




def 							_UpperCAmelCase     (UpperCamelCase_       :       int				):
				'''simple docstring'''
				_lowerCAmelCase			:			Tuple        =					[True for i in range(n + 1				)]
				_lowerCAmelCase			:			Optional[int]        =					[i - 1 for i in range(n + 1				)]
				_lowerCAmelCase			:			List[str]        =					[]
				for i in range(2					,			n + 1				):
								if is_prime[i]:
												primes.append(UpperCamelCase_				)
								for j in range(0					,			len(UpperCamelCase_				)				):
												if i * primes[j] >= n:
																break
												_lowerCAmelCase			:			Optional[int]        =					False

												if i % primes[j] == 0:
																_lowerCAmelCase			:			Union[str, Any]        =					totients[i] * primes[j]
																break

												_lowerCAmelCase			:			Tuple        =					totients[i] * (primes[j] - 1)

				return totients


if __name__ == "__main__":
	import doctest

	doctest.testmod()






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
							UpperCamelCase_	       =   "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			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase=None							,					__UpperCAmelCase=None							,					__UpperCAmelCase=None							,					__UpperCAmelCase=None							,					__UpperCAmelCase=None							,					__UpperCAmelCase=None							,					)       ->       Optional[int]:



						'''simple docstring'''






						if attention_mask is None:
												UpperCAmelCase_     							=     np.where(input_ids != config.pad_token_id							,					1							,					0	)
						if decoder_attention_mask is None:
												UpperCAmelCase_     							=     np.where(decoder_input_ids != config.pad_token_id							,					1							,					0	)
						if head_mask is None:
												UpperCAmelCase_     							=     np.ones((config.encoder_layers, config.encoder_attention_heads)	)
						if decoder_head_mask is None:
												UpperCAmelCase_     							=     np.ones((config.decoder_layers, config.decoder_attention_heads)	)
						if cross_attn_head_mask is None:
												UpperCAmelCase_     							=     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 							a_  :

						def __init__(    self	:int   ,						_lowercase	:List[str]   ,						_lowercase	:Optional[Any]=13   ,						_lowercase	:Union[str, Any]=7   ,						_lowercase	:Any=True   ,						_lowercase	:List[Any]=False   ,						_lowercase	:Any=99   ,						_lowercase	:List[Any]=16   ,						_lowercase	:List[str]=2   ,						_lowercase	:List[str]=4   ,						_lowercase	:Any=4   ,						_lowercase	:Tuple="gelu"   ,						_lowercase	:Optional[int]=0.1   ,						_lowercase	:Dict=0.1   ,						_lowercase	:Union[str, Any]=32   ,						_lowercase	:Union[str, Any]=2   ,						_lowercase	:str=1   ,						_lowercase	:str=0   ,						_lowercase	:Dict=0.02   ,						)		->					int:
												UpperCAmelCase_     							=     parent
												UpperCAmelCase_     							=     batch_size
												UpperCAmelCase_     							=     seq_length
												UpperCAmelCase_     							=     is_training
												UpperCAmelCase_     							=     use_labels
												UpperCAmelCase_     							=     vocab_size
												UpperCAmelCase_     							=     hidden_size
												UpperCAmelCase_     							=     num_hidden_layers
												UpperCAmelCase_     							=     num_attention_heads
												UpperCAmelCase_     							=     intermediate_size
												UpperCAmelCase_     							=     hidden_act
												UpperCAmelCase_     							=     hidden_dropout_prob
												UpperCAmelCase_     							=     attention_probs_dropout_prob
												UpperCAmelCase_     							=     max_position_embeddings
												UpperCAmelCase_     							=     eos_token_id
												UpperCAmelCase_     							=     pad_token_id
												UpperCAmelCase_     							=     bos_token_id
												UpperCAmelCase_     							=     initializer_range

						def 	__a       (    self	:Optional[Any])		->					int:
												UpperCAmelCase_     							=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,						self.vocab_size)   ,						3   ,						self.vocab_size)
												UpperCAmelCase_     							=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,						dtype=np.intaa))   ,						-1)

												UpperCAmelCase_     							=     shift_tokens_right(_lowercase   ,						1   ,						2)

