_size
         _lowercase:			Tuple       =     num_hidden_layers
         _lowercase:			Tuple       =     num_attention_heads
         _lowercase:			Optional[Any]       =     intermediate_size
         _lowercase:			str       =     hidden_act
         _lowercase:			Any       =     hidden_dropout_prob
         _lowercase:			Dict       =     attention_probs_dropout_prob
         _lowercase:			Optional[int]       =     max_position_embeddings
         _lowercase:			int       =     eos_token_id
         _lowercase:			Tuple       =     pad_token_id
         _lowercase:			Union[str, Any]       =     bos_token_id
         _lowercase:			List[str]       =     initializer_range
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			Dict       =     np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,				self.vocab_size       ) ,				3 ,				self.vocab_size       )
         _lowercase:			Tuple       =     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,				dtype=np.intaa       )) ,				-1       )

         _lowercase:			int       =     shift_tokens_right(__lowerCamelCase ,				1 ,				2       )

         _lowercase:			Tuple       =     BlenderbotConfig(
             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 ,				initializer_range=self.initializer_range ,				use_cache=__lowerCamelCase ,				)
         _lowercase:			Tuple       =     prepare_blenderbot_inputs_dict(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase       )
         return config, inputs_dict
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			int       =     self.prepare_config_and_inputs()
         return config, inputs_dict
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Any       =     20
         _lowercase:			Any       =     model_class_name(__lowerCamelCase       )

         _lowercase:			Tuple       =     model.encode(inputs_dict['''input_ids''']       )

         _lowercase:			int       =     (
             inputs_dict["decoder_input_ids"],
             inputs_dict["decoder_attention_mask"],
         )

         _lowercase:			str       =     model.init_cache(decoder_input_ids.shape[0] ,				__lowerCamelCase ,				__lowerCamelCase       )
         _lowercase:			List[Any]       =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,				dtype='''i4'''       )

         _lowercase:			Dict       =     jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :] ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,				)
         _lowercase:			Dict       =     model.decode(
             decoder_input_ids[:, :-1] ,				__lowerCamelCase ,				decoder_attention_mask=__lowerCamelCase ,				past_key_values=__lowerCamelCase ,				decoder_position_ids=__lowerCamelCase ,				)

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

         _lowercase:			Dict       =     model.decode(__lowerCamelCase ,				__lowerCamelCase       )

         _lowercase:			str       =     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 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			List[Any]       =     20
         _lowercase:			Optional[int]       =     model_class_name(__lowerCamelCase       )

         _lowercase:			List[str]       =     model.encode(inputs_dict['''input_ids''']       )

         _lowercase:			int       =     (
             inputs_dict["decoder_input_ids"],
             inputs_dict["decoder_attention_mask"],
         )

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

         _lowercase:			Tuple       =     model.init_cache(decoder_input_ids.shape[0] ,				__lowerCamelCase ,				__lowerCamelCase       )
         _lowercase:			Dict       =     jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :] ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,				)

         _lowercase:			Optional[int]       =     model.decode(
             decoder_input_ids[:, :-1] ,				__lowerCamelCase ,				decoder_attention_mask=__lowerCamelCase ,				past_key_values=__lowerCamelCase ,				decoder_position_ids=__lowerCamelCase ,				)
         _lowercase:			Dict       =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,				dtype='''i4'''       )
         _lowercase:			Dict       =     model.decode(
             decoder_input_ids[:, -1:] ,				__lowerCamelCase ,				past_key_values=outputs_cache.past_key_values ,				decoder_attention_mask=__lowerCamelCase ,				decoder_position_ids=__lowerCamelCase ,				)

         _lowercase:			Dict       =     model.decode(__lowerCamelCase ,				__lowerCamelCase ,				decoder_attention_mask=__lowerCamelCase       )

         _lowercase:			Tuple       =     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 			__magic_name__       (       unittest.TestCase ):
       UpperCamelCase_   =		99
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     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 ,				)

         _lowercase:			Optional[Any]       =     input_ids.shape[0]
         _lowercase:			str       =     BlenderbotConfig(
             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 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			List[str]       =     self._get_config_and_data()
         _lowercase:			Optional[Any]       =     FlaxBlenderbotForConditionalGeneration(__lowerCamelCase       )
         _lowercase:			Any       =     lm_model(input_ids=__lowerCamelCase       )
         _lowercase:			str       =     (batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape ,				__lowerCamelCase       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			str       =     BlenderbotConfig(
             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 ,				)
         _lowercase:			Optional[int]       =     FlaxBlenderbotForConditionalGeneration(__lowerCamelCase       )
         _lowercase:			str       =     np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,				dtype=np.intaa       )
         _lowercase:			List[str]       =     np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,				dtype=np.intaa       )
         _lowercase:			int       =     lm_model(input_ids=__lowerCamelCase ,				decoder_input_ids=__lowerCamelCase       )
         _lowercase:			List[Any]       =     (*summary.shape, config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape ,				__lowerCamelCase       )



