






"""simple docstring"""



class  _UpperCAmelCase	:




					def __init__(    self					:						Optional[Any]       )  ->  Tuple:
							lowercase_			:			Optional[int]								=       []
							lowercase_			:			List[Any]								=       0
							lowercase_			:			str								=       0




					def __str__(    self					:						List[Any]       )  ->  Optional[Any]:
							lowercase_			:			str								=       '''<''' + str(self.entries       )[1:-1] + '''>'''
							return printed




					def   A  (    self					:						Any   ,						A					:						List[str]       )  ->  str:
							self.entries.append(A       )
							lowercase_			:			Union[str, Any]								=       self.length + 1




					def   A  (    self					:						Tuple       )  ->  Tuple:
							lowercase_			:			List[str]								=       self.length - 1
							lowercase_			:			str								=       self.entries[self.front]
							# self.front-=1
							# self.entries = self.entries[self.front:]
							lowercase_			:			Optional[int]								=       self.entries[1:]
							return dequeued




					def   A  (    self					:						Union[str, Any]   ,						A					:						str       )  ->  List[Any]:
							for _ in range(A       ):
									self.put(self.get()       )




					def   A  (    self					:						List[str]       )  ->  int:
							return self.entries[0]




					def   A  (    self					:						Tuple       )  ->  Any:
							return self.length











"""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
						__A		:	Union[str, Any]      =		'''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 						lowercase   (    __snake_case    :			Union[str, Any]		, __snake_case    :			Optional[int]		, __snake_case    :			List[str]=None		, __snake_case    :			List[str]=None		, __snake_case    :			str=None		, __snake_case    :			List[str]=None		, __snake_case    :			Any=None		, __snake_case    :			List[Any]=None		, ):
		if attention_mask is None:
				lowercase_			:			int								=       np.where(input_ids != config.pad_token_id		, 1		, 0     )
		if decoder_attention_mask is None:
				lowercase_			:			Tuple								=       np.where(decoder_input_ids != config.pad_token_id		, 1		, 0     )
		if head_mask is None:
				lowercase_			:			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_			:			int								=       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  _UpperCAmelCase	:




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




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

							lowercase_			:			Optional[int]								=       shift_tokens_right(A   ,						1   ,						2       )

							lowercase_			:			Tuple								=       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=A   ,						)
							lowercase_			:			Tuple								=       prepare_marian_inputs_dict(A   ,						A   ,						A       )
							return config, inputs_dict




					def   A  (    self					:						Any       )  ->  List[Any]:
							lowercase_	, lowercase_			:			Dict								=       self.prepare_config_and_inputs()
							return config, inputs_dict




					def   A  (    self					:						int   ,						A					:						Any   ,						A					:						List[Any]   ,						A					:						Optional[Any]       )  ->  Union[str, Any]:
							lowercase_			:			Dict								=       20
							lowercase_			:			Optional[Any]								=       model_class_name(A       )

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

							lowercase_	, lowercase_			:			int								=       (
							    inputs_dict['''decoder_input_ids'''],
							    inputs_dict['''decoder_attention_mask'''],
							)

							lowercase_			:			Any								=       model.init_cache(decoder_input_ids.shape[0]   ,						A   ,						A       )
							lowercase_			:			int								=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,						dtype='''i4'''       )

							lowercase_			:			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_			:			str								=       model.decode(
							    decoder_input_ids[:, :-1]   ,						A   ,						decoder_attention_mask=A   ,						past_key_values=A   ,						decoder_position_ids=A   ,						)

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

							lowercase_			:			List[Any]								=       model.decode(A   ,						A       )

							lowercase_			:			Union[str, Any]								=       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					:						Optional[Any]   ,						A					:						Tuple   ,						A					:						Optional[Any]   ,						A					:						Any       )  ->  Tuple:
							lowercase_			:			List[str]								=       20
							lowercase_			:			List[str]								=       model_class_name(A       )

							lowercase_			:			Union[str, Any]								=       model.encode(inputs_dict['''input_ids''']       )

							lowercase_	, lowercase_			:			str								=       (
							    inputs_dict['''decoder_input_ids'''],
							    inputs_dict['''decoder_attention_mask'''],
							)

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

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

							lowercase_			:			int								=       model.decode(A   ,						A   ,						decoder_attention_mask=A       )

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




					def   A  (    self					:						Dict       )  ->  List[Any]:
							lowercase_			:			str								=       FlaxMarianModelTester(self       )




					def   A  (    self					:						Optional[int]       )  ->  List[Any]:
							lowercase_	, lowercase_			:			Tuple								=       self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(A   ,						A   ,						A       )




					def   A  (    self					:						List[Any]       )  ->  Dict:
							lowercase_	, lowercase_			:			List[str]								=       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(A   ,						A   ,						A       )




					def   A  (    self					:						Tuple       )  ->  int:
							lowercase_	, lowercase_			:			Dict								=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__       ):
											lowercase_			:			Dict								=       self._prepare_for_class(A   ,						A       )
											lowercase_			:			Tuple								=       model_class(A       )

