'''simple docstring'''




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, 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
	snake_case  =			'''platform'''

	import jax
	import jax.numpy as jnp

	from transformers import MarianTokenizer
	from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							int  ,  _lowerCamelCase			:							Dict=None  ,  _lowerCamelCase			:							Dict=None  ,  _lowerCamelCase			:							Any=None  ,  _lowerCamelCase			:							int=None  ,  _lowerCamelCase			:							Any=None  ,  _lowerCamelCase			:							List[Any]=None  ,  ):
						if attention_mask is None:
												_lowerCAmelCase		   =   np.where(input_ids != config.pad_token_id  ,  1  ,  0    )
						if decoder_attention_mask is None:
												_lowerCAmelCase		   =   np.where(decoder_input_ids != config.pad_token_id  ,  1  ,  0    )
						if head_mask is None:
												_lowerCAmelCase		   =   np.ones((config.encoder_layers, config.encoder_attention_heads)    )
						if decoder_head_mask is None:
												_lowerCAmelCase		   =   np.ones((config.decoder_layers, config.decoder_attention_heads)    )
						if cross_attn_head_mask is None:
												_lowerCAmelCase		   =   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        SCREAMING_SNAKE_CASE   :
		"""simple docstring"""







		def __init__(  self   :					Union[str, Any]					,					__lowerCAmelCase   :					List[str]					,					__lowerCAmelCase   :					Optional[int]=13					,					__lowerCAmelCase   :					List[Any]=7					,					__lowerCAmelCase   :					Dict=True					,					__lowerCAmelCase   :					Optional[Any]=False					,					__lowerCAmelCase   :					List[str]=99					,					__lowerCAmelCase   :					List[str]=16					,					__lowerCAmelCase   :					List[str]=2					,					__lowerCAmelCase   :					Union[str, Any]=4					,					__lowerCAmelCase   :					Optional[Any]=4					,					__lowerCAmelCase   :					Optional[int]="gelu"					,					__lowerCAmelCase   :					List[str]=0.1					,					__lowerCAmelCase   :					Dict=0.1					,					__lowerCAmelCase   :					Union[str, Any]=32					,					__lowerCAmelCase   :					Tuple=2					,					__lowerCAmelCase   :					List[str]=1					,					__lowerCAmelCase   :					int=0					,					__lowerCAmelCase   :					Union[str, Any]=0.0_2					,					):


								"""simple docstring"""



								_lowerCAmelCase		   =   parent
								_lowerCAmelCase		   =   batch_size
								_lowerCAmelCase		   =   seq_length
								_lowerCAmelCase		   =   is_training
								_lowerCAmelCase		   =   use_labels
								_lowerCAmelCase		   =   vocab_size
								_lowerCAmelCase		   =   hidden_size
								_lowerCAmelCase		   =   num_hidden_layers
								_lowerCAmelCase		   =   num_attention_heads
								_lowerCAmelCase		   =   intermediate_size
								_lowerCAmelCase		   =   hidden_act
								_lowerCAmelCase		   =   hidden_dropout_prob
								_lowerCAmelCase		   =   attention_probs_dropout_prob
								_lowerCAmelCase		   =   max_position_embeddings
								_lowerCAmelCase		   =   eos_token_id
								_lowerCAmelCase		   =   pad_token_id
								_lowerCAmelCase		   =   bos_token_id
								_lowerCAmelCase		   =   initializer_range



		def   a   (  self   :					Optional[int]					):


								"""simple docstring"""



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

								_lowerCAmelCase		   =   shift_tokens_right(__lowerCAmelCase					,					1					,					2					)

								_lowerCAmelCase		   =   MarianConfig(
								    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					,					)
								_lowerCAmelCase		   =   prepare_marian_inputs_dict(__lowerCAmelCase					,					__lowerCAmelCase					,					__lowerCAmelCase					)
								return config, inputs_dict



		def   a   (  self   :					Dict					):


								"""simple docstring"""



								_lowerCAmelCase   ,  _lowerCAmelCase		   =   self.prepare_config_and_inputs()
								return config, inputs_dict



		def   a   (  self   :					List[str]					,					__lowerCAmelCase   :					str					,					__lowerCAmelCase   :					Optional[Any]					,					__lowerCAmelCase   :					Tuple					):


								"""simple docstring"""



								_lowerCAmelCase		   =   20
								_lowerCAmelCase		   =   model_class_name(__lowerCAmelCase					)

								_lowerCAmelCase		   =   model.encode(inputs_dict['input_ids']					)

								_lowerCAmelCase   ,  _lowerCAmelCase		   =   (
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								_lowerCAmelCase		   =   model.init_cache(decoder_input_ids.shape[0]					,					__lowerCAmelCase					,					__lowerCAmelCase					)
								_lowerCAmelCase		   =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,					dtype='i4'					)

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

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

								_lowerCAmelCase		   =   model.decode(__lowerCAmelCase					,					__lowerCAmelCase					)

