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




    """simple docstring"""



    if attention_mask is None:
        __UpperCamelCase						:						List[str]		     =	np.where(input_ids != config.pad_token_id					,  1					,  0      )
    if decoder_attention_mask is None:
        __UpperCamelCase						:						Tuple		     =	np.where(decoder_input_ids != config.pad_token_id					,  1					,  0      )
    if head_mask is None:
        __UpperCamelCase						:						List[str]		     =	np.ones((config.encoder_layers, config.encoder_attention_heads)      )
    if decoder_head_mask is None:
        __UpperCamelCase						:						Optional[int]		     =	np.ones((config.decoder_layers, config.decoder_attention_heads)      )
    if cross_attn_head_mask is None:
        __UpperCamelCase						:						str		     =	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       __lowercase		:







     """simple docstring"""






     def __init__(		self  ,      _lowerCamelCase  ,      _lowerCamelCase=1_3  ,      _lowerCamelCase=7  ,      _lowerCamelCase=True  ,      _lowerCamelCase=False  ,      _lowerCamelCase=9_9  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase=2  ,      _lowerCamelCase=4  ,      _lowerCamelCase=4  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=3_2  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=0.0_2  ,      ):
         __UpperCamelCase						:						List[Any]		     =	parent
         __UpperCamelCase						:						Optional[Any]		     =	batch_size
         __UpperCamelCase						:						Optional[int]		     =	seq_length
         __UpperCamelCase						:						Optional[int]		     =	is_training
         __UpperCamelCase						:						str		     =	use_labels
         __UpperCamelCase						:						Any		     =	vocab_size
         __UpperCamelCase						:						List[Any]		     =	hidden_size
         __UpperCamelCase						:						Tuple		     =	num_hidden_layers
         __UpperCamelCase						:						Dict		     =	num_attention_heads
         __UpperCamelCase						:						Optional[Any]		     =	intermediate_size
         __UpperCamelCase						:						str		     =	hidden_act
         __UpperCamelCase						:						Optional[int]		     =	hidden_dropout_prob
         __UpperCamelCase						:						List[str]		     =	attention_probs_dropout_prob
         __UpperCamelCase						:						List[Any]		     =	max_position_embeddings
         __UpperCamelCase						:						List[str]		     =	eos_token_id
         __UpperCamelCase						:						List[Any]		     =	pad_token_id
         __UpperCamelCase						:						Any		     =	bos_token_id
         __UpperCamelCase						:						List[Any]		     =	initializer_range







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

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

         __UpperCamelCase						:						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=lowercase_  ,      )
         __UpperCamelCase						:						str		     =	prepare_marian_inputs_dict(lowercase_  ,      lowercase_  ,      lowercase_     )
         return config, inputs_dict







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						str		     =	self.prepare_config_and_inputs()
         return config, inputs_dict







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Optional[Any]		     =	2_0
         __UpperCamelCase						:						Dict		     =	model_class_name(lowercase_     )

         __UpperCamelCase						:						Optional[Any]		     =	model.encode(inputs_dict['input_ids']     )

         __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )

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

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

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

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

         __UpperCamelCase						:						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}"""     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Optional[Any]		     =	2_0
         __UpperCamelCase						:						int		     =	model_class_name(lowercase_     )

         __UpperCamelCase						:						List[Any]		     =	model.encode(inputs_dict['input_ids']     )

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

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

         __UpperCamelCase						:						Optional[int]		     =	model.init_cache(decoder_input_ids.shape[0]  ,      lowercase_  ,      lowercase_     )
         __UpperCamelCase						:						int		     =	jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]  ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,      )

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

         __UpperCamelCase						:						Any		     =	model.decode(lowercase_  ,      lowercase_  ,      decoder_attention_mask=lowercase_     )

         __UpperCamelCase						:						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}"""     )






@require_flax
class       __lowercase		(    SCREAMING_SNAKE_CASE_      ,				unittest.TestCase      ,				SCREAMING_SNAKE_CASE_    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    True
     SCREAMING_SNAKE_CASE__		 =    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     SCREAMING_SNAKE_CASE__		 =    (FlaxMarianMTModel,) if is_flax_available() else ()







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Union[str, Any]		     =	FlaxMarianModelTester(self     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						str		     =	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 lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						List[Any]		     =	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 lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Dict		     =	self.model_tester.prepare_config_and_inputs_for_common()

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

                 @jax.jit
                 def encode_jitted(_lowerCamelCase  ,      _lowerCamelCase=None  ,      **_lowerCamelCase     ):
                     return model.encode(input_ids=lowercase_  ,      attention_mask=lowercase_     )

                 with self.subTest('JIT Enabled'     ):
                     __UpperCamelCase						:						Tuple		     =	encode_jitted(**lowercase_     ).to_tuple()

                 with self.subTest('JIT Disabled'     ):
                     with jax.disable_jit():
                         __UpperCamelCase						:						Any		     =	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 lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						str		     =	self.model_tester.prepare_config_and_inputs_for_common()

