



"""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				:     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 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=None       ,							UpperCamelCase__=None       ,							UpperCamelCase__=None       ,							UpperCamelCase__=None       ,							UpperCamelCase__=None       ,							UpperCamelCase__=None       ,							):


  '''simple docstring'''


  if attention_mask is None:
    _SCREAMING_SNAKE_CASE        =			np.where(input_ids != config.pad_token_id       ,							1       ,							0    )
  if decoder_attention_mask is None:
    _SCREAMING_SNAKE_CASE        =			np.where(decoder_input_ids != config.pad_token_id       ,							1       ,							0    )
  if head_mask is None:
    _SCREAMING_SNAKE_CASE        =			np.ones((config.encoder_layers, config.encoder_attention_heads)    )
  if decoder_head_mask is None:
    _SCREAMING_SNAKE_CASE        =			np.ones((config.decoder_layers, config.decoder_attention_heads)    )
  if cross_attn_head_mask is None:
    _SCREAMING_SNAKE_CASE        =			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:




    def __init__(   self    ,							A_    ,							A_=13    ,							A_=7    ,							A_=True    ,							A_=False    ,							A_=99    ,							A_=16    ,							A_=2    ,							A_=4    ,							A_=4    ,							A_="gelu"    ,							A_=0.1    ,							A_=0.1    ,							A_=32    ,							A_=2    ,							A_=1    ,							A_=0    ,							A_=0.02    ,							):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			parent
      _SCREAMING_SNAKE_CASE        =			batch_size
      _SCREAMING_SNAKE_CASE        =			seq_length
      _SCREAMING_SNAKE_CASE        =			is_training
      _SCREAMING_SNAKE_CASE        =			use_labels
      _SCREAMING_SNAKE_CASE        =			vocab_size
      _SCREAMING_SNAKE_CASE        =			hidden_size
      _SCREAMING_SNAKE_CASE        =			num_hidden_layers
      _SCREAMING_SNAKE_CASE        =			num_attention_heads
      _SCREAMING_SNAKE_CASE        =			intermediate_size
      _SCREAMING_SNAKE_CASE        =			hidden_act
      _SCREAMING_SNAKE_CASE        =			hidden_dropout_prob
      _SCREAMING_SNAKE_CASE        =			attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE        =			max_position_embeddings
      _SCREAMING_SNAKE_CASE        =			eos_token_id
      _SCREAMING_SNAKE_CASE        =			pad_token_id
      _SCREAMING_SNAKE_CASE        =			bos_token_id
      _SCREAMING_SNAKE_CASE        =			initializer_range




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,							self.vocab_size			)    ,							3    ,							self.vocab_size			)
      _SCREAMING_SNAKE_CASE        =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,							dtype=np.intaa			))    ,							-1			)

      _SCREAMING_SNAKE_CASE        =			shift_tokens_right(A_    ,							1    ,							2			)

      _SCREAMING_SNAKE_CASE        =			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=A_    ,							)
      _SCREAMING_SNAKE_CASE        =			prepare_marian_inputs_dict(A_    ,							A_    ,							A_			)
      return config, inputs_dict




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.prepare_config_and_inputs()
      return config, inputs_dict




    def   A   (   self    ,							A_    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			20
      _SCREAMING_SNAKE_CASE        =			model_class_name(A_			)

      _SCREAMING_SNAKE_CASE        =			model.encode(inputs_dict['''input_ids''']			)

      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			(
          inputs_dict['''decoder_input_ids'''],
          inputs_dict['''decoder_attention_mask'''],
      )

      _SCREAMING_SNAKE_CASE        =			model.init_cache(decoder_input_ids.shape[0]    ,							A_    ,							A_			)
      _SCREAMING_SNAKE_CASE        =			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,							dtype='''i4'''			)

      _SCREAMING_SNAKE_CASE        =			jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]    ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,							)
      _SCREAMING_SNAKE_CASE        =			model.decode(
          decoder_input_ids[:, :-1]    ,							A_    ,							decoder_attention_mask=A_    ,							past_key_values=A_    ,							decoder_position_ids=A_    ,							)

      _SCREAMING_SNAKE_CASE        =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,							dtype='''i4'''			)
      _SCREAMING_SNAKE_CASE        =			model.decode(
          decoder_input_ids[:, -1:]    ,							A_    ,							decoder_attention_mask=A_    ,							past_key_values=outputs_cache.past_key_values    ,							decoder_position_ids=A_    ,							)

      _SCREAMING_SNAKE_CASE        =			model.decode(A_    ,							A_			)

      _SCREAMING_SNAKE_CASE        =			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    ,							A_    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			20
      _SCREAMING_SNAKE_CASE        =			model_class_name(A_			)

      _SCREAMING_SNAKE_CASE        =			model.encode(inputs_dict['''input_ids''']			)

      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			(
          inputs_dict['''decoder_input_ids'''],
          inputs_dict['''decoder_attention_mask'''],
      )

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

      _SCREAMING_SNAKE_CASE        =			model.init_cache(decoder_input_ids.shape[0]    ,							A_    ,							A_			)
      _SCREAMING_SNAKE_CASE        =			jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]    ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,							)

