

from ..utils import DummyObject, requires_backends



class   __lowercase				( metaclass=UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[Any]										=     ['''torch''', '''transformers''', '''onnx''']



			def __init__( self  :     Dict   ,						*lowerCAmelCase__  :     List[str]   ,						**lowerCAmelCase__  :     List[Any]):
									requires_backends(self   ,						["torch", "transformers", "onnx"])



			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     List[str]   ,						*lowerCAmelCase__  :     List[Any]   ,						**lowerCAmelCase__  :     str):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])




			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     List[str]   ,						*lowerCAmelCase__  :     Optional[int]   ,						**lowerCAmelCase__  :     Union[str, Any]):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])







class   __lowercase				( metaclass=UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Dict										=     ['''torch''', '''transformers''', '''onnx''']



			def __init__( self  :     int   ,						*lowerCAmelCase__  :     str   ,						**lowerCAmelCase__  :     Dict):
									requires_backends(self   ,						["torch", "transformers", "onnx"])



			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     Optional[int]   ,						*lowerCAmelCase__  :     Any   ,						**lowerCAmelCase__  :     Optional[Any]):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])




			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     Union[str, Any]   ,						*lowerCAmelCase__  :     int   ,						**lowerCAmelCase__  :     List[str]):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])







class   __lowercase				( metaclass=UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Union[str, Any]										=     ['''torch''', '''transformers''', '''onnx''']



			def __init__( self  :     List[Any]   ,						*lowerCAmelCase__  :     Dict   ,						**lowerCAmelCase__  :     List[Any]):
									requires_backends(self   ,						["torch", "transformers", "onnx"])



			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     Optional[int]   ,						*lowerCAmelCase__  :     int   ,						**lowerCAmelCase__  :     Optional[Any]):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])




			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     Union[str, Any]   ,						*lowerCAmelCase__  :     List[Any]   ,						**lowerCAmelCase__  :     List[Any]):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])







class   __lowercase				( metaclass=UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							int										=     ['''torch''', '''transformers''', '''onnx''']



			def __init__( self  :     Union[str, Any]   ,						*lowerCAmelCase__  :     Tuple   ,						**lowerCAmelCase__  :     Optional[Any]):
									requires_backends(self   ,						["torch", "transformers", "onnx"])



			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     Dict   ,						*lowerCAmelCase__  :     Union[str, Any]   ,						**lowerCAmelCase__  :     Optional[Any]):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])




			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     Tuple   ,						*lowerCAmelCase__  :     int   ,						**lowerCAmelCase__  :     Any):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])







class   __lowercase				( metaclass=UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[Any]										=     ['''torch''', '''transformers''', '''onnx''']



			def __init__( self  :     Any   ,						*lowerCAmelCase__  :     int   ,						**lowerCAmelCase__  :     Any):
									requires_backends(self   ,						["torch", "transformers", "onnx"])



			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     Union[str, Any]   ,						*lowerCAmelCase__  :     List[Any]   ,						**lowerCAmelCase__  :     Tuple):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])




			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     Any   ,						*lowerCAmelCase__  :     Optional[Any]   ,						**lowerCAmelCase__  :     Union[str, Any]):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])







class   __lowercase				( metaclass=UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[int]										=     ['''torch''', '''transformers''', '''onnx''']



			def __init__( self  :     Any   ,						*lowerCAmelCase__  :     int   ,						**lowerCAmelCase__  :     Tuple):
									requires_backends(self   ,						["torch", "transformers", "onnx"])



			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     Optional[int]   ,						*lowerCAmelCase__  :     Optional[int]   ,						**lowerCAmelCase__  :     List[Any]):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])




			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     Any   ,						*lowerCAmelCase__  :     str   ,						**lowerCAmelCase__  :     Union[str, Any]):
									requires_backends(cls   ,						["torch", "transformers", "onnx"])









import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
						import os

						import jax.numpy as jnp
						from jax import jit

						from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
						from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

						lowerCAmelCase							:  Any   	=						"""0.12"""  # assumed parallelism: 8


if is_torch_available():
						import torch







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None    ):
						if rng is None:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       random.Random()

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       1
						for dim in shape:
												total_dims *= dim

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []
						for _ in range(_UpperCAmelCase    ):
												values.append(rng.randint(0				,	vocab_size - 1    )    )

						SCREAMING_SNAKE_CASE_: List[Any]  		=       np.array(_UpperCAmelCase				,	dtype=jnp.intaa    ).reshape(_UpperCAmelCase    )

						return output







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=None    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       ids_tensor(_UpperCAmelCase				,	vocab_size=2				,	rng=_UpperCAmelCase    )
						# make sure that at least one token is attended to for each batch
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       1
						return attn_mask



