



'''simple docstring'''

def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'  )

		_a     :      Optional[int]        =							str(bin(lowerCAmelCase_  )  )
		binary_number += "0" * shift_amount
		return binary_number



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'  )

		_a     :      Tuple        =							str(bin(lowerCAmelCase_  )  )[2:]
		if shift_amount >= len(lowerCAmelCase_  ):
				return "0b0"
		_a     :      Optional[int]        =							binary_number[: len(lowerCAmelCase_  ) - shift_amount]
		return "0b" + shifted_binary_number



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      str:
		if number >= 0:  # Get binary representation of positive number
				_a     :      Optional[Any]        =							'0' + str(bin(lowerCAmelCase_  )  ).strip('-'  )[2:]
		else:  # Get binary (2's complement) representation of negative number
				_a     :      List[str]        =							len(bin(lowerCAmelCase_  )[3:]  )  # Find 2's complement of number
				_a     :      Optional[int]        =							bin(abs(lowerCAmelCase_  ) - (1 << binary_number_length)  )[3:]
				_a     :      Dict        =							(
				    '1' + '0' * (binary_number_length - len(lowerCAmelCase_  )) + binary_number
				)

		if shift_amount >= len(lowerCAmelCase_  ):
				return "0b" + binary_number[0] * len(lowerCAmelCase_  )
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(lowerCAmelCase_  ) - shift_amount]
		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()











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     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["bert-base-cased", "bert-large-uncased"]:
               with self.subTest(__lowerCamelCase ):
                      A__											=						AutoConfig.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

                      A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["roberta-base", "roberta-large"]:
               with self.subTest(__lowerCamelCase ):
                      A__											=						AutoConfig.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

                      A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["bert-base-cased", "bert-large-uncased"]:
               A__											=						AutoTokenizer.from_pretrained(__lowerCamelCase )
               A__											=						FlaxBertModel.from_pretrained(__lowerCamelCase )
               A__											=						tokenizer('''Do you support jax jitted function?''',return_tensors=TensorType.JAX )

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

               eval(**__lowerCamelCase ).block_until_ready()



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["roberta-base", "roberta-large"]:
               A__											=						AutoTokenizer.from_pretrained(__lowerCamelCase )
               A__											=						FlaxRobertaModel.from_pretrained(__lowerCamelCase )
               A__											=						tokenizer('''Do you support jax jitted function?''',return_tensors=TensorType.JAX )

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

               eval(**__lowerCamelCase ).block_until_ready()



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



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



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




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





