'''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
						_lowercase		:					Optional[int]													=		"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 				lowerCamelCase      ( UpperCAmelCase__			:   List[str] , UpperCAmelCase__			:   str , UpperCAmelCase__			:   Tuple=None , UpperCAmelCase__			:   Any=None , UpperCAmelCase__			:   Union[str, Any]=None , UpperCAmelCase__			:   int=None , UpperCAmelCase__			:   Optional[Any]=None , UpperCAmelCase__			:   Optional[Any]=None , )     -> Any:
							if attention_mask is None:
														lowercase_   :     List[Any]									=			np.where(input_ids != config.pad_token_id , 1 , 0					)
							if decoder_attention_mask is None:
														lowercase_   :     Dict									=			np.where(decoder_input_ids != config.pad_token_id , 1 , 0					)
							if head_mask is None:
														lowercase_   :     Optional[Any]									=			np.ones((config.encoder_layers, config.encoder_attention_heads)					)
							if decoder_head_mask is None:
														lowercase_   :     Optional[Any]									=			np.ones((config.decoder_layers, config.decoder_attention_heads)					)
							if cross_attn_head_mask is None:
														lowercase_   :     Union[str, Any]									=			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							:    Dict							,     lowercase_							:    Tuple							,     lowercase_							:    Optional[int]=13							,     lowercase_							:    List[str]=7							,     lowercase_							:    Optional[Any]=True							,     lowercase_							:    str=False							,     lowercase_							:    str=99							,     lowercase_							:    Optional[int]=16							,     lowercase_							:    List[Any]=2							,     lowercase_							:    Union[str, Any]=4							,     lowercase_							:    str=4							,     lowercase_							:    int="gelu"							,     lowercase_							:    Optional[int]=0.1							,     lowercase_							:    Dict=0.1							,     lowercase_							:    Tuple=32							,     lowercase_							:    List[Any]=2							,     lowercase_							:    Any=1							,     lowercase_							:    List[str]=0							,     lowercase_							:    str=0.02							,     ):
										lowercase_   :     Tuple									=			parent
										lowercase_   :     List[Any]									=			batch_size
										lowercase_   :     Optional[int]									=			seq_length
										lowercase_   :     Any									=			is_training
										lowercase_   :     Dict									=			use_labels
										lowercase_   :     Any									=			vocab_size
										lowercase_   :     List[str]									=			hidden_size
										lowercase_   :     List[Any]									=			num_hidden_layers
										lowercase_   :     int									=			num_attention_heads
										lowercase_   :     List[str]									=			intermediate_size
										lowercase_   :     str									=			hidden_act
										lowercase_   :     Tuple									=			hidden_dropout_prob
										lowercase_   :     int									=			attention_probs_dropout_prob
										lowercase_   :     Optional[int]									=			max_position_embeddings
										lowercase_   :     List[str]									=			eos_token_id
										lowercase_   :     Optional[Any]									=			pad_token_id
										lowercase_   :     Optional[int]									=			bos_token_id
										lowercase_   :     Optional[int]									=			initializer_range







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

										lowercase_   :     Any									=			shift_tokens_right(lowercase_							,     1							,     2       )

										lowercase_   :     int									=			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_							,     )
										lowercase_   :     Union[str, Any]									=			prepare_marian_inputs_dict(lowercase_							,     lowercase_							,     lowercase_       )
										return config, inputs_dict







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										lowercase_		,		lowercase_   :     Tuple									=			self.prepare_config_and_inputs()
										return config, inputs_dict







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict							,     lowercase_							:    str							,     lowercase_							:    Optional[Any]							,     lowercase_							:    Tuple       ):
										lowercase_   :     Dict									=			20
										lowercase_   :     Optional[int]									=			model_class_name(lowercase_       )

										lowercase_   :     str									=			model.encode(inputs_dict["""input_ids"""]       )

										lowercase_		,		lowercase_   :     Any									=			(
										    inputs_dict["""decoder_input_ids"""],
										    inputs_dict["""decoder_attention_mask"""],
										)

										lowercase_   :     Dict									=			model.init_cache(decoder_input_ids.shape[0]							,     lowercase_							,     lowercase_       )
										lowercase_   :     Dict									=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,     dtype="""i4"""       )

