



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







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

									_a  :   List[Any]				 =							shift_tokens_right(_UpperCAmelCase			,1			,2	)

									_a  :   List[Any]				 =							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=_UpperCAmelCase			,)
									_a  :   Union[str, Any]				 =							prepare_marian_inputs_dict(_UpperCAmelCase			,_UpperCAmelCase			,_UpperCAmelCase	)
									return config, inputs_dict







							def 							__lowercase    (							self      : Tuple	):
									_a ,      _a  :   str				 =							self.prepare_config_and_inputs()
									return config, inputs_dict







							def 							__lowercase    (							self      : Tuple			,_UpperCAmelCase      : str			,_UpperCAmelCase      : str			,_UpperCAmelCase      : Union[str, Any]	):
									_a  :   str				 =							20
									_a  :   Dict				 =							model_class_name(_UpperCAmelCase	)

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

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

									_a  :   Optional[Any]				 =							model.init_cache(decoder_input_ids.shape[0]			,_UpperCAmelCase			,_UpperCAmelCase	)
									_a  :   str				 =							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,dtype='i4'	)

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

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

									_a  :   Tuple				 =							model.decode(_UpperCAmelCase			,_UpperCAmelCase	)

									_a  :   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 							__lowercase    (							self      : Any			,_UpperCAmelCase      : List[str]			,_UpperCAmelCase      : Optional[Any]			,_UpperCAmelCase      : Any	):
									_a  :   List[str]				 =							20
									_a  :   Tuple				 =							model_class_name(_UpperCAmelCase	)

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

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

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

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

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

									_a  :   Optional[int]				 =							model.decode(_UpperCAmelCase			,_UpperCAmelCase			,decoder_attention_mask=_UpperCAmelCase	)

									_a  :   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     ):
							lowerCAmelCase :					int            =      True
							lowerCAmelCase :					List[str]            =      (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
							lowerCAmelCase :					int            =      (FlaxMarianMTModel,) if is_flax_available() else ()







							def 							__lowercase    (							self      : Any	):
									_a  :   Optional[Any]				 =							FlaxMarianModelTester(self	)







							def 							__lowercase    (							self      : str	):
									_a ,      _a  :   str				 =							self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(_UpperCAmelCase			,_UpperCAmelCase			,_UpperCAmelCase	)







							def 							__lowercase    (							self      : Dict	):
									_a ,      _a  :   Dict				 =							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(_UpperCAmelCase			,_UpperCAmelCase			,_UpperCAmelCase	)







							def 							__lowercase    (							self      : Tuple	):
									_a ,      _a  :   Optional[Any]				 =							self.model_tester.prepare_config_and_inputs_for_common()

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

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

													with self.subTest('JIT Enabled'	):
															_a  :   Union[str, Any]				 =							encode_jitted(**_UpperCAmelCase	).to_tuple()

													with self.subTest('JIT Disabled'	):
															with jax.disable_jit():
																	_a  :   List[str]				 =							encode_jitted(**_UpperCAmelCase	).to_tuple()

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







							def 							__lowercase    (							self      : Dict	):
									_a ,      _a  :   Optional[int]				 =							self.model_tester.prepare_config_and_inputs_for_common()

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

													_a  :   Any				 =							{
													    '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(_UpperCAmelCase      : int			,_UpperCAmelCase      : int			,_UpperCAmelCase      : Optional[int]	):
															return model.decode(
															    decoder_input_ids=_UpperCAmelCase			,decoder_attention_mask=_UpperCAmelCase			,encoder_outputs=_UpperCAmelCase			,)

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

													with self.subTest('JIT Disabled'	):
															with jax.disable_jit():
																	_a  :   Optional[Any]				 =							decode_jitted(**_UpperCAmelCase	).to_tuple()

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







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

@require_flax
@require_sentencepiece
@require_tokenizers
class 			__magic_name__		(					unittest.TestCase     ):
							lowerCAmelCase :					int            =      None
							lowerCAmelCase :					Dict            =      None







							@classmethod
							def 							__lowercase    (							cls      : Union[str, Any]	):
									_a  :   Dict				 =							F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
									return cls







							@cached_property
							def 							__lowercase    (							self      : List[Any]	):
									return MarianTokenizer.from_pretrained(self.model_name	)







							@property
							def 							__lowercase    (							self      : str	):
									return self.tokenizer.eos_token_id







							@cached_property
							def 							__lowercase    (							self      : Any	):
									_a  :   FlaxMarianMTModel				 =							FlaxMarianMTModel.from_pretrained(self.model_name	)
									self.assertEqual(model.config.decoder_start_token_id			,model.config.pad_token_id	)
									return model







							def 							__lowercase    (							self      : str			,**_UpperCAmelCase      : Dict	):
									_a  :   List[str]				 =							self.translate_src_text(**_UpperCAmelCase	)
									self.assertListEqual(self.expected_text			,_UpperCAmelCase	)







							def 							__lowercase    (							self      : Dict			,**_UpperCAmelCase      : Dict	):
									_a  :   Optional[Any]				 =							self.tokenizer(self.src_text			,padding=_UpperCAmelCase			,return_tensors='np'			,**_UpperCAmelCase	)
									_a  :   Optional[Any]				 =							self.model.generate(
									    model_inputs.input_ids			,attention_mask=model_inputs.attention_mask			,num_beams=2			,max_length=128			,).sequences
									_a  :   int				 =							self.tokenizer.batch_decode(_UpperCAmelCase			,skip_special_tokens=_UpperCAmelCase	)
									return generated_words

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







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

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







							@slow
							def 							__lowercase    (							self      : str	):
									self._assert_generated_batch_equal_expected()

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







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

@require_flax
@require_sentencepiece
@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      'en'
							lowerCAmelCase :					Optional[Any]            =      'de'
							lowerCAmelCase :					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.',
							]
							lowerCAmelCase :					List[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 							__lowercase    (							self      : List[str]	):
									self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					Optional[Any]            =      'en'
							lowerCAmelCase :					Any            =      'zh'
							lowerCAmelCase :					List[str]            =      ['My name is Wolfgang and I live in Berlin']
							lowerCAmelCase :					int            =      ['我叫沃尔夫冈 我住在柏林']







							@slow
							def 							__lowercase    (							self      : str	):
									self._assert_generated_batch_equal_expected()

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







							@slow
							def 							__lowercase    (							self      : Union[str, Any]	):
									self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					str            =      'en'
							lowerCAmelCase :					Any            =      'ROMANCE'
							lowerCAmelCase :					Optional[int]            =      [
							    '>>fr<< Don\'t spend so much time watching TV.',
							    '>>pt<< Your message has been sent.',
							    '>>es<< He\'s two years older than me.',
							]
							lowerCAmelCase :					Optional[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 							__lowercase    (							self      : Any	):
									self._assert_generated_batch_equal_expected()









'''simple docstring'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




