


"""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
				__SCREAMING_SNAKE_CASE				:     Optional[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					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=None					,			)     ->					int:
							if attention_mask is None:
														snake_case_     							=		np.where(input_ids != config.pad_token_id					,			1					,			0					)
							if decoder_attention_mask is None:
														snake_case_     							=		np.where(decoder_input_ids != config.pad_token_id					,			1					,			0					)
							if head_mask is None:
														snake_case_     							=		np.ones((config.encoder_layers, config.encoder_attention_heads)					)
							if decoder_head_mask is None:
														snake_case_     							=		np.ones((config.decoder_layers, config.decoder_attention_heads)					)
							if cross_attn_head_mask is None:
														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   __A  :





							'''simple docstring'''

							def __init__(					self				:   List[Any]		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   Tuple=13		,							UpperCAmelCase_				:   Optional[int]=7		,							UpperCAmelCase_				:   int=True		,							UpperCAmelCase_				:   Dict=False		,							UpperCAmelCase_				:   Dict=99		,							UpperCAmelCase_				:   str=16		,							UpperCAmelCase_				:   Optional[Any]=2		,							UpperCAmelCase_				:   Tuple=4		,							UpperCAmelCase_				:   str=4		,							UpperCAmelCase_				:   Union[str, Any]="gelu"		,							UpperCAmelCase_				:   Optional[Any]=0.1		,							UpperCAmelCase_				:   Any=0.1		,							UpperCAmelCase_				:   List[Any]=32		,							UpperCAmelCase_				:   List[Any]=2		,							UpperCAmelCase_				:   Any=1		,							UpperCAmelCase_				:   int=0		,							UpperCAmelCase_				:   str=0.02		,							)       ->List[Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   int							)       ->Union[str, Any]:



														"""simple docstring"""



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

														snake_case_     							=		shift_tokens_right(UpperCAmelCase_		,							1		,							2							)

														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=UpperCAmelCase_		,							)
														snake_case_     							=		prepare_marian_inputs_dict(UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)
														return config, inputs_dict



							def 		lowerCAmelCase							(					self				:   Tuple							)       ->Optional[Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   str		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   Any							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		20
														snake_case_     							=		model_class_name(UpperCAmelCase_							)

														snake_case_     							=		model.encode(inputs_dict["""input_ids"""]							)

														snake_case_							,    snake_case_     							=		(
														    inputs_dict["""decoder_input_ids"""],
														    inputs_dict["""decoder_attention_mask"""],
														)

														snake_case_     							=		model.init_cache(decoder_input_ids.shape[0]		,							UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,							dtype="""i4"""							)

														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)		,							)
														snake_case_     							=		model.decode(
														    decoder_input_ids[:, :-1]		,							UpperCAmelCase_		,							decoder_attention_mask=UpperCAmelCase_		,							past_key_values=UpperCAmelCase_		,							decoder_position_ids=UpperCAmelCase_		,							)

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

														snake_case_     							=		model.decode(UpperCAmelCase_		,							UpperCAmelCase_							)

														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 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   str							)       ->Optional[Any]:



														"""simple docstring"""



														snake_case_     							=		20
														snake_case_     							=		model_class_name(UpperCAmelCase_							)

														snake_case_     							=		model.encode(inputs_dict["""input_ids"""]							)

														snake_case_							,    snake_case_     							=		(
														    inputs_dict["""decoder_input_ids"""],
														    inputs_dict["""decoder_attention_mask"""],
														)

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

														snake_case_     							=		model.init_cache(decoder_input_ids.shape[0]		,							UpperCAmelCase_		,							UpperCAmelCase_							)
														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)		,							)

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

														snake_case_     							=		model.decode(UpperCAmelCase_		,							UpperCAmelCase_		,							decoder_attention_mask=UpperCAmelCase_							)

														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   __A  (snake_case__    ,		unittest.TestCase    ,		snake_case__):





							'''simple docstring'''

							__lowercase:		Union[str, Any]				   =     True
							__lowercase:		Dict				   =     (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
							__lowercase:		List[Any]				   =     (FlaxMarianMTModel,) if is_flax_available() else ()



							def 		lowerCAmelCase							(					self				:   int							)       ->str:



														"""simple docstring"""



														snake_case_     							=		FlaxMarianModelTester(self							)



							def 		lowerCAmelCase							(					self				:   Dict							)       ->str:



														"""simple docstring"""



														snake_case_							,    snake_case_     							=		self.model_tester.prepare_config_and_inputs()
														for model_class in self.all_model_classes:
																					self.model_tester.check_use_cache_forward(UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   int							)       ->Dict:



														"""simple docstring"""



														snake_case_							,    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(UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[int]							)       ->int:



														"""simple docstring"""



														snake_case_							,    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__							):
																												snake_case_     							=		self._prepare_for_class(UpperCAmelCase_		,							UpperCAmelCase_							)
																												snake_case_     							=		model_class(UpperCAmelCase_							)

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

																												with self.subTest("""JIT Enabled"""							):
																																			snake_case_     							=		encode_jitted(**UpperCAmelCase_							).to_tuple()

																												with self.subTest("""JIT Disabled"""							):
																																			with jax.disable_jit():
																																										snake_case_     							=		encode_jitted(**UpperCAmelCase_							).to_tuple()

