
'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel






@require_tf
class  lowerCAmelCase__   :
					lowerCAmelCase_        =     MBartConfig
					lowerCAmelCase_        =     {}
					lowerCAmelCase_        =     '''gelu'''






					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=32				,							__SCREAMING_SNAKE_CASE=2				,							__SCREAMING_SNAKE_CASE=4				,							__SCREAMING_SNAKE_CASE=37				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=20				,							__SCREAMING_SNAKE_CASE=2				,							__SCREAMING_SNAKE_CASE=1				,							__SCREAMING_SNAKE_CASE=0				,							):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       ids_tensor([self.batch_size, self.seq_length - 1]				,							self.vocab_size		)
											lowercase_	:     Any													=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)				,							1		)
											lowercase_	:     List[str]													=       tf.concat([input_ids, eos_tensor]				,							axis=1		)

											lowercase_	:     Dict													=       ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size		)

											lowercase_	:     Any													=       self.config_cls(
											    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_ids=[2]				,							bos_token_id=self.bos_token_id				,							pad_token_id=self.pad_token_id				,							decoder_start_token_id=self.pad_token_id				,							**self.config_updates				,							)
											lowercase_	:     str													=       prepare_mbart_inputs_dict(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											return config, inputs_dict






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       TFMBartModel(config=__SCREAMING_SNAKE_CASE		).get_decoder()
											lowercase_	:     Dict													=       inputs_dict['''input_ids''']

											lowercase_	:     Tuple													=       input_ids[:1, :]
											lowercase_	:     str													=       inputs_dict['''attention_mask'''][:1, :]
											lowercase_	:     Optional[int]													=       inputs_dict['''head_mask''']
											lowercase_	:     Union[str, Any]													=       1

											# first forward pass
											lowercase_	:     str													=       model(__SCREAMING_SNAKE_CASE				,							attention_mask=__SCREAMING_SNAKE_CASE				,							head_mask=__SCREAMING_SNAKE_CASE				,							use_cache=__SCREAMING_SNAKE_CASE		)

											lowercase_						, lowercase_	:     Optional[int]													=       outputs.to_tuple()
											lowercase_	:     Tuple													=       past_key_values[1]







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: List[Any]=None      ,      __SCREAMING_SNAKE_CASE					: Dict=None      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]=None      ,      __SCREAMING_SNAKE_CASE					: int=None      ,      __SCREAMING_SNAKE_CASE					: List[str]=None      ,      ):



						"""simple docstring"""





						if attention_mask is None:
												lowercase_	:     Optional[int]													=       tf.cast(tf.math.not_equal(__SCREAMING_SNAKE_CASE      ,      config.pad_token_id							)      ,      tf.inta							)
						if decoder_attention_mask is None:
												lowercase_	:     str													=       tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape      ,      dtype=tf.inta							),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]      ,      config.pad_token_id							)      ,      tf.inta							),
												    ]      ,      axis=-1      ,      )
						if head_mask is None:
												lowercase_	:     List[str]													=       tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
						if decoder_head_mask is None:
												lowercase_	:     Any													=       tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
						if cross_attn_head_mask is None:
												lowercase_	:     List[str]													=       tf.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,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}






@require_tf
class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
					lowerCAmelCase_        =     (TFMBartForConditionalGeneration,) if is_tf_available() else ()
					lowerCAmelCase_        =     (
					    {
					        '''conversational''': TFMBartForConditionalGeneration,
					        '''feature-extraction''': TFMBartModel,
					        '''summarization''': TFMBartForConditionalGeneration,
					        '''text2text-generation''': TFMBartForConditionalGeneration,
					        '''translation''': TFMBartForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase_        =     True
					lowerCAmelCase_        =     False
					lowerCAmelCase_        =     False






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																	# Exception encountered when calling layer '...'
																	return True

											return False






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       TFMBartModelTester(self		)
											lowercase_	:     Union[str, Any]													=       ConfigTester(self				,							config_class=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.config_tester.run_common_tests()






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE		)







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






					@cached_property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return AutoTokenizer.from_pretrained(self.model_name		)






					@cached_property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Any													=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
											return model






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       self.translate_src_text(**__SCREAMING_SNAKE_CASE		)
											self.assertListEqual(self.expected_text				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       self.tokenizer(self.src_text				,							**__SCREAMING_SNAKE_CASE				,							return_tensors='''tf'''		)
											lowercase_	:     str													=       self.model.generate(
											    model_inputs.input_ids				,							attention_mask=model_inputs.attention_mask				,							num_beams=2		)
											lowercase_	:     List[str]													=       self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE				,							skip_special_tokens=__SCREAMING_SNAKE_CASE		)
											return generated_words






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											self._assert_generated_batch_equal_expected()






import math


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						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(SCREAMING_SNAKE_CASE__       ) + 1       )  ,		6       ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 10001       ):

						try:
												snake_case_   =	int(SCREAMING_SNAKE_CASE__       )
						except (TypeError, ValueError):
												raise TypeError('''Parameter nth must be int or castable to int.'''       ) from None
						if nth <= 0:
												raise ValueError('''Parameter nth must be greater than or equal to one.'''       )
						snake_case_   =	[]
						snake_case_   =	2
						while len(SCREAMING_SNAKE_CASE__       ) < nth:
												if is_prime(SCREAMING_SNAKE_CASE__       ):
																		primes.append(SCREAMING_SNAKE_CASE__       )
																		num += 1
												else:
																		num += 1
						return primes[len(SCREAMING_SNAKE_CASE__       ) - 1]


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