



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

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



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

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



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

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

													eval(**UpperCAmelCase__		).block_until_ready()



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

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

													eval(**UpperCAmelCase__		).block_until_ready()



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



	def 			__A							(       self		):
							with self.assertRaisesRegex(
							    UpperCAmelCase__			,	R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"		):
													A__							=       FlaxAutoModel.from_pretrained(UpperCAmelCase__			,	revision="aaaaaa"		)



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










def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       str:





						"""simple docstring"""





						A__							=       len(_A    )
						A__							=       len(_A    )
						A__							=       (
						    first_str_length if first_str_length > second_str_length else second_str_length
						)
						A__							=       []
						for char_count in range(_A    ):
												if char_count < first_str_length:
																		output_list.append(first_str[char_count]    )
												if char_count < second_str_length:
																		output_list.append(second_str[char_count]    )
						return "".join(_A    )


if __name__ == "__main__":
						print(alternative_string_arrange("AB", "XYZ"), end=" ")