								_lowerCAmelCase		   =   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   a   (  self   :					List[Any]					,					__lowerCAmelCase   :					List[str]					,					__lowerCAmelCase   :					Union[str, Any]					,					__lowerCAmelCase   :					Any					):


								"""simple docstring"""



								_lowerCAmelCase		   =   20
								_lowerCAmelCase		   =   model_class_name(__lowerCAmelCase					)

								_lowerCAmelCase		   =   model.encode(inputs_dict['input_ids']					)

								_lowerCAmelCase   ,  _lowerCAmelCase		   =   (
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

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

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

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

								_lowerCAmelCase		   =   model.decode(__lowerCAmelCase					,					__lowerCAmelCase					,					decoder_attention_mask=__lowerCAmelCase					)

								_lowerCAmelCase		   =   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        SCREAMING_SNAKE_CASE   (				__a	,						unittest.TestCase	,						__a ):
		"""simple docstring"""







		__A     =		True
		__A     =		(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
		__A     =		(FlaxMarianMTModel,) if is_flax_available() else ()



		def   a   (  self   :					Optional[int]					):


								"""simple docstring"""



								_lowerCAmelCase		   =   FlaxMarianModelTester(self					)



		def   a   (  self   :					str					):


								"""simple docstring"""



								_lowerCAmelCase   ,  _lowerCAmelCase		   =   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   a   (  self   :					Union[str, Any]					):


								"""simple docstring"""



								_lowerCAmelCase   ,  _lowerCAmelCase		   =   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   a   (  self   :					Optional[int]					):


								"""simple docstring"""



								_lowerCAmelCase   ,  _lowerCAmelCase		   =   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__					):
																				_lowerCAmelCase		   =   self._prepare_for_class(__lowerCAmelCase					,					__lowerCAmelCase					)
																				_lowerCAmelCase		   =   model_class(__lowerCAmelCase					)

																				@jax.jit
																				def encode_jitted(__lowerCAmelCase   :					List[str]					,					__lowerCAmelCase   :					List[Any]=None					,					**__lowerCAmelCase   :					Optional[int]					):
																										return model.encode(input_ids=__lowerCAmelCase					,					attention_mask=__lowerCAmelCase					)

																				with self.subTest('JIT Enabled'					):
																										_lowerCAmelCase		   =   encode_jitted(**__lowerCAmelCase					).to_tuple()

																				with self.subTest('JIT Disabled'					):
																										with jax.disable_jit():
																																_lowerCAmelCase		   =   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   a   (  self   :					Optional[Any]					):


								"""simple docstring"""



								_lowerCAmelCase   ,  _lowerCAmelCase		   =   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__					):
																				_lowerCAmelCase		   =   model_class(__lowerCAmelCase					)
																				_lowerCAmelCase		   =   model.encode(inputs_dict['input_ids']					,					inputs_dict['attention_mask']					)

																				_lowerCAmelCase		   =   {
																				    '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(__lowerCAmelCase   :					Dict					,					__lowerCAmelCase   :					List[Any]					,					__lowerCAmelCase   :					Union[str, Any]					):
																										return model.decode(
																										    decoder_input_ids=__lowerCAmelCase					,					decoder_attention_mask=__lowerCAmelCase					,					encoder_outputs=__lowerCAmelCase					,					)

																				with self.subTest('JIT Enabled'					):
																										_lowerCAmelCase		   =   decode_jitted(**__lowerCAmelCase					).to_tuple()

																				with self.subTest('JIT Disabled'					):
																										with jax.disable_jit():
																																_lowerCAmelCase		   =   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   a   (  self   :					Union[str, Any]					):


								"""simple docstring"""



								for model_class_name in self.all_model_classes:
														_lowerCAmelCase		   =   model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'					)
														# FlaxMarianForSequenceClassification expects eos token in input_ids
														_lowerCAmelCase		   =   np.ones((1, 1)					) * model.config.eos_token_id
														_lowerCAmelCase		   =   model(__lowerCAmelCase					)
														self.assertIsNotNone(__lowerCAmelCase					)





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				unittest.TestCase ):
		"""simple docstring"""







		__A     =		None
		__A     =		None



		@classmethod
		def   a   (  cls   :					Union[str, Any]					):


								"""simple docstring"""



								_lowerCAmelCase		   =   F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
								return cls



		@cached_property
		def   a   (  self   :					Dict					):


								"""simple docstring"""



								return MarianTokenizer.from_pretrained(self.model_name					)



		@property
		def   a   (  self   :					List[str]					):


								"""simple docstring"""



								return self.tokenizer.eos_token_id



		@cached_property
		def   a   (  self   :					Dict					):


								"""simple docstring"""



								_lowerCAmelCase		   =   FlaxMarianMTModel.from_pretrained(self.model_name					)
								self.assertEqual(model.config.decoder_start_token_id					,					model.config.pad_token_id					)
								return model



		def   a   (  self   :					Optional[int]					,					**__lowerCAmelCase   :					Tuple					):