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




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


@require_flax
class 			__magic_name__       (       lowercase__   ,					unittest.TestCase   ,					lowercase__ ):
       UpperCamelCase_   =		True
       UpperCamelCase_   =		(
           (
               FlaxBlenderbotModel,
               FlaxBlenderbotForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       UpperCamelCase_   =		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			int       =     FlaxBlenderbotModelTester(self       )
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			int       =     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(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase       )
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__       ):
             _lowercase:			Any       =     self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase       )
             _lowercase:			Union[str, Any]       =     model_class(__lowerCamelCase       )

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

             with self.subTest('''JIT Enabled'''       ):
               _lowercase:			Optional[Any]       =     encode_jitted(**__lowerCamelCase       ).to_tuple()

             with self.subTest('''JIT Disabled'''       ):
               with jax.disable_jit():
                 _lowercase:			List[Any]       =     encode_jitted(**__lowerCamelCase       ).to_tuple()

             self.assertEqual(len(__lowerCamelCase       ) ,				len(__lowerCamelCase       )       )
             for jitted_output, output in zip(__lowerCamelCase ,				__lowerCamelCase       ):
               self.assertEqual(jitted_output.shape ,				output.shape       )
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			Dict       =     self.model_tester.prepare_config_and_inputs_for_common()

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

             _lowercase:			List[Any]       =     {
                 "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(A_ ,				A_ ,				A_       ):
               return model.decode(
                   decoder_input_ids=__lowerCamelCase ,				decoder_attention_mask=__lowerCamelCase ,				encoder_outputs=__lowerCamelCase ,				)

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

             with self.subTest('''JIT Disabled'''       ):
               with jax.disable_jit():
                 _lowercase:			Any       =     decode_jitted(**__lowerCamelCase       ).to_tuple()

             self.assertEqual(len(__lowerCamelCase       ) ,				len(__lowerCamelCase       )       )
             for jitted_output, output in zip(__lowerCamelCase ,				__lowerCamelCase       ):
               self.assertEqual(jitted_output.shape ,				output.shape       )
       @slow
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         for model_class_name in self.all_model_classes:
           _lowercase:			str       =     model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''       )
           # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
           _lowercase:			int       =     np.ones((1, 1)       ) * model.config.eos_token_id
           _lowercase:			Optional[int]       =     model(__lowerCamelCase       )
           self.assertIsNotNone(__lowerCamelCase       )



       @unittest.skipUnless(jax_device != '''cpu''' ,				'''3B test too slow on CPU.'''       )
       @slow
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			List[Any]       =     {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
         _lowercase:			Any       =     {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

         _lowercase:			Dict       =     FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''' ,				from_pt=__lowerCamelCase       )
         _lowercase:			Dict       =     BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''       )

         _lowercase:			Dict       =     ["Sam"]
         _lowercase:			List[Any]       =     tokenizer(__lowerCamelCase ,				return_tensors='''jax'''       )

         _lowercase:			Any       =     model.generate(**__lowerCamelCase ,				**__lowerCamelCase       )
         _lowercase:			List[Any]       =     "Sam is a great name. It means \"sun\" in Gaelic."

         _lowercase:			int       =     tokenizer.batch_decode(__lowerCamelCase ,				**__lowerCamelCase       )
         assert generated_txt[0].strip() == tgt_text


"""simple docstring"""




import qiskit




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Dict       =     qiskit.Aer.get_backend('''aer_simulator'''						)

  # Create a Quantum Circuit acting on the q register
  _lowercase:			str       =     qiskit.QuantumCircuit(_UpperCamelCase ,				_UpperCamelCase						)

  # Apply X (NOT) Gate to Qubits 0 & 1
  circuit.x(0						)
  circuit.x(1						)

  # Map the quantum measurement to the classical bits
  circuit.measure([0, 1] ,				[0, 1]						)

  # Execute the circuit on the qasm simulator
  _lowercase:			Tuple       =     qiskit.execute(_UpperCamelCase ,				_UpperCamelCase ,				shots=1_000						)

  # Return the histogram data of the results of the experiment.
  return job.result().get_counts(_UpperCamelCase						)


if __name__ == "__main__":
  A__   :					Optional[int]							=   single_qubit_measure(2, 2)
  print(f"""Total count for various states are: {counts}""")
