

'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor





class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def __init__(       self     :		Optional[int]		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		str=7		,					UpperCAmelCase__     :		Tuple=3		,					UpperCAmelCase__     :		Optional[Any]=18		,					UpperCAmelCase__     :		Union[str, Any]=30		,					UpperCAmelCase__     :		List[Any]=400		,					UpperCAmelCase__     :		List[Any]=True		,					UpperCAmelCase__     :		Optional[Any]=None		,					UpperCAmelCase__     :		Union[str, Any]=True		,					UpperCAmelCase__     :		List[str]=None		,					UpperCAmelCase__     :		Dict=True		,					):
										'''simple docstring'''
										lowercase :							int    		=size if size is not None else {'''shortest_edge''': 20}
										lowercase :							List[Any]    		=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
										lowercase :							Optional[Any]    		=parent
										lowercase :							List[Any]    		=batch_size
										lowercase :							str    		=num_channels
										lowercase :							Dict    		=image_size
										lowercase :							List[Any]    		=min_resolution
										lowercase :							Dict    		=max_resolution
										lowercase :							Dict    		=do_resize
										lowercase :							Union[str, Any]    		=size
										lowercase :							Dict    		=do_center_crop
										lowercase :							Optional[Any]    		=crop_size
										lowercase :							Any    		=do_flip_channel_order







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_flip_channel_order": self.do_flip_channel_order,
										}





@require_torch
@require_vision
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    MobileViTImageProcessor if is_vision_available() else None







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=MobileViTImageProcessingTester(self   )







				@property
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_resize'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''size'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_center_crop'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''center_crop'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_flip_channel_order'''   )   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size		,					{'''shortest_edge''': 20}   )
										self.assertEqual(image_processor.crop_size		,					{'''height''': 18, '''width''': 18}   )

										lowercase :							str    		=self.image_processing_class.from_dict(self.image_processor_dict		,					size=42		,					crop_size=84   )
										self.assertEqual(image_processor.size		,					{'''shortest_edge''': 42}   )
										self.assertEqual(image_processor.crop_size		,					{'''height''': 84, '''width''': 84}   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							Union[str, Any]    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowercase :							List[str]    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					Image.Image   )

										# Test not batched input
										lowercase :							Tuple    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)

										# Test batched
										lowercase :							Optional[int]    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							Union[str, Any]    		=self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowercase :							Optional[Any]    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__		,					numpify=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					np.ndarray   )

										# Test not batched input
										lowercase :							Optional[int]    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)

										# Test batched
										lowercase :							Tuple    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							Tuple    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowercase :							Any    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__		,					torchify=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					torch.Tensor   )

										# Test not batched input
										lowercase :							Tuple    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)

										# Test batched
										lowercase :							Optional[int]    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A								=       logging.get_logger(__name__)

__A								=       {
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''vit_mae'''


			def __init__( self     ,		lowerCamelCase__=768     ,		lowerCamelCase__=12     ,		lowerCamelCase__=12     ,		lowerCamelCase__=3_072     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-12     ,		lowerCamelCase__=224     ,		lowerCamelCase__=16     ,		lowerCamelCase__=3     ,		lowerCamelCase__=True     ,		lowerCamelCase__=16     ,		lowerCamelCase__=512     ,		lowerCamelCase__=8     ,		lowerCamelCase__=2_048     ,		lowerCamelCase__=0.75     ,		lowerCamelCase__=False     ,		**lowerCamelCase__     ,		)       -> Optional[int]:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__				)

							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       image_size
							__lowerCamelCase									=       patch_size
							__lowerCamelCase									=       num_channels
							__lowerCamelCase									=       qkv_bias
							__lowerCamelCase									=       decoder_num_attention_heads
							__lowerCamelCase									=       decoder_hidden_size
							__lowerCamelCase									=       decoder_num_hidden_layers
							__lowerCamelCase									=       decoder_intermediate_size
							__lowerCamelCase									=       mask_ratio
							__lowerCamelCase									=       norm_pix_loss


