






"""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_								=	'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						,			__UpperCamelCase						,			__UpperCamelCase=None						,			__UpperCamelCase=None						,			__UpperCamelCase=None						,			__UpperCamelCase=None						,			__UpperCamelCase=None						,			__UpperCamelCase=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    snake_case						:


   '''simple docstring'''

   def __init__(      self					:	Dict,			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	str=13,			_lowerCamelCase					:	Union[str, Any]=7,			_lowerCamelCase					:	List[Any]=True,			_lowerCamelCase					:	Tuple=False,			_lowerCamelCase					:	Optional[Any]=99,			_lowerCamelCase					:	Any=16,			_lowerCamelCase					:	Dict=2,			_lowerCamelCase					:	Optional[int]=4,			_lowerCamelCase					:	Tuple=4,			_lowerCamelCase					:	Dict="gelu",			_lowerCamelCase					:	Any=0.1,			_lowerCamelCase					:	List[Any]=0.1,			_lowerCamelCase					:	Optional[int]=32,			_lowerCamelCase					:	Tuple=2,			_lowerCamelCase					:	Union[str, Any]=1,			_lowerCamelCase					:	int=0,			_lowerCamelCase					:	str=0.02,			):

     '''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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[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 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     __A     ,       __A		     =					self.prepare_config_and_inputs()
     return config, inputs_dict


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	Union[str, Any]    ):

     '''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 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	int    ):

     '''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    snake_case						(				_lowerCAmelCase  ,      unittest.TestCase  ,      _lowerCAmelCase		):


   '''simple docstring'''

   A_		:      int							=					True
   A_		:      Optional[Any]							=					(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
   A_		:      List[Any]							=					(FlaxMarianMTModel,) if is_flax_available() else ()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					FlaxMarianModelTester(self    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''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 							_SCREAMING_SNAKE_CASE							(      self					:	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 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''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					:	Optional[Any],			_lowerCamelCase					:	List[Any]=None,			**_lowerCamelCase					:	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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[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					:	str,			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	Tuple    ):
           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 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''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    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   A_		:      Any							=					None
   A_		:      int							=					None


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	Tuple    ):

     '''simple docstring'''
     __A		     =					f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
     return cls


   @cached_property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     return MarianTokenizer.from_pretrained(self.model_name    )


   @property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     return self.tokenizer.eos_token_id


   @cached_property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''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 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			**_lowerCamelCase					:	int    ):

     '''simple docstring'''
     __A		     =					self.translate_src_text(**_lowerCamelCase    )
     self.assertListEqual(self.expected_text,			_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			**_lowerCamelCase					:	List[str]    ):

     '''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    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Tuple							=					"en"
   A_		:      Tuple							=					"fr"
   A_		:      int							=					[
       "I am a small frog.",
       "Now I can forget the 100 words of german that I know.",
   ]
   A_		:      Optional[Any]							=					[
       "Je suis une petite grenouille.",
       "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
   ]


   @slow
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      str							=					"fr"
   A_		:      Union[str, Any]							=					"en"
   A_		:      List[Any]							=					[
       "Donnez moi le micro.",
       "Tom et Mary étaient assis à une table.",  # Accents
   ]
   A_		:      Any							=					[
       "Give me the microphone.",
       "Tom and Mary were sitting at a table.",
   ]


   @slow
   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Dict							=					"mt"
   A_		:      Optional[int]							=					"en"
   A_		:      List[Any]							=					["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
   A_		:      Union[str, Any]							=					["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]


   @slow
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[Any]							=					"en"
   A_		:      Optional[int]							=					"de"
   A_		:      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.",
   ]
   A_		:      Union[str, 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 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Dict							=					"en"
   A_		:      Any							=					"zh"
   A_		:      List[Any]							=					["My name is Wolfgang and I live in Berlin"]
   A_		:      Dict							=					["我叫沃尔夫冈 我住在柏林"]


   @slow
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      List[Any]							=					"ru"
   A_		:      str							=					"fr"
   A_		:      int							=					["Он показал мне рукопись своей новой пьесы."]
   A_		:      Optional[Any]							=					["Il m'a montré le manuscrit de sa nouvelle pièce."]


   @slow
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      List[Any]							=					"en"
   A_		:      Tuple							=					"ROMANCE"
   A_		:      str							=					[
       ">>fr<< Don't spend so much time watching TV.",
       ">>pt<< Your message has been sent.",
       ">>es<< He's two years older than me.",
   ]
   A_		:      str							=					[
       "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					:	int    ):

     '''simple docstring'''
     self._assert_generated_batch_equal_expected()












"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


lowercase_								=	logging.get_logger(__name__)


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	Optional[int],			*_lowerCamelCase					:	int,			**_lowerCamelCase					:	str    ):

     '''simple docstring'''
     warnings.warn(
         '''The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
         ''' use ViltImageProcessor instead.''',			_lowerCamelCase,			)
     super().__init__(*_lowerCamelCase,			**_lowerCamelCase    )