@require_flax
class   __lowercase				:




			"""simple docstring"""







			_UpperCAmelCase   :							Any										=     None
			_UpperCAmelCase   :							List[Any]										=     ()



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.model_tester.prepare_config_and_inputs_for_common()

									# cut to half length & take max batch_size 3
									SCREAMING_SNAKE_CASE_: str  		=       2
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       inputs["input_ids"].shape[-1] // 2
									SCREAMING_SNAKE_CASE_: List[str]  		=       inputs["input_ids"][:max_batch_size, :sequence_length]

									SCREAMING_SNAKE_CASE_: Any  		=       jnp.ones_like(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       attention_mask[:max_batch_size, :sequence_length]

									# generate max 5 tokens
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       input_ids.shape[-1] + 5
									if config.eos_token_id is not None and config.pad_token_id is None:
															# hack to allow generate for models such as GPT2 as is done in `generate()`
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       config.eos_token_id
									return config, input_ids, attention_mask, max_length



			@is_pt_flax_cross_test
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       False
									SCREAMING_SNAKE_CASE_: Dict  		=       max_length
									SCREAMING_SNAKE_CASE_: List[Any]  		=       0

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: int  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
															SCREAMING_SNAKE_CASE_: List[Any]  		=       getattr(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pt_model_class(lowerCAmelCase__).eval()
															SCREAMING_SNAKE_CASE_: str  		=       load_flax_weights_in_pytorch_model(lowerCAmelCase__   ,						flax_model.params)

															SCREAMING_SNAKE_CASE_: List[Any]  		=       flax_model.generate(lowerCAmelCase__).sequences
															SCREAMING_SNAKE_CASE_: str  		=       pt_model.generate(torch.tensor(lowerCAmelCase__   ,						dtype=torch.long))

															if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

															self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,						flax_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       True
									SCREAMING_SNAKE_CASE_: Dict  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: List[str]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Dict  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: int  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       2

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: List[str]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Any  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: str  		=       False
									SCREAMING_SNAKE_CASE_: int  		=       max_length
									SCREAMING_SNAKE_CASE_: str  		=       2
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       2

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: str  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[0]   ,						input_ids.shape[0] * config.num_return_sequences)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Tuple  		=       True
									SCREAMING_SNAKE_CASE_: List[str]  		=       max_length
									SCREAMING_SNAKE_CASE_: Any  		=       0.8
									SCREAMING_SNAKE_CASE_: Any  		=       10
									SCREAMING_SNAKE_CASE_: List[str]  		=       0.3
									SCREAMING_SNAKE_CASE_: Tuple  		=       1
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       8
									SCREAMING_SNAKE_CASE_: int  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: List[str]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Dict  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Any  		=       max_length
									SCREAMING_SNAKE_CASE_: int  		=       1
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       8
									SCREAMING_SNAKE_CASE_: List[Any]  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: int  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[str]  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Any  		=       max_length
									SCREAMING_SNAKE_CASE_: List[str]  		=       2
									SCREAMING_SNAKE_CASE_: str  		=       1
									SCREAMING_SNAKE_CASE_: Tuple  		=       8
									SCREAMING_SNAKE_CASE_: List[Any]  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[str]  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_: Dict  		=       attention_mask.at[(0, 0)].set(0)

									SCREAMING_SNAKE_CASE_: Dict  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Any  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: List[Any]  		=       model.generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_: List[Any]  		=       attention_mask.at[(0, 0)].set(0)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       True
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: str  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Dict  		=       model.generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_: Dict  		=       attention_mask.at[(0, 0)].set(0)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       2
									SCREAMING_SNAKE_CASE_: Any  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model.generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())







@require_flax
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert")
									SCREAMING_SNAKE_CASE_: List[Any]  		=       FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only")

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       "Hello world"
									SCREAMING_SNAKE_CASE_: List[Any]  		=       tokenizer(lowerCAmelCase__   ,						return_tensors="np").input_ids

									# typos are quickly detected (the correct argument is `do_sample`)
									with self.assertRaisesRegex(lowerCAmelCase__   ,						"do_samples"):
															model.generate(lowerCAmelCase__   ,						do_samples=lowerCAmelCase__)

									# arbitrary arguments that will not be used anywhere are also not accepted
									with self.assertRaisesRegex(lowerCAmelCase__   ,						"foo"):
															SCREAMING_SNAKE_CASE_: str  		=       {"foo": "bar"}
															model.generate(lowerCAmelCase__   ,						**lowerCAmelCase__)






