
"""simple docstring"""







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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__     =						"""platform"""

							import jax
							import jax.numpy as jnp

							from transformers import AutoTokenizer
							from transformers.models.mbart.modeling_flax_mbart import (
							    FlaxMBartForConditionalGeneration,
							    FlaxMBartForQuestionAnswering,
							    FlaxMBartForSequenceClassification,
							    FlaxMBartModel,
							    shift_tokens_right,
							)
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    ):
	if attention_mask is None:
		__lowerCAmelCase     :		int		     = np.where(input_ids != config.pad_token_id			,    1			,    0						)
	if decoder_attention_mask is None:
		__lowerCAmelCase     :		Any		     = np.where(decoder_input_ids != config.pad_token_id			,    1			,    0						)
	if head_mask is None:
		__lowerCAmelCase     :		Dict		     = np.ones((config.encoder_layers, config.encoder_attention_heads)						)
	if decoder_head_mask is None:
		__lowerCAmelCase     :		str		     = np.ones((config.decoder_layers, config.decoder_attention_heads)						)
	if cross_attn_head_mask is None:
		__lowerCAmelCase     :		Optional[int]		     = 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": decoder_attention_mask,
	}




class 					A__			(      unittest.TestCase):

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

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

			__lowerCAmelCase     :		Tuple		     = shift_tokens_right(_SCREAMING_SNAKE_CASE			,						1					)

			__lowerCAmelCase     :		Optional[Any]		     = MBartConfig(
			    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			,						decoder_start_token_id=self.decoder_start_token_id			,						initializer_range=self.initializer_range			,						use_cache=_SCREAMING_SNAKE_CASE			,						)
			__lowerCAmelCase     :		Dict		     = prepare_mbart_inputs_dict(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
			return config, inputs_dict

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		Union[str, Any]		     = self.prepare_config_and_inputs()
			return config, inputs_dict

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		int		     = 20
			__lowerCAmelCase     :		Optional[int]		     = model_class_name(_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		Tuple		     = model.encode(inputs_dict['input_ids']					)

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

			__lowerCAmelCase     :		str		     = model.init_cache(decoder_input_ids.shape[0]			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		List[str]		     = jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,						dtype='i4'					)

			__lowerCAmelCase     :		Optional[int]		     = jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]			,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,						)
			__lowerCAmelCase     :		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			,						)

			__lowerCAmelCase     :		Union[str, Any]		     = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,						dtype='i4'					)
			__lowerCAmelCase     :		List[str]		     = 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			,						)

			__lowerCAmelCase     :		Any		     = model.decode(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		List[str]		     = 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			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Optional[int]		     = 20
			__lowerCAmelCase     :		List[str]		     = model_class_name(_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		str		     = model.encode(inputs_dict['input_ids']					)

			__lowerCAmelCase     ,  __lowerCAmelCase     :		List[str]		     = (
			    inputs_dict['decoder_input_ids'],
			    inputs_dict['decoder_attention_mask'],
			)

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

			__lowerCAmelCase     :		Tuple		     = model.init_cache(decoder_input_ids.shape[0]			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Union[str, Any]		     = jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]			,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,						)

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

			__lowerCAmelCase     :		Optional[int]		     = model.decode(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						decoder_attention_mask=_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		Dict		     = 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__			(      unittest.TestCase):
		A_		:			Union[str, Any]							  =      9_9

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Dict		     = np.array(
			    [
			        [71, 82, 18, 33, 46, 91, 2],
			        [68, 34, 26, 58, 30, 82, 2],
			        [5, 97, 17, 39, 94, 40, 2],
			        [76, 83, 94, 25, 70, 78, 2],
			        [87, 59, 41, 35, 48, 66, 2],
			        [55, 13, 16, 58, 5, 2, 1],  # note padding
			        [64, 27, 31, 51, 12, 75, 2],
			        [52, 64, 86, 17, 83, 39, 2],
			        [48, 61, 9, 24, 71, 82, 2],
			        [26, 1, 60, 48, 22, 13, 2],
			        [21, 5, 62, 28, 14, 76, 2],
			        [45, 98, 37, 86, 59, 48, 2],
			        [70, 70, 50, 9, 28, 0, 2],
			    ]			,						dtype=np.intaa			,						)

