




'''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      :      Tuple   							=		'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 		snake_case_    (UpperCamelCase       :  List[str] ,						UpperCamelCase       :  Any ,						UpperCamelCase       :  Tuple=None ,						UpperCamelCase       :  Optional[int]=None ,						UpperCamelCase       :  Dict=None ,						UpperCamelCase       :  str=None ,						UpperCamelCase       :  Dict=None ,						UpperCamelCase       :  Dict=None ,						):





							'''simple docstring'''


							if attention_mask is None:
														_a	     =				np.where(input_ids != config.pad_token_id ,						1 ,						0      )
							if decoder_attention_mask is None:
														_a	     =				np.where(decoder_input_ids != config.pad_token_id ,						1 ,						0      )
							if head_mask is None:
														_a	     =				np.ones((config.encoder_layers, config.encoder_attention_heads)      )
							if decoder_head_mask is None:
														_a	     =				np.ones((config.decoder_layers, config.decoder_attention_heads)      )
							if cross_attn_head_mask is None:
														_a	     =				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       :  Optional[Any]							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Any=13							,			lowerCAmelCase_       :  Optional[int]=7							,			lowerCAmelCase_       :  Tuple=True							,			lowerCAmelCase_       :  Optional[int]=False							,			lowerCAmelCase_       :  List[str]=99							,			lowerCAmelCase_       :  List[Any]=16							,			lowerCAmelCase_       :  Optional[Any]=2							,			lowerCAmelCase_       :  Any=4							,			lowerCAmelCase_       :  str=4							,			lowerCAmelCase_       :  Any="gelu"							,			lowerCAmelCase_       :  Any=0.1							,			lowerCAmelCase_       :  Tuple=0.1							,			lowerCAmelCase_       :  Tuple=32							,			lowerCAmelCase_       :  Optional[Any]=2							,			lowerCAmelCase_       :  Tuple=1							,			lowerCAmelCase_       :  List[str]=0							,			lowerCAmelCase_       :  Dict=0.0_2							,			)			->							Dict:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  int					)			->							List[Any]:


														"""simple docstring"""


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

														_a	     =				shift_tokens_right(lowerCAmelCase_							,			1							,			2					)

														_a	     =				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_							,			)
														_a	     =				prepare_marian_inputs_dict(lowerCAmelCase_							,			lowerCAmelCase_							,			lowerCAmelCase_					)
														return config, inputs_dict







							def    __lowerCAmelCase			(  self       :  int					)			->							Any:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  Optional[int]							,			lowerCAmelCase_       :  List[str]							,			lowerCAmelCase_       :  Union[str, Any]							,			lowerCAmelCase_       :  Optional[int]					)			->							Tuple:


														"""simple docstring"""


														_a	     =				20
														_a	     =				model_class_name(lowerCAmelCase_					)

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

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

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

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

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

														_a	     =				model.decode(lowerCAmelCase_							,			lowerCAmelCase_					)

														_a	     =				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    __lowerCAmelCase			(  self       :  Union[str, Any]							,			lowerCAmelCase_       :  Dict							,			lowerCAmelCase_       :  List[Any]							,			lowerCAmelCase_       :  Optional[Any]					)			->							List[str]:


														"""simple docstring"""


														_a	     =				20
														_a	     =				model_class_name(lowerCAmelCase_					)

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

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

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

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

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

														_a	     =				model.decode(lowerCAmelCase_							,			lowerCAmelCase_							,			decoder_attention_mask=lowerCAmelCase_					)

														_a	     =				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    (		_a     ,unittest.TestCase     ,_a    ):
							lowercase_							=				True
							lowercase_							=				(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
							lowercase_							=				(FlaxMarianMTModel,) if is_flax_available() else ()







							def    __lowerCAmelCase			(  self       :  Optional[int]					)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				FlaxMarianModelTester(self					)







							def    __lowerCAmelCase			(  self       :  Union[str, Any]					)			->							Dict:


														"""simple docstring"""


														_a     ,							_a	     =				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    __lowerCAmelCase			(  self       :  Optional[Any]					)			->							Optional[Any]:


														"""simple docstring"""


														_a     ,							_a	     =				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    __lowerCAmelCase			(  self       :  int					)			->							Union[str, Any]:


														"""simple docstring"""


														_a     ,							_a	     =				self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__					):
																												_a	     =				self._prepare_for_class(lowerCAmelCase_							,			lowerCAmelCase_					)
																												_a	     =				model_class(lowerCAmelCase_					)

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

																												with self.subTest('''JIT Enabled'''					):
																																			_a	     =				encode_jitted(**lowerCAmelCase_					).to_tuple()

																												with self.subTest('''JIT Disabled'''					):
																																			with jax.disable_jit():
																																										_a	     =				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    __lowerCAmelCase			(  self       :  List[Any]					)			->							List[Any]:


														"""simple docstring"""


														_a     ,							_a	     =				self.model_tester.prepare_config_and_inputs_for_common()

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

																												_a	     =				{
																												    '''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_       :  str							,			lowerCAmelCase_       :  Optional[Any]					):
																																			return model.decode(
																																			    decoder_input_ids=lowerCAmelCase_							,			decoder_attention_mask=lowerCAmelCase_							,			encoder_outputs=lowerCAmelCase_							,			)

																												with self.subTest('''JIT Enabled'''					):
																																			_a	     =				decode_jitted(**lowerCAmelCase_					).to_tuple()