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

                 __UpperCamelCase						:						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(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
                     return model.decode(
                         decoder_input_ids=lowercase_  ,      decoder_attention_mask=lowercase_  ,      encoder_outputs=lowercase_  ,      )

                 with self.subTest('JIT Enabled'     ):
                     __UpperCamelCase						:						Union[str, Any]		     =	decode_jitted(**lowercase_     ).to_tuple()

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






@require_flax
@require_sentencepiece
@require_tokenizers
class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    None
     SCREAMING_SNAKE_CASE__		 =    None







     @classmethod
     def lowerCAmelCase  (		cls     ):
         __UpperCamelCase						:						int		     =	f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
         return cls







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







     @property
     def lowerCAmelCase  (		self     ):
         return self.tokenizer.eos_token_id







     @cached_property
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						str		     =	FlaxMarianMTModel.from_pretrained(self.model_name     )
         self.assertEqual(model.config.decoder_start_token_id  ,      model.config.pad_token_id     )
         return model







     def lowerCAmelCase  (		self  ,      **_lowerCamelCase     ):
         __UpperCamelCase						:						List[str]		     =	self.translate_src_text(**lowercase_     )
         self.assertListEqual(self.expected_text  ,      lowercase_     )







     def lowerCAmelCase  (		self  ,      **_lowerCamelCase     ):
         __UpperCamelCase						:						Optional[int]		     =	self.tokenizer(self.src_text  ,      padding=lowercase_  ,      return_tensors='np'  ,      **lowercase_     )
         __UpperCamelCase						:						Optional[int]		     =	self.model.generate(
             model_inputs.input_ids  ,      attention_mask=model_inputs.attention_mask  ,      num_beams=2  ,      max_length=1_2_8  ,      ).sequences
         __UpperCamelCase						:						str		     =	self.tokenizer.batch_decode(lowercase_  ,      skip_special_tokens=lowercase_     )
         return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class       __lowercase		(    SCREAMING_SNAKE_CASE_    ):







     """simple docstring"""






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







     @slow
     def lowerCAmelCase  (		self     ):
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       __lowercase		(    SCREAMING_SNAKE_CASE_    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    '''fr'''
     SCREAMING_SNAKE_CASE__		 =    '''en'''
     SCREAMING_SNAKE_CASE__		 =    [
         '''Donnez moi le micro.''',
         '''Tom et Mary étaient assis à une table.''',  # Accents
     ]
     SCREAMING_SNAKE_CASE__		 =    [
         '''Give me the microphone.''',
         '''Tom and Mary were sitting at a table.''',
     ]







     @slow
     def lowerCAmelCase  (		self     ):
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       __lowercase		(    SCREAMING_SNAKE_CASE_    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    '''mt'''
     SCREAMING_SNAKE_CASE__		 =    '''en'''
     SCREAMING_SNAKE_CASE__		 =    ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
     SCREAMING_SNAKE_CASE__		 =    ['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']







     @slow
     def lowerCAmelCase  (		self     ):
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       __lowercase		(    SCREAMING_SNAKE_CASE_    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    '''en'''
     SCREAMING_SNAKE_CASE__		 =    '''de'''
     SCREAMING_SNAKE_CASE__		 =    [
         '''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__		 =    [
         '''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     ):
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       __lowercase		(    SCREAMING_SNAKE_CASE_    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    '''en'''
     SCREAMING_SNAKE_CASE__		 =    '''zh'''
     SCREAMING_SNAKE_CASE__		 =    ['''My name is Wolfgang and I live in Berlin''']
     SCREAMING_SNAKE_CASE__		 =    ['''我叫沃尔夫冈 我住在柏林''']







     @slow
     def lowerCAmelCase  (		self     ):
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       __lowercase		(    SCREAMING_SNAKE_CASE_    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    '''ru'''
     SCREAMING_SNAKE_CASE__		 =    '''fr'''
     SCREAMING_SNAKE_CASE__		 =    ['''Он показал мне рукопись своей новой пьесы.''']
     SCREAMING_SNAKE_CASE__		 =    ['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']







     @slow
     def lowerCAmelCase  (		self     ):
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class       __lowercase		(    SCREAMING_SNAKE_CASE_    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    '''en'''
     SCREAMING_SNAKE_CASE__		 =    '''ROMANCE'''
     SCREAMING_SNAKE_CASE__		 =    [
         '''>>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__		 =    [
         '''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     ):
         self._assert_generated_batch_equal_expected()








"""simple docstring"""


def 					_lowerCAmelCase (  lowercase_		):
						return " ".join(sorted(set(sentence.split()		)		)		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()





