

# Function to print upper half of diamond (pyramid)




def 					_lowercase     (				a_				:					List[str]				)				->	Union[str, Any]:




					'''simple docstring'''







					for i in range(0					,a_				):
										for _ in range(0					,n - i - 1				):  # printing spaces
															print(' '					,end=''				)
										for _ in range(0					,i + 1				):  # printing stars
															print('* '					,end=''				)
										print()




def 					_lowercase     (				a_				:					Dict				)				->	Any:




					'''simple docstring'''







					for i in range(a_					,0					,-1				):
										for _ in range(a_					,0					,-1				):  # printing stars
															print('* '					,end=''				)
										print()
										for _ in range(n - i + 1					,0					,-1				):  # printing spaces
															print(' '					,end=''				)




def 					_lowercase     (				a_				:					Tuple				)				->	Dict:




					'''simple docstring'''







					if n <= 0:
										print('       ...       ....        nothing printing :('				)
										return
					floyd(a_				)  # upper half
					reverse_floyd(a_				)  # lower half


if __name__ == "__main__":
						print(r"| /\ | |- |  |-  |--| |\  /| |-")
						print(r"|/  \| |- |_ |_  |__| | \/ | |_")
						A__						=							1
						while K:
												A__						=							int(input("enter the number and , and see the magic : "))
												print()
												pretty_print(user_number)
												A__						=							int(input("press 0 to exit... and 1 to continue..."))

						print("Good Bye...")







import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
						import jax

						from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
						from transformers.models.bert.modeling_flax_bert import FlaxBertModel
						from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel

@require_flax
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											with self.subTest(__UpperCamelCase      ):
																__magic_name__							=    AutoConfig.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )

																__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						for model_name in ["roberta-base", "roberta-large"]:
											with self.subTest(__UpperCamelCase      ):
																__magic_name__							=    AutoConfig.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )

																__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											__magic_name__							=    AutoTokenizer.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    FlaxBertModel.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    tokenizer('Do you support jax jitted function?'  , return_tensors=TensorType.JAX      )

											@jax.jit
											def eval(**__UpperCamelCase: Optional[Any]      ):
																return model(**__UpperCamelCase      )

											eval(**__UpperCamelCase      ).block_until_ready()







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						for model_name in ["roberta-base", "roberta-large"]:
											__magic_name__							=    AutoTokenizer.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    FlaxRobertaModel.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    tokenizer('Do you support jax jitted function?'  , return_tensors=TensorType.JAX      )

											@jax.jit
											def eval(**__UpperCamelCase: Any      ):
																return model(**__UpperCamelCase      )

											eval(**__UpperCamelCase      ).block_until_ready()







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , 'bert-base is not a local folder and is not a valid model identifier'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('bert-base'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase  , revision='aaaaaa'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , 'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'  , ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						with self.assertRaisesRegex(__UpperCamelCase  , 'Use `from_pt=True` to load this model'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'      )




