"""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			:
				_snake_case     =  MBartConfig
				_snake_case     =  {}
				_snake_case     =  "gelu"







				def __init__( self    :       Union[str, Any]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       str=1_3			,					lowerCamelCase_    :       Dict=7			,					lowerCamelCase_    :       int=True			,					lowerCamelCase_    :       str=False			,					lowerCamelCase_    :       Dict=9_9			,					lowerCamelCase_    :       Dict=3_2			,					lowerCamelCase_    :       str=2			,					lowerCamelCase_    :       Optional[Any]=4			,					lowerCamelCase_    :       Dict=3_7			,					lowerCamelCase_    :       str=0.1			,					lowerCamelCase_    :       List[str]=0.1			,					lowerCamelCase_    :       str=2_0			,					lowerCamelCase_    :       Optional[int]=2			,					lowerCamelCase_    :       Optional[Any]=1			,					lowerCamelCase_    :       Union[str, Any]=0			,					):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






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

					_lowercase :    Tuple							=							ids_tensor([self.batch_size, self.seq_length]			,					self.vocab_size						)

					_lowercase :    Tuple							=							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 :    Tuple							=							prepare_mbart_inputs_dict(_lowerCamelCase			,					_lowerCamelCase			,					_lowerCamelCase						)
					return config, inputs_dict







				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       List[Any]						):

					"""simple docstring"""






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

					_lowercase :    Optional[int]							=							input_ids[:1, :]
					_lowercase :    List[Any]							=							inputs_dict['attention_mask'][:1, :]
					_lowercase :    List[Any]							=							inputs_dict['head_mask']
					_lowercase :    Any							=							1

					# first forward pass
					_lowercase :    Dict							=							model(_lowerCamelCase			,					attention_mask=_lowerCamelCase			,					head_mask=_lowerCamelCase			,					use_cache=_lowerCamelCase						)

					_lowercase				,      _lowercase :    Dict							=							outputs.to_tuple()
					_lowercase :    int							=							past_key_values[1]



def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None     ,):






	"""simple docstring"""





	if attention_mask is None:
		_lowercase :    List[Any]							=							tf.cast(tf.math.not_equal(_lowerCAmelCase     ,config.pad_token_id			)     ,tf.inta			)
	if decoder_attention_mask is None:
		_lowercase :    List[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 :    Union[str, Any]							=							tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
	if decoder_head_mask is None:
		_lowercase :    Optional[int]							=							tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
	if cross_attn_head_mask is None:
		_lowercase :    List[Any]							=							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			(__UpperCAmelCase						,							__UpperCAmelCase						,							unittest.TestCase     ):
				_snake_case     =  (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
				_snake_case     =  (TFMBartForConditionalGeneration,) if is_tf_available() else ()
				_snake_case     =  (
				    {
				        "conversational": TFMBartForConditionalGeneration,
				        "feature-extraction": TFMBartModel,
				        "summarization": TFMBartForConditionalGeneration,
				        "text2text-generation": TFMBartForConditionalGeneration,
				        "translation": TFMBartForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				_snake_case     =  True
				_snake_case     =  False
				_snake_case     =  False







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       Union[str, Any]			,					lowerCamelCase_    :       Union[str, Any]			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       str						):

					"""simple docstring"""






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

					return False







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    str							=							TFMBartModelTester(self						)
					_lowercase :    Optional[Any]							=							ConfigTester(self			,					config_class=_lowerCamelCase						)







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					self.config_tester.run_common_tests()







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    Dict							=							self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase						)



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







				@cached_property
				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					return AutoTokenizer.from_pretrained(self.model_name						)







				@cached_property
				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					_lowercase :    int							=							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
					return model







				def 							__UpperCAmelCase					( self    :       Dict			,					**lowerCamelCase_    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							self.translate_src_text(**_lowerCamelCase						)
					self.assertListEqual(self.expected_text			,					_lowerCamelCase						)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]			,					**lowerCamelCase_    :       Optional[Any]						):

					"""simple docstring"""






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







				@slow
				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					self._assert_generated_batch_equal_expected()




"""simple docstring"""







from decimal import Decimal, getcontext
from math import ceil, factorial


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	if not isinstance(__UpperCAmelCase     ,__UpperCAmelCase			):
		raise TypeError('Undefined for non-integers'			)
	elif precision < 1:
		raise ValueError('Undefined for non-natural numbers'			)

	_lowercase :    Optional[Any]							=							precision
	_lowercase :    Dict							=							ceil(precision / 14			)
	_lowercase :    int							=							426_880 * Decimal(10_005			).sqrt()
	_lowercase :    Optional[Any]							=							1
	_lowercase :    Union[str, Any]							=							13_591_409
	_lowercase :    Optional[int]							=							Decimal(__UpperCAmelCase			)
	for k in range(1     ,__UpperCAmelCase			):
		_lowercase :    List[str]							=							factorial(6 * k			) // (factorial(3 * k			) * factorial(__UpperCAmelCase			) ** 3)
		linear_term += 545_140_134
		exponential_term *= -262_537_412_640_768_000
		partial_sum += Decimal(multinomial_term * linear_term			) / exponential_term
	return str(constant_term / partial_sum			)[:-1]


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						50
						print(f"""The first {n} digits of pi is: {pi(n)}""")