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



							def 		lowerCAmelCase							(					self				:   Dict							)       ->List[str]:



														"""simple docstring"""



														snake_case_							,    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__							):
																												snake_case_     							=		model_class(UpperCAmelCase_							)
																												snake_case_     							=		model.encode(inputs_dict["""input_ids"""]		,							inputs_dict["""attention_mask"""]							)

																												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(UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   List[str]							):
																																			return model.decode(
																																			    decoder_input_ids=UpperCAmelCase_		,							decoder_attention_mask=UpperCAmelCase_		,							encoder_outputs=UpperCAmelCase_		,							)

																												with self.subTest("""JIT Enabled"""							):
																																			snake_case_     							=		decode_jitted(**UpperCAmelCase_							).to_tuple()

																												with self.subTest("""JIT Disabled"""							):
																																			with jax.disable_jit():
																																										snake_case_     							=		decode_jitted(**UpperCAmelCase_							).to_tuple()

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



							@slow
							def 		lowerCAmelCase							(					self				:   Optional[int]							)       ->Dict:



														"""simple docstring"""



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





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





							'''simple docstring'''

							__lowercase:		Any				   =     None
							__lowercase:		str				   =     None



							@classmethod
							def 		lowerCAmelCase							(					cls				:   str							)       ->None:



														"""simple docstring"""



														snake_case_     							=		F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
														return cls



							@cached_property
							def 		lowerCAmelCase							(					self				:   Dict							)       ->Dict:



														"""simple docstring"""



														return MarianTokenizer.from_pretrained(self.model_name							)



							@property
							def 		lowerCAmelCase							(					self				:   str							)       ->int:



														"""simple docstring"""



														return self.tokenizer.eos_token_id



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



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Tuple		,							**UpperCAmelCase_				:   Union[str, Any]							)       ->str:



														"""simple docstring"""



														snake_case_     							=		self.translate_src_text(**UpperCAmelCase_							)
														self.assertListEqual(self.expected_text		,							UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   List[str]		,							**UpperCAmelCase_				:   Union[str, Any]							)       ->Any:



														"""simple docstring"""



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





@require_flax
@require_sentencepiece
@require_tokenizers
class   __A  (snake_case__):





							'''simple docstring'''

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



							@slow
							def 		lowerCAmelCase							(					self				:   Union[str, Any]							)       ->str:



														"""simple docstring"""



														self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		Union[str, Any]				   =     """fr"""
							__lowercase:		int				   =     """en"""
							__lowercase:		List[str]				   =     [
							    """Donnez moi le micro.""",
							    """Tom et Mary étaient assis à une table.""",  # Accents
							]
							__lowercase:		Optional[int]				   =     [
							    """Give me the microphone.""",
							    """Tom and Mary were sitting at a table.""",
							]



							@slow
							def 		lowerCAmelCase							(					self				:   List[Any]							)       ->int:



														"""simple docstring"""



														self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class   __A  (snake_case__):





							'''simple docstring'''

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



							@slow
							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->Union[str, Any]:



														"""simple docstring"""



														self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		str				   =     """en"""
							__lowercase:		str				   =     """de"""
							__lowercase:		Tuple				   =     [
							    """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.""",
							]
							__lowercase:		Optional[int]				   =     [
							    """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				:   Any							)       ->int:



														"""simple docstring"""



														self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		List[str]				   =     """en"""
							__lowercase:		Optional[int]				   =     """zh"""
							__lowercase:		Dict				   =     ["""My name is Wolfgang and I live in Berlin"""]
							__lowercase:		Tuple				   =     ["""我叫沃尔夫冈 我住在柏林"""]



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



														"""simple docstring"""



														self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		Optional[Any]				   =     """ru"""
							__lowercase:		Tuple				   =     """fr"""
							__lowercase:		Optional[Any]				   =     ["""Он показал мне рукопись своей новой пьесы."""]
							__lowercase:		Tuple				   =     ["""Il m'a montré le manuscrit de sa nouvelle pièce."""]



							@slow
							def 		lowerCAmelCase							(					self				:   Optional[int]							)       ->Dict:



														"""simple docstring"""



														self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		Dict				   =     """en"""
							__lowercase:		List[str]				   =     """ROMANCE"""
							__lowercase:		str				   =     [
							    """>>fr<< Don't spend so much time watching TV.""",
							    """>>pt<< Your message has been sent.""",
							    """>>es<< He's two years older than me.""",
							]
							__lowercase:		Any				   =     [
							    """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				:   Optional[Any]							)       ->Tuple:



														"""simple docstring"""



														self._assert_generated_batch_equal_expected()







"""simple docstring"""
import math







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					list[int]:
							if not isinstance(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					):
														snake_case_     							=		f"""Input value of [number={number}] must be an integer"""
														raise TypeError(_SCREAMING_SNAKE_CASE					)
							if number < 1:
														snake_case_     							=		f"""Input value of [number={number}] must be a positive integer"""
														raise ValueError(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		[number]
							while number != 1:
														if number % 2 == 0:
																					snake_case_     							=		math.floor(math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														else:
																					snake_case_     							=		math.floor(
																					    math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														sequence.append(_SCREAMING_SNAKE_CASE					)
							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()



