


'''simple docstring'''






import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
							import jax

							from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class 						__UpperCamelCase	(						unittest.TestCase							):





							def __init__(  self		:Optional[int]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:List[str]=1_3  ,_UpperCamelCase		:Optional[Any]=3_0  ,_UpperCamelCase		:Dict=2  ,_UpperCamelCase		:List[Any]=3  ,_UpperCamelCase		:List[str]=True  ,_UpperCamelCase		:str=True  ,_UpperCamelCase		:Optional[int]=3_2  ,_UpperCamelCase		:Dict=5  ,_UpperCamelCase		:Optional[int]=4  ,_UpperCamelCase		:List[Any]=3_7  ,_UpperCamelCase		:Union[str, Any]="gelu"  ,_UpperCamelCase		:List[Any]=0.1  ,_UpperCamelCase		:Any=0.1  ,_UpperCamelCase		:str=1_0  ,_UpperCamelCase		:Dict=0.02  ,):
									snake_case_		:							List[Any]			       =						parent
									snake_case_		:							Union[str, Any]			       =						batch_size
									snake_case_		:							Dict			       =						image_size
									snake_case_		:							Dict			       =						patch_size
									snake_case_		:							Optional[int]			       =						num_channels
									snake_case_		:							List[Any]			       =						is_training
									snake_case_		:							Tuple			       =						use_labels
									snake_case_		:							Dict			       =						hidden_size
									snake_case_		:							str			       =						num_hidden_layers
									snake_case_		:							List[Any]			       =						num_attention_heads
									snake_case_		:							Dict			       =						intermediate_size
									snake_case_		:							Union[str, Any]			       =						hidden_act
									snake_case_		:							Dict			       =						hidden_dropout_prob
									snake_case_		:							Union[str, Any]			       =						attention_probs_dropout_prob
									snake_case_		:							List[Any]			       =						type_sequence_label_size
									snake_case_		:							Tuple			       =						initializer_range

									# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									snake_case_		:							int			       =						(image_size // patch_size) ** 2
									snake_case_		:							int			       =						num_patches + 1





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							List[str]			       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

									snake_case_		:							List[Any]			       =						ViTConfig(
									    image_size=self.image_size  ,patch_size=self.patch_size  ,num_channels=self.num_channels  ,hidden_size=self.hidden_size  ,num_hidden_layers=self.num_hidden_layers  ,num_attention_heads=self.num_attention_heads  ,intermediate_size=self.intermediate_size  ,hidden_act=self.hidden_act  ,hidden_dropout_prob=self.hidden_dropout_prob  ,attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,is_decoder=_UpperCamelCase  ,initializer_range=self.initializer_range  ,)

									return config, pixel_values





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Optional[Any]			):
									snake_case_		:							List[str]			       =						FlaxViTModel(config=_UpperCamelCase			)
									snake_case_		:							int			       =						model(_UpperCamelCase			)
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									snake_case_		:							List[Any]			       =						(self.image_size, self.image_size)
									snake_case_		:							Optional[Any]			       =						(self.patch_size, self.patch_size)
									snake_case_		:							Dict			       =						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, num_patches + 1, self.hidden_size)			)





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Dict			):
									snake_case_		:							List[Any]			       =						self.type_sequence_label_size
									snake_case_		:							Optional[Any]			       =						FlaxViTForImageClassification(config=_UpperCamelCase			)
									snake_case_		:							List[str]			       =						model(_UpperCamelCase			)
									self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size)			)

									# test greyscale images
									snake_case_		:							Optional[int]			       =						1
									snake_case_		:							str			       =						FlaxViTForImageClassification(_UpperCamelCase			)

									snake_case_		:							Any			       =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
									snake_case_		:							Optional[Any]			       =						model(_UpperCamelCase			)





							def 		a__   (  self		:int			):
									snake_case_		:							List[str]			       =						self.prepare_config_and_inputs()
									(
									    (
									    snake_case_     
									)       ,      (
									    snake_case_     
									)       ,           
									)		:							List[str]			       =						config_and_inputs
									snake_case_		:							List[str]			       =						{"""pixel_values""": pixel_values}
									return config, inputs_dict





@require_flax
class 						__UpperCamelCase	(						_snake_case						,  unittest.TestCase							):
							lowercase			:		Any   		=  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Dict			       =						FlaxViTModelTester(self			)
									snake_case_		:							Optional[Any]			       =						ConfigTester(self  ,config_class=_UpperCamelCase  ,has_text_modality=_UpperCamelCase  ,hidden_size=3_7			)





							def 		a__   (  self		:Tuple			):
									self.config_tester.run_common_tests()





							def 		a__   (  self		:List[str]			):
									snake_case_		:							Optional[int]			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase			)





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Any			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase			)





							def 		a__   (  self		:List[Any]			):
									snake_case_       ,      snake_case_		:							Union[str, Any]			       =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											snake_case_		:							List[str]			       =						model_class(_UpperCamelCase			)
											snake_case_		:							List[str]			       =						inspect.signature(model.__call__			)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											snake_case_		:							Any			       =						[*signature.parameters.keys()]

											snake_case_		:							Any			       =						["""pixel_values"""]
											self.assertListEqual(arg_names[:1]  ,_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]			):
									snake_case_       ,      snake_case_		:							int			       =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__			):
													snake_case_		:							Any			       =						self._prepare_for_class(_UpperCamelCase  ,_UpperCamelCase			)
													snake_case_		:							Tuple			       =						model_class(_UpperCamelCase			)

													@jax.jit
													def model_jitted(_UpperCamelCase		:int  ,**_UpperCamelCase		:Union[str, Any]			):
															return model(pixel_values=_UpperCamelCase  ,**_UpperCamelCase			)

													with self.subTest("""JIT Enabled"""			):
															snake_case_		:							Any			       =						model_jitted(**_UpperCamelCase			).to_tuple()

													with self.subTest("""JIT Disabled"""			):
															with jax.disable_jit():
																	snake_case_		:							str			       =						model_jitted(**_UpperCamelCase			).to_tuple()

													self.assertEqual(len(_UpperCamelCase			)  ,len(_UpperCamelCase			)			)
													for jitted_output, output in zip(_UpperCamelCase  ,_UpperCamelCase			):
															self.assertEqual(jitted_output.shape  ,output.shape			)





							@slow
							def 		a__   (  self		:Optional[Any]			):

									for model_class_name in self.all_model_classes:
											snake_case_		:							Tuple			       =						model_class_name.from_pretrained("""google/vit-base-patch16-224"""			)
											snake_case_		:							Optional[int]			       =						model(np.ones((1, 3, 2_2_4, 2_2_4)			)			)
											self.assertIsNotNone(_UpperCamelCase			)



'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		return " ".join(
		    """""".join(word[::-1] ) if len(lowerCamelCase_ ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(reverse_long_words('Hey wollef sroirraw'))