


"""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 			A__		(					_UpperCAmelCase    :      Optional[int]				,    _UpperCAmelCase    :      Any				,    _UpperCAmelCase    :      Union[str, Any]=None				,    _UpperCAmelCase    :      Optional[Any]=None				,    _UpperCAmelCase    :      Optional[int]=None				,    _UpperCAmelCase    :      Union[str, Any]=None				,    _UpperCAmelCase    :      Optional[Any]=None				,    _UpperCAmelCase    :      List[Any]=None				,    )							->	str:

	'''simple docstring'''


	if attention_mask is None:
		snake_case__  :						Any		=      np.where(input_ids != config.pad_token_id				,    1				,    0	)
	if decoder_attention_mask is None:
		snake_case__  :						Tuple		=      np.where(decoder_input_ids != config.pad_token_id				,    1				,    0	)
	if head_mask is None:
		snake_case__  :						Optional[Any]		=      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__  :						List[Any]		=      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 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=13	,					lowerCamelCase__=7	,					lowerCamelCase__=True	,					lowerCamelCase__=False	,					lowerCamelCase__=99	,					lowerCamelCase__=16	,					lowerCamelCase__=2	,					lowerCamelCase__=4	,					lowerCamelCase__=4	,					lowerCamelCase__="gelu"	,					lowerCamelCase__=0.1	,					lowerCamelCase__=0.1	,					lowerCamelCase__=32	,					lowerCamelCase__=2	,					lowerCamelCase__=1	,					lowerCamelCase__=0	,					lowerCamelCase__=0.02	,					)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						str		=      parent
								snake_case__  :						Any		=      batch_size
								snake_case__  :						List[str]		=      seq_length
								snake_case__  :						Union[str, Any]		=      is_training
								snake_case__  :						Tuple		=      use_labels
								snake_case__  :						List[str]		=      vocab_size
								snake_case__  :						Optional[int]		=      hidden_size
								snake_case__  :						str		=      num_hidden_layers
								snake_case__  :						Optional[Any]		=      num_attention_heads
								snake_case__  :						int		=      intermediate_size
								snake_case__  :						Union[str, Any]		=      hidden_act
								snake_case__  :						Optional[Any]		=      hidden_dropout_prob
								snake_case__  :						Union[str, Any]		=      attention_probs_dropout_prob
								snake_case__  :						int		=      max_position_embeddings
								snake_case__  :						List[str]		=      eos_token_id
								snake_case__  :						Tuple		=      pad_token_id
								snake_case__  :						Optional[int]		=      bos_token_id
								snake_case__  :						int		=      initializer_range




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						List[str]		=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,					self.vocab_size)	,					3	,					self.vocab_size)
								snake_case__  :						Optional[int]		=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,					dtype=np.intaa))	,					-1)

								snake_case__  :						Dict		=      shift_tokens_right(__SCREAMING_SNAKE_CASE	,					1	,					2)

								snake_case__  :						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	,					)
								snake_case__  :						List[str]		=      prepare_marian_inputs_dict(__SCREAMING_SNAKE_CASE	,					__SCREAMING_SNAKE_CASE	,					__SCREAMING_SNAKE_CASE)
								return config, inputs_dict




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__,					snake_case__  :						Tuple		=      self.prepare_config_and_inputs()
								return config, inputs_dict




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Any:







								'''simple docstring'''




								snake_case__  :						int		=      20
								snake_case__  :						List[str]		=      model_class_name(__SCREAMING_SNAKE_CASE)

								snake_case__  :						List[str]		=      model.encode(inputs_dict["input_ids"])

								snake_case__,					snake_case__  :						str		=      (
								    inputs_dict["decoder_input_ids"],
								    inputs_dict["decoder_attention_mask"],
								)

								snake_case__  :						List[Any]		=      model.init_cache(decoder_input_ids.shape[0]	,					__SCREAMING_SNAKE_CASE	,					__SCREAMING_SNAKE_CASE)
								snake_case__  :						str		=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,					dtype="i4")

								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__  :						str		=      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	,					)

								snake_case__  :						List[Any]		=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,					dtype="i4")
								snake_case__  :						int		=      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	,					)

								snake_case__  :						List[str]		=      model.decode(__SCREAMING_SNAKE_CASE	,					__SCREAMING_SNAKE_CASE)

								snake_case__  :						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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Any		=      20
								snake_case__  :						int		=      model_class_name(__SCREAMING_SNAKE_CASE)

								snake_case__  :						List[Any]		=      model.encode(inputs_dict["input_ids"])

								snake_case__,					snake_case__  :						Any		=      (
								    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__  :						int		=      model.init_cache(decoder_input_ids.shape[0]	,					__SCREAMING_SNAKE_CASE	,					__SCREAMING_SNAKE_CASE)
								snake_case__  :						Tuple		=      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__  :						Tuple		=      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	,					)
								snake_case__  :						List[Any]		=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,					dtype="i4")
								snake_case__  :						int		=      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	,					)

								snake_case__  :						int		=      model.decode(__SCREAMING_SNAKE_CASE	,					__SCREAMING_SNAKE_CASE	,					decoder_attention_mask=__SCREAMING_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 			SCREAMING_SNAKE_CASE_  (   __UpperCAmelCase	,      unittest.TestCase	,      __UpperCAmelCase):




							'''simple docstring'''





							__magic_name__					:     Dict				  =  True
							__magic_name__					:     str				  =  (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
							__magic_name__					:     str				  =  (FlaxMarianMTModel,) if is_flax_available() else ()