			__lowerCAmelCase     :		Union[str, Any]		     = input_ids.shape[0]
			__lowerCAmelCase     :		Dict		     = MBartConfig(
			    vocab_size=self.vocab_size			,						d_model=24			,						encoder_layers=2			,						decoder_layers=2			,						encoder_attention_heads=2			,						decoder_attention_heads=2			,						encoder_ffn_dim=32			,						decoder_ffn_dim=32			,						max_position_embeddings=48			,						eos_token_id=2			,						pad_token_id=1			,						bos_token_id=0			,						)
			return config, input_ids, batch_size

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     :		Dict		     = self._get_config_and_data()
			__lowerCAmelCase     :		Any		     = FlaxMBartForSequenceClassification(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Dict		     = model(input_ids=_SCREAMING_SNAKE_CASE			,						decoder_input_ids=_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		List[str]		     = (batch_size, config.num_labels)
			self.assertEqual(outputs['logits'].shape			,						_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     :		Tuple		     = self._get_config_and_data()
			__lowerCAmelCase     :		Optional[Any]		     = FlaxMBartForQuestionAnswering(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Dict		     = model(input_ids=_SCREAMING_SNAKE_CASE					)

			self.assertEqual(outputs['start_logits'].shape			,						input_ids.shape					)
			self.assertEqual(outputs['end_logits'].shape			,						input_ids.shape					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     :		Tuple		     = self._get_config_and_data()
			__lowerCAmelCase     :		List[Any]		     = FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		int		     = lm_model(input_ids=_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Optional[Any]		     = (batch_size, input_ids.shape[1], config.vocab_size)
			self.assertEqual(outputs['logits'].shape			,						_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Union[str, Any]		     = MBartConfig(
			    vocab_size=self.vocab_size			,						d_model=14			,						encoder_layers=2			,						decoder_layers=2			,						encoder_attention_heads=2			,						decoder_attention_heads=2			,						encoder_ffn_dim=8			,						decoder_ffn_dim=8			,						max_position_embeddings=48			,						)
			__lowerCAmelCase     :		Optional[int]		     = FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		str		     = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,						dtype=np.intaa					)
			__lowerCAmelCase     :		Union[str, Any]		     = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,						dtype=np.intaa					)
			__lowerCAmelCase     :		int		     = lm_model(input_ids=_SCREAMING_SNAKE_CASE			,						decoder_input_ids=_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Optional[Any]		     = (*summary.shape, config.vocab_size)
			self.assertEqual(outputs['logits'].shape			,						_SCREAMING_SNAKE_CASE					)







		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		int		     = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,						dtype=np.intaa					)
			__lowerCAmelCase     :		Tuple		     = shift_tokens_right(_SCREAMING_SNAKE_CASE			,						1					)
			__lowerCAmelCase     :		List[Any]		     = np.equal(_SCREAMING_SNAKE_CASE			,						1					).astype(np.floataa					).sum()
			__lowerCAmelCase     :		Tuple		     = np.equal(_SCREAMING_SNAKE_CASE			,						1					).astype(np.floataa					).sum()
			self.assertEqual(shifted.shape			,						input_ids.shape					)
			self.assertEqual(_SCREAMING_SNAKE_CASE			,						n_pad_before - 1					)
			self.assertTrue(np.equal(shifted[:, 0]			,						2					).all()					)




