


'''simple docstring'''







def       lowerCAmelCase__       (				lowerCamelCase     :			int   ):
       if not isinstance(lowerCamelCase  ,lowerCamelCase   ):
              _A     :	Dict      =					F'Input value of [number={number}] must be an integer'
              raise TypeError(lowerCamelCase   )
       if number < 0 or number % 2 == 1:
              return False
       _A     :	Optional[Any]      =					int(number**0.5   )
       return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
   import doctest

   doctest.testmod()










'''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       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			List[Any]  ,lowerCamelCase     :			List[Any]=None  ,lowerCamelCase     :			Union[str, Any]=None  ,lowerCamelCase     :			int=None  ,lowerCamelCase     :			List[str]=None  ,lowerCamelCase     :			Optional[int]=None  ,lowerCamelCase     :			Dict=None  ,):
       if attention_mask is None:
              _A     :	str      =					np.where(input_ids != config.pad_token_id  ,1  ,0   )
       if decoder_attention_mask is None:
              _A     :	List[str]      =					np.where(decoder_input_ids != config.pad_token_id  ,1  ,0   )
       if head_mask is None:
              _A     :	List[str]      =					np.ones((config.encoder_layers, config.encoder_attention_heads)   )
       if decoder_head_mask is None:
              _A     :	Any      =					np.ones((config.decoder_layers, config.decoder_attention_heads)   )
       if cross_attn_head_mask is None:
              _A     :	Tuple      =					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  __lowerCamelCase :
    """simple docstring"""


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


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

           _A     :	str      =					shift_tokens_right(SCREAMING_SNAKE_CASE      ,							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=SCREAMING_SNAKE_CASE      ,							)
           _A     :	List[Any]      =					prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           return config, inputs_dict


    def    A    (  self			:							List[str]):
           _A  , _A     :	Tuple      =					self.prepare_config_and_inputs()
           return config, inputs_dict


    def    A    (  self			:							int      ,							SCREAMING_SNAKE_CASE			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	Optional[Any]      =					20
           _A     :	Any      =					model_class_name(SCREAMING_SNAKE_CASE)

           _A     :	Optional[int]      =					model.encode(inputs_dict['input_ids'])

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

           _A     :	Any      =					model.init_cache(decoder_input_ids.shape[0]      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	Tuple      =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,							dtype='i4')

           _A     :	Optional[int]      =					jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,							)
           _A     :	int      =					model.decode(
               decoder_input_ids[:, :-1]      ,							SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							past_key_values=SCREAMING_SNAKE_CASE      ,							decoder_position_ids=SCREAMING_SNAKE_CASE      ,							)

           _A     :	Union[str, Any]      =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,							dtype='i4')
           _A     :	Dict      =					model.decode(
               decoder_input_ids[:, -1:]      ,							SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							past_key_values=outputs_cache.past_key_values      ,							decoder_position_ids=SCREAMING_SNAKE_CASE      ,							)

           _A     :	List[Any]      =					model.decode(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)

           _A     :	Optional[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			:							List[Any]      ,							SCREAMING_SNAKE_CASE			:							Tuple      ,							SCREAMING_SNAKE_CASE			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							Tuple):
           _A     :	List[Any]      =					20
           _A     :	str      =					model_class_name(SCREAMING_SNAKE_CASE)

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

           _A  , _A     :	Union[str, Any]      =					(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

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

           _A     :	Optional[int]      =					model.init_cache(decoder_input_ids.shape[0]      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	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)      ,							)

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

           _A     :	Tuple      =					model.decode(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE)

           _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  __lowerCamelCase (       a_				,					unittest.TestCase				,					a_       ):
    """simple docstring"""
    a													=    True
    a													=    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
    a													=    (FlaxMarianMTModel,) if is_flax_available() else ()


    def    A    (  self			:							str):
           _A     :	Any      =					FlaxMarianModelTester(self)


    def    A    (  self			:							Optional[Any]):
           _A  , _A     :	int      =					self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							int):
           _A  , _A     :	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(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							Tuple):
           _A  , _A     :	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     :	int      =					self._prepare_for_class(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                         _A     :	int      =					model_class(SCREAMING_SNAKE_CASE)

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

                         with self.subTest('JIT Enabled'):
                                _A     :	str      =					encode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

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

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


    def    A    (  self			:							Tuple):
           _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[Any]      =					model_class(SCREAMING_SNAKE_CASE)
                         _A     :	Tuple      =					model.encode(inputs_dict['input_ids']      ,							inputs_dict['attention_mask'])

                         _A     :	int      =					{
                             '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(SCREAMING_SNAKE_CASE			:							Optional[Any]      ,							SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
                                return model.decode(
                                    decoder_input_ids=SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							encoder_outputs=SCREAMING_SNAKE_CASE      ,							)

                         with self.subTest('JIT Enabled'):
                                _A     :	Optional[int]      =					decode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

                         with self.subTest('JIT Disabled'):
                                with jax.disable_jit():
                                       _A     :	List[str]      =					decode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

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


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




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       unittest.TestCase       ):
    """simple docstring"""
    a													=    None
    a													=    None


    @classmethod
    def    A    (  cls			:							List[Any]):
           _A     :	Optional[int]      =					F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
           return cls


    @cached_property
    def    A    (  self			:							Optional[Any]):
           return MarianTokenizer.from_pretrained(self.model_name)


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


    @cached_property
    def    A    (  self			:							int):
           _A     :	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			:							Union[str, Any]      ,							**SCREAMING_SNAKE_CASE			:							int):
           _A     :	str      =					self.translate_src_text(**SCREAMING_SNAKE_CASE)
           self.assertListEqual(self.expected_text      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							str      ,							**SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	int      =					self.tokenizer(self.src_text      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='np'      ,							**SCREAMING_SNAKE_CASE)
           _A     :	Union[str, 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(SCREAMING_SNAKE_CASE      ,							skip_special_tokens=SCREAMING_SNAKE_CASE)
           return generated_words




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


    @slow
    def    A    (  self			:							Any):
           self._assert_generated_batch_equal_expected()




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


    @slow
    def    A    (  self			:							Any):
           self._assert_generated_batch_equal_expected()




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


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




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "de"
    a													=    [
        "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													=    [
        "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			:							Optional[Any]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "zh"
    a													=    ["My name is Wolfgang and I live in Berlin"]
    a													=    ["我叫沃尔夫冈 我住在柏林"]


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




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "ru"
    a													=    "fr"
    a													=    ["Он показал мне рукопись своей новой пьесы."]
    a													=    ["Il m'a montré le manuscrit de sa nouvelle pièce."]


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




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "ROMANCE"
    a													=    [
        ">>fr<< Don't spend so much time watching TV.",
        ">>pt<< Your message has been sent.",
        ">>es<< He's two years older than me.",
    ]
    a													=    [
        "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			:							Any):
           self._assert_generated_batch_equal_expected()