												UpperCAmelCase_     							=     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=_lowercase   ,						)
												UpperCAmelCase_     							=     prepare_marian_inputs_dict(_lowercase   ,						_lowercase   ,						_lowercase)
												return config, inputs_dict

						def 	__a       (    self	:List[str])		->					Optional[Any]:
												UpperCAmelCase_							,      UpperCAmelCase_     							=     self.prepare_config_and_inputs()
												return config, inputs_dict

						def 	__a       (    self	:Optional[Any]   ,						_lowercase	:Any   ,						_lowercase	:Any   ,						_lowercase	:List[str])		->					List[Any]:
												UpperCAmelCase_     							=     20
												UpperCAmelCase_     							=     model_class_name(_lowercase)

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

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

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

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

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

												UpperCAmelCase_     							=     model.decode(_lowercase   ,						_lowercase)

												UpperCAmelCase_     							=     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	:str   ,						_lowercase	:Union[str, Any]   ,						_lowercase	:List[Any]   ,						_lowercase	:Dict)		->					List[Any]:
												UpperCAmelCase_     							=     20
												UpperCAmelCase_     							=     model_class_name(_lowercase)

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

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

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

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

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

												UpperCAmelCase_     							=     model.decode(_lowercase   ,						_lowercase   ,						decoder_attention_mask=_lowercase)

												UpperCAmelCase_     							=     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 							a_  (		_snake_case					, unittest.TestCase					, _snake_case				):
						UpperCamelCase__      :						Optional[Any]				       =True
						UpperCamelCase__      :						str				       =(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
						UpperCamelCase__      :						Any				       =(FlaxMarianMTModel,) if is_flax_available() else ()

						def 	__a       (    self	:List[Any])		->					Tuple:
												UpperCAmelCase_     							=     FlaxMarianModelTester(self)

						def 	__a       (    self	:Tuple)		->					Optional[Any]:
												UpperCAmelCase_							,      UpperCAmelCase_     							=     self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(_lowercase   ,						_lowercase   ,						_lowercase)

						def 	__a       (    self	:Tuple)		->					str:
												UpperCAmelCase_							,      UpperCAmelCase_     							=     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(_lowercase   ,						_lowercase   ,						_lowercase)

						def 	__a       (    self	:Dict)		->					str:
												UpperCAmelCase_							,      UpperCAmelCase_     							=     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__):
																								UpperCAmelCase_     							=     self._prepare_for_class(_lowercase   ,						_lowercase)
																								UpperCAmelCase_     							=     model_class(_lowercase)

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

																								with self.subTest('''JIT Enabled'''):
																														UpperCAmelCase_     							=     encode_jitted(**_lowercase).to_tuple()

																								with self.subTest('''JIT Disabled'''):
																														with jax.disable_jit():
																																				UpperCAmelCase_     							=     encode_jitted(**_lowercase).to_tuple()

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

						def 	__a       (    self	:Optional[int])		->					str:
												UpperCAmelCase_							,      UpperCAmelCase_     							=     self.model_tester.prepare_config_and_inputs_for_common()

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

																								UpperCAmelCase_     							=     {
																								    '''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(_lowercase	:Dict   ,						_lowercase	:Optional[Any]   ,						_lowercase	:int):
																														return model.decode(
																														    decoder_input_ids=_lowercase   ,						decoder_attention_mask=_lowercase   ,						encoder_outputs=_lowercase   ,						)

																								with self.subTest('''JIT Enabled'''):
																														UpperCAmelCase_     							=     decode_jitted(**_lowercase).to_tuple()

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

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




						@slow
						def 	__a       (    self	:int)		->					Any:
												for model_class_name in self.all_model_classes:
																		UpperCAmelCase_     							=     model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de''')
																		# FlaxMarianForSequenceClassification expects eos token in input_ids
																		UpperCAmelCase_     							=     np.ones((1, 1)) * model.config.eos_token_id
																		UpperCAmelCase_     							=     model(_lowercase)
																		self.assertIsNotNone(_lowercase)







@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_  (		unittest.TestCase				):
						UpperCamelCase__      :						str				       =None
						UpperCamelCase__      :						List[str]				       =None

						@classmethod
						def 	__a       (    cls	:Optional[int])		->					None:
												UpperCAmelCase_     							=     f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
												return cls

						@cached_property
						def 	__a       (    self	:str)		->					str:
												return MarianTokenizer.from_pretrained(self.model_name)

						@property
						def 	__a       (    self	:Optional[int])		->					int:
												return self.tokenizer.eos_token_id