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

											with self.subTest('''JIT Enabled'''       ):
													lowercase_			:			List[str]								=       encode_jitted(**A       ).to_tuple()

											with self.subTest('''JIT Disabled'''       ):
													with jax.disable_jit():
															lowercase_			:			int								=       encode_jitted(**A       ).to_tuple()

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




					def   A  (    self					:						Tuple       )  ->  Union[str, Any]:
							lowercase_	, lowercase_			:			List[str]								=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__       ):
											lowercase_			:			Dict								=       model_class(A       )
											lowercase_			:			Optional[int]								=       model.encode(inputs_dict['''input_ids''']   ,						inputs_dict['''attention_mask''']       )

											lowercase_			:			List[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(A					:						Tuple   ,						A					:						Tuple   ,						A					:						int       ):
													return model.decode(
													    decoder_input_ids=A   ,						decoder_attention_mask=A   ,						encoder_outputs=A   ,						)

											with self.subTest('''JIT Enabled'''       ):
													lowercase_			:			List[str]								=       decode_jitted(**A       ).to_tuple()

											with self.subTest('''JIT Disabled'''       ):
													with jax.disable_jit():
															lowercase_			:			str								=       decode_jitted(**A       ).to_tuple()

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






					@slow
					def   A  (    self					:						Optional[int]       )  ->  str:
							for model_class_name in self.all_model_classes:
									lowercase_			:			List[Any]								=       model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''       )
									# FlaxMarianForSequenceClassification expects eos token in input_ids
									lowercase_			:			Union[str, Any]								=       np.ones((1, 1)       ) * model.config.eos_token_id
									lowercase_			:			str								=       model(A       )
									self.assertIsNotNone(A       )





@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase	(			unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:    int							   =       None
					SCREAMING_SNAKE_CASE_		:    str							   =       None




					@classmethod
					def   A  (    cls					:						List[Any]       )  ->  None:
							lowercase_			:			List[Any]								=       F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
							return cls




					@cached_property
					def   A  (    self					:						List[str]       )  ->  Optional[int]:
							return MarianTokenizer.from_pretrained(self.model_name       )




					@property
					def   A  (    self					:						Dict       )  ->  int:
							return self.tokenizer.eos_token_id




					@cached_property
					def   A  (    self					:						Union[str, Any]       )  ->  str:
							lowercase_			:			FlaxMarianMTModel								=       FlaxMarianMTModel.from_pretrained(self.model_name       )
							self.assertEqual(model.config.decoder_start_token_id   ,						model.config.pad_token_id       )
							return model




					def   A  (    self					:						Tuple   ,						**A					:						Optional[Any]       )  ->  Dict:
							lowercase_			:			Dict								=       self.translate_src_text(**A       )
							self.assertListEqual(self.expected_text   ,						A       )






					def   A  (    self					:						List[str]   ,						**A					:						Union[str, Any]       )  ->  List[str]:
							lowercase_			:			Optional[Any]								=       self.tokenizer(self.src_text   ,						padding=A   ,						return_tensors='''np'''   ,						**A       )
							lowercase_			:			Union[str, Any]								=       self.model.generate(
							    model_inputs.input_ids   ,						attention_mask=model_inputs.attention_mask   ,						num_beams=2   ,						max_length=1_28   ,						).sequences
							lowercase_			:			Any								=       self.tokenizer.batch_decode(A   ,						skip_special_tokens=A       )
							return generated_words





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




					@slow
					def   A  (    self					:						str       )  ->  List[Any]:
							self._assert_generated_batch_equal_expected()





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




					@slow
					def   A  (    self					:						List[str]       )  ->  List[Any]:
							self._assert_generated_batch_equal_expected()





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




					@slow
					def   A  (    self					:						List[str]       )  ->  List[Any]:
							self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase	(			_A							):
					SCREAMING_SNAKE_CASE_		:    Optional[int]							   =       "en"
					SCREAMING_SNAKE_CASE_		:    Optional[Any]							   =       "de"
					SCREAMING_SNAKE_CASE_		:    int							   =       [
					    "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.",
					]
					SCREAMING_SNAKE_CASE_		:    Tuple							   =       [
					    "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]       )  ->  Union[str, Any]:
							self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase	(			_A							):
					SCREAMING_SNAKE_CASE_		:    Dict							   =       "en"
					SCREAMING_SNAKE_CASE_		:    Dict							   =       "zh"
					SCREAMING_SNAKE_CASE_		:    int							   =       ["My name is Wolfgang and I live in Berlin"]
					SCREAMING_SNAKE_CASE_		:    Any							   =       ["我叫沃尔夫冈 我住在柏林"]




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





@require_flax
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase	(			_A							):
					SCREAMING_SNAKE_CASE_		:    Tuple							   =       "ru"
					SCREAMING_SNAKE_CASE_		:    List[str]							   =       "fr"
					SCREAMING_SNAKE_CASE_		:    Dict							   =       ["Он показал мне рукопись своей новой пьесы."]
					SCREAMING_SNAKE_CASE_		:    int							   =       ["Il m'a montré le manuscrit de sa nouvelle pièce."]




					@slow
					def   A  (    self					:						Optional[Any]       )  ->  Optional[int]:
							self._assert_generated_batch_equal_expected()





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