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

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

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

										lowercase_   :     List[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       SCREAMING_SNAKE_CASE_	(      self							:    List[str]							,     lowercase_							:    str							,     lowercase_							:    Any							,     lowercase_							:    List[Any]       ):
										lowercase_   :     str									=			20
										lowercase_   :     int									=			model_class_name(lowercase_       )

										lowercase_   :     str									=			model.encode(inputs_dict["""input_ids"""]       )

										lowercase_		,		lowercase_   :     Any									=			(
										    inputs_dict["""decoder_input_ids"""],
										    inputs_dict["""decoder_attention_mask"""],
										)

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

										lowercase_   :     int									=			model.init_cache(decoder_input_ids.shape[0]							,     lowercase_							,     lowercase_       )
										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_   :     List[Any]									=			model.decode(
										    decoder_input_ids[:, :-1]							,     lowercase_							,     decoder_attention_mask=lowercase_							,     past_key_values=lowercase_							,     decoder_position_ids=lowercase_							,     )
										lowercase_   :     Any									=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,     dtype="""i4"""       )
										lowercase_   :     Dict									=			model.decode(
										    decoder_input_ids[:, -1:]							,     lowercase_							,     past_key_values=outputs_cache.past_key_values							,     decoder_attention_mask=lowercase_							,     decoder_position_ids=lowercase_							,     )

										lowercase_   :     str									=			model.decode(lowercase_							,     lowercase_							,     decoder_attention_mask=lowercase_       )

										lowercase_   :     Optional[int]									=			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__		(       _UpperCAmelCase,	unittest.TestCase,	_UpperCAmelCase):
			UpperCamelCase__							=			True
			UpperCamelCase__							=			(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
			UpperCamelCase__							=			(FlaxMarianMTModel,) if is_flax_available() else ()







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										lowercase_   :     List[Any]									=			FlaxMarianModelTester(self       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										lowercase_		,		lowercase_   :     Optional[int]									=			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       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										lowercase_		,		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(lowercase_							,     lowercase_							,     lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										lowercase_		,		lowercase_   :     Tuple									=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__       ):
																								lowercase_   :     Optional[int]									=			self._prepare_for_class(lowercase_							,     lowercase_       )
																								lowercase_   :     Optional[int]									=			model_class(lowercase_       )

																								@jax.jit
																								def encode_jitted(lowercase_							:    Tuple							,     lowercase_							:    Union[str, Any]=None							,     **lowercase_							:    Tuple       ):
																															return model.encode(input_ids=lowercase_							,     attention_mask=lowercase_       )

																								with self.subTest("""JIT Enabled"""       ):
																															lowercase_   :     Optional[int]									=			encode_jitted(**lowercase_       ).to_tuple()

																								with self.subTest("""JIT Disabled"""       ):
																															with jax.disable_jit():
																																						lowercase_   :     Dict									=			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       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										lowercase_		,		lowercase_   :     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_   :     str									=			model_class(lowercase_       )
																								lowercase_   :     str									=			model.encode(inputs_dict["""input_ids"""]							,     inputs_dict["""attention_mask"""]       )

																								lowercase_   :     str									=			{
																								    """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_							:    Optional[Any]							,     lowercase_							:    Optional[Any]							,     lowercase_							:    Any       ):
																															return model.decode(
																															    decoder_input_ids=lowercase_							,     decoder_attention_mask=lowercase_							,     encoder_outputs=lowercase_							,     )

																								with self.subTest("""JIT Enabled"""       ):
																															lowercase_   :     Any									=			decode_jitted(**lowercase_       ).to_tuple()