						@cached_property
						def 	__a       (    self	:Dict)		->					Optional[int]:
												UpperCAmelCase_     							=     FlaxMarianMTModel.from_pretrained(self.model_name)
												self.assertEqual(model.config.decoder_start_token_id   ,						model.config.pad_token_id)
												return model

						def 	__a       (    self	:List[Any]   ,						**_lowercase	:Any)		->					Union[str, Any]:
												UpperCAmelCase_     							=     self.translate_src_text(**_lowercase)
												self.assertListEqual(self.expected_text   ,						_lowercase)




						def 	__a       (    self	:List[str]   ,						**_lowercase	:List[str])		->					Any:
												UpperCAmelCase_     							=     self.tokenizer(self.src_text   ,						padding=_lowercase   ,						return_tensors='''np'''   ,						**_lowercase)
												UpperCAmelCase_     							=     self.model.generate(
												    model_inputs.input_ids   ,						attention_mask=model_inputs.attention_mask   ,						num_beams=2   ,						max_length=128   ,						).sequences
												UpperCAmelCase_     							=     self.tokenizer.batch_decode(_lowercase   ,						skip_special_tokens=_lowercase)
												return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_  (		_snake_case				):
						UpperCamelCase__      :						str				       ="en"
						UpperCamelCase__      :						List[str]				       ="fr"
						UpperCamelCase__      :						Dict				       =[
						    "I am a small frog.",
						    "Now I can forget the 100 words of german that I know.",
						]
						UpperCamelCase__      :						Optional[Any]				       =[
						    "Je suis une petite grenouille.",
						    "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
						]

						@slow
						def 	__a       (    self	:List[Any])		->					Dict:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_  (		_snake_case				):
						UpperCamelCase__      :						Tuple				       ="fr"
						UpperCamelCase__      :						Tuple				       ="en"
						UpperCamelCase__      :						Union[str, Any]				       =[
						    "Donnez moi le micro.",
						    "Tom et Mary étaient assis à une table.",  # Accents
						]
						UpperCamelCase__      :						Tuple				       =[
						    "Give me the microphone.",
						    "Tom and Mary were sitting at a table.",
						]

						@slow
						def 	__a       (    self	:Union[str, Any])		->					int:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_  (		_snake_case				):
						UpperCamelCase__      :						List[str]				       ="mt"
						UpperCamelCase__      :						List[Any]				       ="en"
						UpperCamelCase__      :						List[Any]				       =["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
						UpperCamelCase__      :						int				       =["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]

						@slow
						def 	__a       (    self	:Union[str, Any])		->					Union[str, Any]:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_  (		_snake_case				):
						UpperCamelCase__      :						str				       ="en"
						UpperCamelCase__      :						List[str]				       ="de"
						UpperCamelCase__      :						List[str]				       =[
						    "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.",
						]
						UpperCamelCase__      :						Any				       =[
						    "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	:List[Any])		->					Dict:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_  (		_snake_case				):
						UpperCamelCase__      :						List[Any]				       ="en"
						UpperCamelCase__      :						Union[str, Any]				       ="zh"
						UpperCamelCase__      :						Optional[Any]				       =["My name is Wolfgang and I live in Berlin"]
						UpperCamelCase__      :						List[Any]				       =["我叫沃尔夫冈 我住在柏林"]

						@slow
						def 	__a       (    self	:Tuple)		->					Optional[Any]:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_  (		_snake_case				):
						UpperCamelCase__      :						Dict				       ="ru"
						UpperCamelCase__      :						Optional[int]				       ="fr"
						UpperCamelCase__      :						Optional[Any]				       =["Он показал мне рукопись своей новой пьесы."]
						UpperCamelCase__      :						int				       =["Il m'a montré le manuscrit de sa nouvelle pièce."]

						@slow
						def 	__a       (    self	:Optional[Any])		->					Union[str, Any]:
												self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_  (		_snake_case				):
						UpperCamelCase__      :						Dict				       ="en"
						UpperCamelCase__      :						Optional[Any]				       ="ROMANCE"
						UpperCamelCase__      :						int				       =[
						    ">>fr<< Don't spend so much time watching TV.",
						    ">>pt<< Your message has been sent.",
						    ">>es<< He's two years older than me.",
						]
						UpperCamelCase__      :						Tuple				       =[
						    "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	:List[str])		->					List[str]:
												self._assert_generated_batch_equal_expected()