      _SCREAMING_SNAKE_CASE        =			model.decode(
          decoder_input_ids[:, :-1]    ,							A_    ,							decoder_attention_mask=A_    ,							past_key_values=A_    ,							decoder_position_ids=A_    ,							)
      _SCREAMING_SNAKE_CASE        =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,							dtype='''i4'''			)
      _SCREAMING_SNAKE_CASE        =			model.decode(
          decoder_input_ids[:, -1:]    ,							A_    ,							past_key_values=outputs_cache.past_key_values    ,							decoder_attention_mask=A_    ,							decoder_position_ids=A_    ,							)

      _SCREAMING_SNAKE_CASE        =			model.decode(A_    ,							A_    ,							decoder_attention_mask=A_			)

      _SCREAMING_SNAKE_CASE        =			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(					__A			,			unittest.TestCase			,			__A       ):
    _A       = True
    _A       = (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
    _A       = (FlaxMarianMTModel,) if is_flax_available() else ()




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			FlaxMarianModelTester(self			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(A_    ,							A_    ,							A_			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			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(A_    ,							A_    ,							A_			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__			):
          _SCREAMING_SNAKE_CASE        =			self._prepare_for_class(A_    ,							A_			)
          _SCREAMING_SNAKE_CASE        =			model_class(A_			)

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

          with self.subTest('''JIT Enabled'''			):
            _SCREAMING_SNAKE_CASE        =			encode_jitted(**A_			).to_tuple()

          with self.subTest('''JIT Disabled'''			):
            with jax.disable_jit():
              _SCREAMING_SNAKE_CASE        =			encode_jitted(**A_			).to_tuple()

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




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__			):
          _SCREAMING_SNAKE_CASE        =			model_class(A_			)
          _SCREAMING_SNAKE_CASE        =			model.encode(inputs_dict['''input_ids''']    ,							inputs_dict['''attention_mask''']			)

          _SCREAMING_SNAKE_CASE        =			{
              '''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(A_    ,							A_    ,							A_			):
            return model.decode(
                decoder_input_ids=A_    ,							decoder_attention_mask=A_    ,							encoder_outputs=A_    ,							)

          with self.subTest('''JIT Enabled'''			):
            _SCREAMING_SNAKE_CASE        =			decode_jitted(**A_			).to_tuple()

          with self.subTest('''JIT Disabled'''			):
            with jax.disable_jit():
              _SCREAMING_SNAKE_CASE        =			decode_jitted(**A_			).to_tuple()

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




    @slow
    def   A   (   self			):





      '''simple docstring'''



      for model_class_name in self.all_model_classes:
        _SCREAMING_SNAKE_CASE        =			model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''			)
        # FlaxMarianForSequenceClassification expects eos token in input_ids
        _SCREAMING_SNAKE_CASE        =			np.ones((1, 1)			) * model.config.eos_token_id
        _SCREAMING_SNAKE_CASE        =			model(A_			)
        self.assertIsNotNone(A_			)




@require_flax
@require_sentencepiece
@require_tokenizers
class    __snake_case(					unittest.TestCase       ):
    _A       = None
    _A       = None




    @classmethod
    def   A   (   cls			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
      return cls




    @cached_property
    def   A   (   self			):





      '''simple docstring'''



      return MarianTokenizer.from_pretrained(self.model_name			)




    @property
    def   A   (   self			):





      '''simple docstring'''



      return self.tokenizer.eos_token_id




    @cached_property
    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			FlaxMarianMTModel.from_pretrained(self.model_name			)
      self.assertEqual(model.config.decoder_start_token_id    ,							model.config.pad_token_id			)
      return model




    def   A   (   self    ,							**A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.translate_src_text(**A_			)
      self.assertListEqual(self.expected_text    ,							A_			)




    def   A   (   self    ,							**A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.tokenizer(self.src_text    ,							padding=A_    ,							return_tensors='''np'''    ,							**A_			)
      _SCREAMING_SNAKE_CASE        =			self.model.generate(
          model_inputs.input_ids    ,							attention_mask=model_inputs.attention_mask    ,							num_beams=2    ,							max_length=128    ,							).sequences
      _SCREAMING_SNAKE_CASE        =			self.tokenizer.batch_decode(A_    ,							skip_special_tokens=A_			)
      return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class    __snake_case(					__A       ):
    _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			):





      '''simple docstring'''



      self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    __snake_case(					__A       ):
    _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			):





      '''simple docstring'''



      self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    __snake_case(					__A       ):
    _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			):





      '''simple docstring'''



      self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    __snake_case(					__A       ):
    _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			):





      '''simple docstring'''



      self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    __snake_case(					__A       ):
    _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			):





      '''simple docstring'''



      self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    __snake_case(					__A       ):
    _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			):





      '''simple docstring'''



      self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    __snake_case(					__A       ):
    _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			):





      '''simple docstring'''



      self._assert_generated_batch_equal_expected()





"""simple docstring"""






from math import gcd


def 							A__ (     UpperCamelCase__ = 12_000    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			0
  for d in range(max_d + 1    ):
    for n in range(d // 3 + 1       ,							(d + 1) // 2    ):
      if gcd(UpperCamelCase__       ,							UpperCamelCase__    ) == 1:
        fractions_number += 1
  return fractions_number


if __name__ == "__main__":
    print(F'''{solution() = }''')
