


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_       :List[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_							(A			:			Union[str, Any]		, A			:			List[str]		, A			:			Optional[Any]=None		, A			:			int=None		, A			:			Optional[Any]=None		, A			:			Any=None		, A			:			Any=None		, A			:			Optional[int]=None		, ):



       if attention_mask is None:
              snake_case__							:     Optional[int]            =     np.where(input_ids != config.pad_token_id		, 1		, 0				)
       if decoder_attention_mask is None:
              snake_case__							:     List[str]            =     np.where(decoder_input_ids != config.pad_token_id		, 1		, 0				)
       if head_mask is None:
              snake_case__							:     Tuple            =     np.ones((config.encoder_layers, config.encoder_attention_heads)				)
       if decoder_head_mask is None:
              snake_case__							:     List[str]            =     np.ones((config.decoder_layers, config.decoder_attention_heads)				)
       if cross_attn_head_mask is None:
              snake_case__							:     Dict            =     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  :  Any,			_snake_case  :  str,			_snake_case  :  Dict=1_3,			_snake_case  :  Union[str, Any]=7,			_snake_case  :  Any=True,			_snake_case  :  List[str]=False,			_snake_case  :  int=9_9,			_snake_case  :  Optional[int]=1_6,			_snake_case  :  Tuple=2,			_snake_case  :  int=4,			_snake_case  :  Optional[Any]=4,			_snake_case  :  Optional[Any]="gelu",			_snake_case  :  Optional[Any]=0.1,			_snake_case  :  Any=0.1,			_snake_case  :  Optional[int]=3_2,			_snake_case  :  Tuple=2,			_snake_case  :  Dict=1,			_snake_case  :  Optional[Any]=0,			_snake_case  :  Tuple=0.0_2,			)       ->Union[str, Any]:
            snake_case__							:     int            =     parent
            snake_case__							:     str            =     batch_size
            snake_case__							:     str            =     seq_length
            snake_case__							:     str            =     is_training
            snake_case__							:     Tuple            =     use_labels
            snake_case__							:     List[str]            =     vocab_size
            snake_case__							:     str            =     hidden_size
            snake_case__							:     Dict            =     num_hidden_layers
            snake_case__							:     int            =     num_attention_heads
            snake_case__							:     List[str]            =     intermediate_size
            snake_case__							:     Union[str, Any]            =     hidden_act
            snake_case__							:     Optional[Any]            =     hidden_dropout_prob
            snake_case__							:     Dict            =     attention_probs_dropout_prob
            snake_case__							:     Optional[int]            =     max_position_embeddings
            snake_case__							:     List[Any]            =     eos_token_id
            snake_case__							:     Tuple            =     pad_token_id
            snake_case__							:     int            =     bos_token_id
            snake_case__							:     Any            =     initializer_range






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

            snake_case__							:     Tuple            =     shift_tokens_right(_snake_case,			1,			2 )

            snake_case__							:     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=_snake_case,			)
            snake_case__							:     int            =     prepare_marian_inputs_dict(_snake_case,			_snake_case,			_snake_case )
            return config, inputs_dict






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






     def        lowercase_       (	self  :  List[str],			_snake_case  :  Optional[Any],			_snake_case  :  Dict,			_snake_case  :  List[str] )       ->Union[str, Any]:
            snake_case__							:     List[Any]            =     2_0
            snake_case__							:     Union[str, Any]            =     model_class_name(_snake_case )

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

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

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

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

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

            snake_case__							:     str            =     model.decode(_snake_case,			_snake_case )

            snake_case__							:     List[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  :  Union[str, Any],			_snake_case  :  Union[str, Any],			_snake_case  :  Dict,			_snake_case  :  Any )       ->Union[str, Any]:
            snake_case__							:     Tuple            =     2_0
            snake_case__							:     Dict            =     model_class_name(_snake_case )

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

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

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

            snake_case__							:     Dict            =     model.init_cache(decoder_input_ids.shape[0],			_snake_case,			_snake_case )
            snake_case__							:     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),			)

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

            snake_case__							:     Union[str, Any]            =     model.decode(_snake_case,			_snake_case,			decoder_attention_mask=_snake_case )

            snake_case__							:     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}''' )




@require_flax
class  snake_case__						(	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        lowercase_       (	self  :  Tuple )       ->Tuple:
            snake_case__							:     int            =     FlaxMarianModelTester(self )






     def        lowercase_       (	self  :  List[Any] )       ->Dict:
            snake_case__     ,    snake_case__							:     Tuple            =     self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                   self.model_tester.check_use_cache_forward(_snake_case,			_snake_case,			_snake_case )






     def        lowercase_       (	self  :  List[str] )       ->List[str]:
            snake_case__     ,    snake_case__							:     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(_snake_case,			_snake_case,			_snake_case )