@require_flax
class 					A__			(      _lowerCamelCase			,	unittest.TestCase			,	_lowerCamelCase):
		A_		:			str							  =      True
		A_		:			Union[str, Any]							  =      (
		    (
		        FlaxMBartModel,
		        FlaxMBartForConditionalGeneration,
		        FlaxMBartForSequenceClassification,
		        FlaxMBartForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)
		A_		:			str							  =      (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = FlaxMBartModelTester(self					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		int		     = self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
				self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		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 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		Tuple		     = self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				with self.subTest(model_class.__name__					):
					__lowerCAmelCase     :		List[Any]		     = self._prepare_for_class(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
					__lowerCAmelCase     :		str		     = model_class(_SCREAMING_SNAKE_CASE					)

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

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

					with self.subTest('JIT Disabled'					):
						with jax.disable_jit():
							__lowerCAmelCase     :		int		     = 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 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		List[Any]		     = self.model_tester.prepare_config_and_inputs_for_common()

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

					__lowerCAmelCase     :		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(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
						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'					):
						__lowerCAmelCase     :		Tuple		     = decode_jitted(**_SCREAMING_SNAKE_CASE					).to_tuple()

					with self.subTest('JIT Disabled'					):
						with jax.disable_jit():
							__lowerCAmelCase     :		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 				__lowerCamelCase				( self					):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase     :		Tuple		     = model_class_name.from_pretrained('facebook/mbart-large-cc25'			,						from_pt=_SCREAMING_SNAKE_CASE					)
				# FlaxMBartForSequenceClassification expects eos token in input_ids
				__lowerCAmelCase     :		Optional[int]		     = np.ones((1, 1)					) * model.config.eos_token_id
				__lowerCAmelCase     :		Optional[int]		     = model(_SCREAMING_SNAKE_CASE					)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)




@require_flax
@require_sentencepiece
@require_tokenizers
class 					A__			(      unittest.TestCase):
		A_		:			str							  =      [
		    ' UN Chief Says There Is No Military Solution in Syria',
		]
		A_		:			int							  =      [
		    'Şeful ONU declară că nu există o soluţie militară în Siria',
		]
		A_		:			Any							  =      'facebook/mbart-large-en-ro'

		@cached_property
		def 				__lowerCamelCase				( self					):
			return AutoTokenizer.from_pretrained(self.model_name					)

		@cached_property
		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Any		     = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name			,						from_pt=_SCREAMING_SNAKE_CASE					)
			return model

		def 				__lowerCamelCase				( self			,						**_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Optional[Any]		     = self.translate_src_text(**_SCREAMING_SNAKE_CASE					)
			self.assertListEqual(self.expected_text			,						_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						**_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Dict		     = self.tokenizer(self.src_text			,						**_SCREAMING_SNAKE_CASE			,						return_tensors='np'					)
			__lowerCAmelCase     :		Union[str, Any]		     = self.model.generate(
			    model_inputs.input_ids			,						attention_mask=model_inputs.attention_mask			,						decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']			,						early_stopping=_SCREAMING_SNAKE_CASE			,						num_beams=2			,						).sequences
			__lowerCAmelCase     :		Tuple		     = self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			,						skip_special_tokens=_SCREAMING_SNAKE_CASE					)
			return generated_words







		@slow
		def 				__lowerCamelCase				( self					):
			self._assert_generated_batch_equal_expected()

"""simple docstring"""







def        __lowerCAmelCase							(_UpperCamelCase = 10**6						):

	if n <= 0:
		raise ValueError('Please enter an integer greater than 0'						)

	__lowerCAmelCase     :		Optional[Any]		     = list(range(n + 1						)						)
	for number in range(2			,    n + 1						):
		if phi[number] == number:
			phi[number] -= 1
			for multiple in range(number * 2			,    n + 1			,    _UpperCamelCase						):
				__lowerCAmelCase     :		Tuple		     = (phi[multiple] // number) * (number - 1)

	__lowerCAmelCase     :		Any		     = 1
	for number in range(1			,    n + 1						):
		if (answer / phi[answer]) < (number / phi[number]):
			__lowerCAmelCase     :		List[Any]		     = number

	return answer


if __name__ == "__main__":
							print(solution())