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						int		=      FlaxMarianModelTester(self)




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__,					snake_case__  :						Dict		=      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        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								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_with_attn_mask(__SCREAMING_SNAKE_CASE	,					__SCREAMING_SNAKE_CASE	,					__SCREAMING_SNAKE_CASE)




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__,					snake_case__  :						int		=      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		=      self._prepare_for_class(__SCREAMING_SNAKE_CASE	,					__SCREAMING_SNAKE_CASE)
										snake_case__  :						Optional[int]		=      model_class(__SCREAMING_SNAKE_CASE)

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

										with self.subTest("JIT Enabled"):
											snake_case__  :						Dict		=      encode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

										with self.subTest("JIT Disabled"):
											with jax.disable_jit():
												snake_case__  :						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        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__,					snake_case__  :						int		=      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		=      model_class(__SCREAMING_SNAKE_CASE)
										snake_case__  :						Optional[int]		=      model.encode(inputs_dict["input_ids"]	,					inputs_dict["attention_mask"])

										snake_case__  :						List[Any]		=      {
										    "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=__SCREAMING_SNAKE_CASE	,					decoder_attention_mask=__SCREAMING_SNAKE_CASE	,					encoder_outputs=__SCREAMING_SNAKE_CASE	,					)

										with self.subTest("JIT Enabled"):
											snake_case__  :						Tuple		=      decode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

										with self.subTest("JIT Disabled"):
											with jax.disable_jit():
												snake_case__  :						Tuple		=      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        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




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


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




							'''simple docstring'''





							__magic_name__					:     Optional[Any]				  =  None
							__magic_name__					:     Dict				  =  None




							@classmethod
							def        UpperCAmelCase (						cls)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
								return cls




							@cached_property
							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								return MarianTokenizer.from_pretrained(self.model_name)




							@property
							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								return self.tokenizer.eos_token_id




							@cached_property
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						str		=      FlaxMarianMTModel.from_pretrained(self.model_name)
								self.assertEqual(model.config.decoder_start_token_id	,					model.config.pad_token_id)
								return model




							def        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								snake_case__  :						Any		=      self.translate_src_text(**__SCREAMING_SNAKE_CASE)
								self.assertListEqual(self.expected_text	,					__SCREAMING_SNAKE_CASE)







							def        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.tokenizer(self.src_text	,					padding=__SCREAMING_SNAKE_CASE	,					return_tensors="np"	,					**__SCREAMING_SNAKE_CASE)
								snake_case__  :						Dict		=      self.model.generate(
								    model_inputs.input_ids	,					attention_mask=model_inputs.attention_mask	,					num_beams=2	,					max_length=128	,					).sequences
								snake_case__  :						Union[str, Any]		=      self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	,					skip_special_tokens=__SCREAMING_SNAKE_CASE)
								return generated_words


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




							'''simple docstring'''





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




							@slow
							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								self._assert_generated_batch_equal_expected()


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




							'''simple docstring'''





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




							@slow
							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								self._assert_generated_batch_equal_expected()


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




							'''simple docstring'''





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




							@slow
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								self._assert_generated_batch_equal_expected()


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




							'''simple docstring'''





							__magic_name__					:     Any				  =  '''en'''
							__magic_name__					:     List[str]				  =  '''de'''
							__magic_name__					:     Dict				  =  [
							    '''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.''',
							]
							__magic_name__					:     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        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								self._assert_generated_batch_equal_expected()


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




							'''simple docstring'''





							__magic_name__					:     int				  =  '''en'''
							__magic_name__					:     Union[str, Any]				  =  '''zh'''
							__magic_name__					:     int				  =  ['''My name is Wolfgang and I live in Berlin''']
							__magic_name__					:     str				  =  ['''我叫沃尔夫冈 我住在柏林''']




							@slow
							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								self._assert_generated_batch_equal_expected()


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




							'''simple docstring'''





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




							@slow
							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								self._assert_generated_batch_equal_expected()


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




							'''simple docstring'''





							__magic_name__					:     Optional[int]				  =  '''en'''
							__magic_name__					:     List[Any]				  =  '''ROMANCE'''
							__magic_name__					:     Any				  =  [
							    '''>>fr<< Don\'t spend so much time watching TV.''',
							    '''>>pt<< Your message has been sent.''',
							    '''>>es<< He\'s two years older than me.''',
							]
							__magic_name__					:     List[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        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								self._assert_generated_batch_equal_expected()










from math import floor, sqrt





def 	__snake_case      (     _UpperCAmelCase							):
    __a					=							0.0
    __a					=							1.0
    __a					=							int(sqrt(_UpperCAmelCase							)							)
    __a					=							root
    __a					=							0
    while integer_part != 2 * root:
        __a					=							denominator * integer_part - numerator
        __a					=							(n - numerator**2) / denominator
        __a					=							int((root + numerator) / denominator							)
        period += 1
    return period





def 	__snake_case      (     _UpperCAmelCase = 10000							):
    __a					=							0
    for i in range(2  ,					n + 1							):
        __a					=							sqrt(_UpperCAmelCase							)
        if sr - floor(_UpperCAmelCase							) != 0 and continuous_fraction_period(_UpperCAmelCase							) % 2 == 1:
            count_odd_periods += 1
    return count_odd_periods


if __name__ == "__main__":
 print(f'{solution(int(input().strip()))}')





