

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 MobileNetVaImageProcessor

class 						__UpperCamelCase    (					unittest.TestCase						):




	def __init__(		self	:						Optional[Any]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						List[Any]=7		,	lowerCAmelCase	:						Tuple=3		,	lowerCAmelCase	:						int=18		,	lowerCAmelCase	:						Dict=30		,	lowerCAmelCase	:						Tuple=400		,	lowerCAmelCase	:						Any=True		,	lowerCAmelCase	:						Optional[int]=None		,	lowerCAmelCase	:						str=True		,	lowerCAmelCase	:						Dict=None		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			size if size is not None else {"shortest_edge": 20}
		UpperCAmelCase_       				=			crop_size if crop_size is not None else {"height": 18, "width": 18}
		UpperCAmelCase_       				=			parent
		UpperCAmelCase_       				=			batch_size
		UpperCAmelCase_       				=			num_channels
		UpperCAmelCase_       				=			image_size
		UpperCAmelCase_       				=			min_resolution
		UpperCAmelCase_       				=			max_resolution
		UpperCAmelCase_       				=			do_resize
		UpperCAmelCase_       				=			size
		UpperCAmelCase_       				=			do_center_crop
		UpperCAmelCase_       				=			crop_size




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







		return {
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "do_center_crop": self.do_center_crop,
		    "crop_size": self.crop_size,
		}


@require_torch
@require_vision
class 						__UpperCamelCase    (					__lowerCamelCase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     MobileNetVaImageProcessor if is_vision_available() else None




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MobileNetVaImageProcessingTester(self							)




	@property
	def       __A					(		self	:						int							):


		'''simple docstring'''







		return self.image_processor_tester.prepare_image_processor_dict()




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.image_processing_class(**self.image_processor_dict							)
		self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,	"do_resize"							)							)
		self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,	"size"							)							)
		self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,	"do_center_crop"							)							)
		self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,	"crop_size"							)							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			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}							)

		UpperCAmelCase_       				=			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       __A					(		self	:						List[str]							):


		'''simple docstring'''







		pass




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.image_processing_class(**self.image_processor_dict							)
		# create random PIL images
		UpperCAmelCase_       				=			prepare_image_inputs(self.image_processor_tester		,	equal_resolution=SCREAMING_SNAKE_CASE_							)
		for image in image_inputs:
			self.assertIsInstance(SCREAMING_SNAKE_CASE_		,	Image.Image							)

		# Test not batched input
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			image_processing(SCREAMING_SNAKE_CASE_		,	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       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.image_processing_class(**self.image_processor_dict							)
		# create random numpy tensors
		UpperCAmelCase_       				=			prepare_image_inputs(self.image_processor_tester		,	equal_resolution=SCREAMING_SNAKE_CASE_		,	numpify=SCREAMING_SNAKE_CASE_							)
		for image in image_inputs:
			self.assertIsInstance(SCREAMING_SNAKE_CASE_		,	np.ndarray							)

		# Test not batched input
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			image_processing(SCREAMING_SNAKE_CASE_		,	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       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.image_processing_class(**self.image_processor_dict							)
		# create random PyTorch tensors
		UpperCAmelCase_       				=			prepare_image_inputs(self.image_processor_tester		,	equal_resolution=SCREAMING_SNAKE_CASE_		,	torchify=SCREAMING_SNAKE_CASE_							)
		for image in image_inputs:
			self.assertIsInstance(SCREAMING_SNAKE_CASE_		,	torch.Tensor							)

		# Test not batched input
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			image_processing(SCREAMING_SNAKE_CASE_		,	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:    List[str] 							=			logging.get_logger(__name__)

_a:    Any 							=			{
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     'vit_msn'




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[int]=768		,	lowerCAmelCase	:						Dict=12		,	lowerCAmelCase	:						str=12		,	lowerCAmelCase	:						List[str]=3_072		,	lowerCAmelCase	:						Tuple="gelu"		,	lowerCAmelCase	:						int=0.0		,	lowerCAmelCase	:						Optional[int]=0.0		,	lowerCAmelCase	:						Union[str, Any]=0.02		,	lowerCAmelCase	:						str=1e-06		,	lowerCAmelCase	:						Dict=224		,	lowerCAmelCase	:						str=16		,	lowerCAmelCase	:						List[Any]=3		,	lowerCAmelCase	:						Union[str, Any]=True		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







		super().__init__(**lowerCAmelCase							)

		UpperCAmelCase_       				=			hidden_size
		UpperCAmelCase_       				=			num_hidden_layers
		UpperCAmelCase_       				=			num_attention_heads
		UpperCAmelCase_       				=			intermediate_size
		UpperCAmelCase_       				=			hidden_act
		UpperCAmelCase_       				=			hidden_dropout_prob
		UpperCAmelCase_       				=			attention_probs_dropout_prob
		UpperCAmelCase_       				=			initializer_range
		UpperCAmelCase_       				=			layer_norm_eps
		UpperCAmelCase_       				=			image_size
		UpperCAmelCase_       				=			patch_size
		UpperCAmelCase_       				=			num_channels
		UpperCAmelCase_       				=			qkv_bias