																												with self.subTest('''JIT Disabled'''					):
																																			with jax.disable_jit():
																																										_a	     =				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    __lowerCAmelCase			(  self       :  str					)			->							Union[str, Any]:


														"""simple docstring"""


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



@require_flax
@require_sentencepiece
@require_tokenizers
class 			A    (		unittest.TestCase    ):
							lowercase_							=				None
							lowercase_							=				None







							@classmethod
							def    __lowerCAmelCase			(  cls       :  int					)			->							None:


														"""simple docstring"""


														_a	     =				F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
														return cls







							@cached_property
							def    __lowerCAmelCase			(  self       :  Tuple					)			->							Union[str, Any]:


														"""simple docstring"""


														return MarianTokenizer.from_pretrained(self.model_name					)







							@property
							def    __lowerCAmelCase			(  self       :  Optional[int]					)			->							int:


														"""simple docstring"""


														return self.tokenizer.eos_token_id







							@cached_property
							def    __lowerCAmelCase			(  self       :  str					)			->							Any:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  Optional[Any]							,			**lowerCAmelCase_       :  str					)			->							int:


														"""simple docstring"""


														_a	     =				self.translate_src_text(**lowerCAmelCase_					)
														self.assertListEqual(self.expected_text							,			lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Tuple							,			**lowerCAmelCase_       :  str					)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				self.tokenizer(self.src_text							,			padding=lowerCAmelCase_							,			return_tensors='''np'''							,			**lowerCAmelCase_					)
														_a	     =				self.model.generate(
														    model_inputs.input_ids							,			attention_mask=model_inputs.attention_mask							,			num_beams=2							,			max_length=1_28							,			).sequences
														_a	     =				self.tokenizer.batch_decode(lowerCAmelCase_							,			skip_special_tokens=lowerCAmelCase_					)
														return generated_words



@require_flax
@require_sentencepiece
@require_tokenizers
class 			A    (		_a    ):
							lowercase_							=				'en'
							lowercase_							=				'fr'
							lowercase_							=				[
							    'I am a small frog.',
							    'Now I can forget the 100 words of german that I know.',
							]
							lowercase_							=				[
							    'Je suis une petite grenouille.',
							    'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
							]







							@slow
							def    __lowerCAmelCase			(  self       :  Dict					)			->							Any:


														"""simple docstring"""


														self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 			A    (		_a    ):
							lowercase_							=				'fr'
							lowercase_							=				'en'
							lowercase_							=				[
							    'Donnez moi le micro.',
							    'Tom et Mary étaient assis à une table.',  # Accents
							]
							lowercase_							=				[
							    'Give me the microphone.',
							    'Tom and Mary were sitting at a table.',
							]







							@slow
							def    __lowerCAmelCase			(  self       :  Dict					)			->							Optional[int]:


														"""simple docstring"""


														self._assert_generated_batch_equal_expected()



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







							@slow
							def    __lowerCAmelCase			(  self       :  int					)			->							int:


														"""simple docstring"""


														self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 			A    (		_a    ):
							lowercase_							=				'en'
							lowercase_							=				'de'
							lowercase_							=				[
							    '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.',
							]
							lowercase_							=				[
							    '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    __lowerCAmelCase			(  self       :  str					)			->							List[Any]:


														"""simple docstring"""


														self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 			A    (		_a    ):
							lowercase_							=				'en'
							lowercase_							=				'zh'
							lowercase_							=				['My name is Wolfgang and I live in Berlin']
							lowercase_							=				['我叫沃尔夫冈 我住在柏林']







							@slow
							def    __lowerCAmelCase			(  self       :  List[Any]					)			->							Union[str, Any]:


														"""simple docstring"""


														self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 			A    (		_a    ):
							lowercase_							=				'ru'
							lowercase_							=				'fr'
							lowercase_							=				['Он показал мне рукопись своей новой пьесы.']
							lowercase_							=				['Il m\'a montré le manuscrit de sa nouvelle pièce.']







							@slow
							def    __lowerCAmelCase			(  self       :  List[str]					)			->							Dict:


														"""simple docstring"""


														self._assert_generated_batch_equal_expected()



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







							@slow
							def    __lowerCAmelCase			(  self       :  Dict					)			->							Optional[int]:


														"""simple docstring"""


														self._assert_generated_batch_equal_expected()







'''simple docstring'''


from dataclasses import dataclass
from typing import Callable, Optional

import datasets

@dataclass
class 			A    (		datasets.BuilderConfig    ):
							lowercase_							=				None
							lowercase_							=				None
							lowercase_							=				None







							def    __lowerCAmelCase			(  self       :  Dict					)			->							List[str]:


														"""simple docstring"""


														assert self.generator is not None, "generator must be specified"

														if self.gen_kwargs is None:
																					_a	     =				{}



class 			A    (		datasets.GeneratorBasedBuilder    ):
							lowercase_							=				GeneratorConfig







							def    __lowerCAmelCase			(  self       :  Any					)			->							List[str]:


														"""simple docstring"""


														return datasets.DatasetInfo(features=self.config.features					)







							def    __lowerCAmelCase			(  self       :  Dict							,			lowerCAmelCase_       :  Optional[int]					)			->							Optional[Any]:


														"""simple docstring"""


														return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,			gen_kwargs=self.config.gen_kwargs					)]







							def    __lowerCAmelCase			(  self       :  str							,			**lowerCAmelCase_       :  int					)			->							List[str]:


														"""simple docstring"""


														for idx, ex in enumerate(self.config.generator(**lowerCAmelCase_					)					):
																					yield idx, ex