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

            for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__ ):
                          snake_case__							:     int            =     self._prepare_for_class(_snake_case,			_snake_case )
                          snake_case__							:     Any            =     model_class(_snake_case )

                          @jax.jit
                          def encode_jitted(_snake_case  :  Dict,			_snake_case  :  List[str]=None,			**_snake_case  :  Any ):
                                 return model.encode(input_ids=_snake_case,			attention_mask=_snake_case )

                          with self.subTest('JIT Enabled' ):
                                 snake_case__							:     Tuple            =     encode_jitted(**_snake_case ).to_tuple()

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

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






     def        lowercase_       (	self  :  List[Any] )       ->Any:
            snake_case__     ,    snake_case__							:     Dict            =     self.model_tester.prepare_config_and_inputs_for_common()

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

                          snake_case__							:     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(_snake_case  :  Optional[int],			_snake_case  :  Optional[Any],			_snake_case  :  List[Any] ):
                                 return model.decode(
                                     decoder_input_ids=_snake_case,			decoder_attention_mask=_snake_case,			encoder_outputs=_snake_case,			)

                          with self.subTest('JIT Enabled' ):
                                 snake_case__							:     List[Any]            =     decode_jitted(**_snake_case ).to_tuple()

                          with self.subTest('JIT Disabled' ):
                                 with jax.disable_jit():
                                        snake_case__							:     Optional[int]            =     decode_jitted(**_snake_case ).to_tuple()

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






     @slow
     def        lowercase_       (	self  :  Any )       ->Tuple:

            for model_class_name in self.all_model_classes:
                   snake_case__							:     Tuple            =     model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de' )
                   # FlaxMarianForSequenceClassification expects eos token in input_ids
                   snake_case__							:     List[Any]            =     np.ones((1, 1) ) * model.config.eos_token_id
                   snake_case__							:     Tuple            =     model(_snake_case )
                   self.assertIsNotNone(_snake_case )




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






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     None
     _SCREAMING_SNAKE_CASE            =     None






     @classmethod
     def        lowercase_       (	cls  :  Any )       ->Optional[Any]:
            snake_case__							:     Optional[Any]            =     F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
            return cls






     @cached_property
     def        lowercase_       (	self  :  Dict )       ->List[str]:
            return MarianTokenizer.from_pretrained(self.model_name )






     @property
     def        lowercase_       (	self  :  Any )       ->str:
            return self.tokenizer.eos_token_id






     @cached_property
     def        lowercase_       (	self  :  List[str] )       ->Union[str, Any]:
            snake_case__							:     Union[str, Any]            =     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,			**_snake_case  :  Optional[Any] )       ->Dict:
            snake_case__							:     Union[str, Any]            =     self.translate_src_text(**_snake_case )
            self.assertListEqual(self.expected_text,			_snake_case )






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




@require_flax
@require_sentencepiece
@require_tokenizers
class  snake_case__						(	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        lowercase_       (	self  :  int )       ->Tuple:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  snake_case__						(	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        lowercase_       (	self  :  str )       ->List[str]:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  snake_case__						(	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        lowercase_       (	self  :  str )       ->Optional[int]:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  snake_case__						(	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        lowercase_       (	self  :  List[Any] )       ->Union[str, Any]:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  snake_case__						(	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        lowercase_       (	self  :  List[str] )       ->Dict:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  snake_case__						(	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        lowercase_       (	self  :  int )       ->List[str]:
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  snake_case__						(	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        lowercase_       (	self  :  Union[str, Any] )       ->Union[str, Any]:
            self._assert_generated_batch_equal_expected()











import math
def 	lowercase_							(A			:			int				):

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5		, int(math.sqrt(A				) + 1				)		, 6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True
def 	lowercase_							(A			:			int = 1_0_0_0		, A			:			int = 1_0_0_0				):
       snake_case__							:     str            =     [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1		, A				):
              for b in range(2		, A				):
                     if is_prime(A				):
                            snake_case__							:     Optional[Any]            =     0
                            snake_case__							:     Optional[int]            =     0
                            while is_prime((n**2) + (a * n) + b				):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   snake_case__							:     Union[str, Any]            =     [count, a, b]
       snake_case__							:     List[str]            =     longest[1] * longest[2]
       return ans


if __name__ == "__main__":
 print(solution(1_000, 1_000))