								"""simple docstring"""



								_lowerCAmelCase		   =   self.translate_src_text(**__lowerCAmelCase					)
								self.assertListEqual(self.expected_text					,					__lowerCAmelCase					)



		def   a   (  self   :					int					,					**__lowerCAmelCase   :					int					):


								"""simple docstring"""



								_lowerCAmelCase		   =   self.tokenizer(self.src_text					,					padding=__lowerCAmelCase					,					return_tensors='np'					,					**__lowerCAmelCase					)
								_lowerCAmelCase		   =   self.model.generate(
								    model_inputs.input_ids					,					attention_mask=model_inputs.attention_mask					,					num_beams=2					,					max_length=128					,					).sequences
								_lowerCAmelCase		   =   self.tokenizer.batch_decode(__lowerCAmelCase					,					skip_special_tokens=__lowerCAmelCase					)
								return generated_words





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
		"""simple docstring"""







		__A     =		"en"
		__A     =		"fr"
		__A     =		[
		    "I am a small frog.",
		    "Now I can forget the 100 words of german that I know.",
		]
		__A     =		[
		    "Je suis une petite grenouille.",
		    "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
		]



		@slow
		def   a   (  self   :					int					):


								"""simple docstring"""



								self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
		"""simple docstring"""







		__A     =		"fr"
		__A     =		"en"
		__A     =		[
		    "Donnez moi le micro.",
		    "Tom et Mary étaient assis à une table.",  # Accents
		]
		__A     =		[
		    "Give me the microphone.",
		    "Tom and Mary were sitting at a table.",
		]



		@slow
		def   a   (  self   :					Optional[Any]					):


								"""simple docstring"""



								self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
		"""simple docstring"""







		__A     =		"mt"
		__A     =		"en"
		__A     =		["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
		__A     =		["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]



		@slow
		def   a   (  self   :					Dict					):


								"""simple docstring"""



								self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
		"""simple docstring"""







		__A     =		"en"
		__A     =		"de"
		__A     =		[
		    "I am a small frog.",
		    "Now I can forget the 100 words of german that I know.",
		    "Tom asked his teacher for advice.",
		    "That's how I would do it.",
		    "Tom really admired Mary's courage.",
		    "Turn around and close your eyes.",
		]
		__A     =		[
		    "Ich bin ein kleiner Frosch.",
		    "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
		    "Tom bat seinen Lehrer um Rat.",
		    "So würde ich das machen.",
		    "Tom bewunderte Marias Mut wirklich.",
		    "Drehen Sie sich um und schließen Sie die Augen.",
		]



		@slow
		def   a   (  self   :					Tuple					):


								"""simple docstring"""



								self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
		"""simple docstring"""







		__A     =		"en"
		__A     =		"zh"
		__A     =		["My name is Wolfgang and I live in Berlin"]
		__A     =		["我叫沃尔夫冈 我住在柏林"]



		@slow
		def   a   (  self   :					List[Any]					):


								"""simple docstring"""



								self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
		"""simple docstring"""







		__A     =		"ru"
		__A     =		"fr"
		__A     =		["Он показал мне рукопись своей новой пьесы."]
		__A     =		["Il m'a montré le manuscrit de sa nouvelle pièce."]



		@slow
		def   a   (  self   :					Union[str, Any]					):


								"""simple docstring"""



								self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE   (				__a ):
		"""simple docstring"""







		__A     =		"en"
		__A     =		"ROMANCE"
		__A     =		[
		    ">>fr<< Don't spend so much time watching TV.",
		    ">>pt<< Your message has been sent.",
		    ">>es<< He's two years older than me.",
		]
		__A     =		[
		    "Ne passez pas autant de temps à regarder la télé.",
		    "A sua mensagem foi enviada.",
		    "Es dos años más viejo que yo.",
		]



		@slow
		def   a   (  self   :					Optional[Any]					):


								"""simple docstring"""



								self._assert_generated_batch_equal_expected()






'''simple docstring'''




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







def 			A_   ( _lowerCamelCase			:							float = np.pi / 2  ,  _lowerCamelCase			:							float = np.pi / 2  ,  _lowerCamelCase			:							float = np.pi / 2    ):
      _lowerCAmelCase		   =   QuantumRegister(3  ,  'qr'    )  # Define the number of quantum bits
      _lowerCAmelCase		   =   ClassicalRegister(1  ,  'cr'    )  # Define the number of classical bits

      _lowerCAmelCase		   =   QuantumCircuit(_lowerCamelCase  ,  _lowerCamelCase    )  # Define the quantum circuit.

      # Build the circuit
      quantum_circuit.u(_lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase  ,  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		   =   Aer.get_backend('aer_simulator'    )
      _lowerCAmelCase		   =   execute(_lowerCamelCase  ,  _lowerCamelCase  ,  shots=1_000    )

      return job.result().get_counts(_lowerCamelCase    )


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





