





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 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

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

                SCREAMING_SNAKE_CASE__											=			FlaxAutoModel.from_pretrained(A_     )
                self.assertIsNotNone(A_     )
                self.assertIsInstance(A_   ,				A_     )



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

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

                SCREAMING_SNAKE_CASE__											=			FlaxAutoModel.from_pretrained(A_     )
                self.assertIsNotNone(A_     )
                self.assertIsInstance(A_   ,				A_     )



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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

            eval(**A_     ).block_until_ready()



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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

            eval(**A_     ).block_until_ready()



    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

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




    def  lowercase_					(       self     ):





        '''simple docstring'''

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


'''simple docstring'''
from __future__ import annotations

lowerCAmelCase					:    Union[str, Any]           = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase					:    List[str]           = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     []
							UpperCamelCase   			=     len(A)

							for i in range(A):
														UpperCamelCase   			=     -1
														for j in range(i + 1		,     A):
																					if arr[i] < arr[j]:
																												UpperCamelCase   			=     arr[j]
																												break
														result.append(A)
							return result







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     []
							for i, outer in enumerate(A):
														UpperCamelCase   			=     -1
														for inner in arr[i + 1 :]:
																					if outer < inner:
																												UpperCamelCase   			=     inner
																												break
														result.append(A)
							return result







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     len(A)
							UpperCamelCase   			=     []
							UpperCamelCase   			=     [-1] * arr_size

							for index in reversed(range(A)):
														if stack:
																					while stack[-1] <= arr[index]:
																												stack.pop()
																												if not stack:
																																			break
														if stack:
																					UpperCamelCase   			=     stack[-1]
														stack.append(arr[index])
							return result


if __name__ == "__main__":
						from doctest import testmod
						from timeit import timeit

						testmod()
						print(next_greatest_element_slow(arr))
						print(next_greatest_element_fast(arr))
						print(next_greatest_element(arr))

						lowerCAmelCase					:    Optional[Any]           = (
						    'from __main__ import arr, next_greatest_element_slow, '
						    'next_greatest_element_fast, next_greatest_element'
						)
						print(
						    'next_greatest_element_slow():',
						    timeit('next_greatest_element_slow(arr)', setup=setup),
						)
						print(
						    'next_greatest_element_fast():',
						    timeit('next_greatest_element_fast(arr)', setup=setup),
						)
						print(
						    '     next_greatest_element():',
						    timeit('next_greatest_element(arr)', setup=setup),
						)