																								with self.subTest("""JIT Disabled"""       ):
																															with jax.disable_jit():
																																						lowercase_   :     Optional[Any]									=			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       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										for model_class_name in self.all_model_classes:
																	lowercase_   :     Optional[int]									=			model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""       )
																	# FlaxMarianForSequenceClassification expects eos token in input_ids
																	lowercase_   :     Any									=			np.ones((1, 1)       ) * model.config.eos_token_id
																	lowercase_   :     int									=			model(lowercase_       )
																	self.assertIsNotNone(lowercase_       )





@require_flax
@require_sentencepiece
@require_tokenizers
class        __magic_name__		(       unittest.TestCase):
			UpperCamelCase__							=			None
			UpperCamelCase__							=			None







			@classmethod
			def       SCREAMING_SNAKE_CASE_	(      cls							:    Optional[Any]       ):
										lowercase_   :     str									=			f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
										return cls







			@cached_property
			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										return MarianTokenizer.from_pretrained(self.model_name       )







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										return self.tokenizer.eos_token_id







			@cached_property
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										lowercase_   :     FlaxMarianMTModel									=			FlaxMarianMTModel.from_pretrained(self.model_name       )
										self.assertEqual(model.config.decoder_start_token_id							,     model.config.pad_token_id       )
										return model







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     **lowercase_							:    Union[str, Any]       ):
										lowercase_   :     Tuple									=			self.translate_src_text(**lowercase_       )
										self.assertListEqual(self.expected_text							,     lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     **lowercase_							:    List[str]       ):
										lowercase_   :     str									=			self.tokenizer(self.src_text							,     padding=lowercase_							,     return_tensors="""np"""							,     **lowercase_       )
										lowercase_   :     Tuple									=			self.model.generate(
										    model_inputs.input_ids							,     attention_mask=model_inputs.attention_mask							,     num_beams=2							,     max_length=128							,     ).sequences
										lowercase_   :     Any									=			self.tokenizer.batch_decode(lowercase_							,     skip_special_tokens=lowercase_       )
										return generated_words





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







			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										self._assert_generated_batch_equal_expected()





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







			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										self._assert_generated_batch_equal_expected()





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







			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			'''en'''
			UpperCamelCase__							=			'''de'''
			UpperCamelCase__							=			[
			    '''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__							=			[
			    '''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       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										self._assert_generated_batch_equal_expected()





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







			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										self._assert_generated_batch_equal_expected()





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







			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										self._assert_generated_batch_equal_expected()





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







			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										self._assert_generated_batch_equal_expected()






'''simple docstring'''




import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowercase		:					int													=		[(1,)]




def 				lowerCamelCase      ( )     -> Dict:
							lowercase_   :     Union[str, Any]									=			bool(os.getenv("""RUN_SLOW""" , 0					)					)
							if run_slow:
														assert True
							else:
														assert False, "should have been skipped"




def 				lowerCamelCase      ( )     -> List[str]:
							lowercase_   :     Union[str, Any]									=			bool(os.getenv("""RUN_SLOW""" , 0					)					)
							if run_slow and torch_device == "cuda":
														assert True
							else:
														assert False, "should have been skipped"





@require_torch
class        __magic_name__		(       unittest.TestCase):







			@slow
			@require_torch_gpu
			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										check_slow_torch_cuda()







			@require_torch_gpu
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										check_slow_torch_cuda()







			@parameterized.expand(lowercase_       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     lowercase_							:    Dict=None       ):
										check_slow()




@slow
@require_torch_gpu
def 				lowerCamelCase      ( )     -> Union[str, Any]:
							check_slow_torch_cuda()




@require_torch_gpu
@slow
def 				lowerCamelCase      ( )     -> Any:
							check_slow_torch_cuda()




@slow
@pytest.mark.parametrize("""param""" , [1]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   List[str]					)     -> List[Any]:
							check_slow()




@pytest.mark.parametrize("""param""" , [1]					)
@slow
def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[Any]					)     -> Any:
							check_slow()





