
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 PoolFormerImageProcessor




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def __init__(					self		:List[str]     ,   a		:Union[str, Any]     ,   a		:Any=7     ,   a		:int=3     ,   a		:int=3_0     ,   a		:Dict=4_0_0     ,   a		:Optional[int]=True     ,   a		:Dict=None     ,   a		:Optional[Any]=0.9     ,   a		:List[str]=None     ,   a		:Tuple=True     ,   a		:List[Any]=[0.5, 0.5, 0.5]     ,   a		:int=[0.5, 0.5, 0.5]     ,   )		-> Any:
								__UpperCamelCase	:   Union[str, Any]			   =					size if size is not None else {"shortest_edge": 3_0}
								__UpperCamelCase	:   Optional[Any]			   =					crop_size if crop_size is not None else {"height": 3_0, "width": 3_0}
								__UpperCamelCase	:   Tuple			   =					parent
								__UpperCamelCase	:   Optional[int]			   =					batch_size
								__UpperCamelCase	:   Tuple			   =					num_channels
								__UpperCamelCase	:   Optional[Any]			   =					min_resolution
								__UpperCamelCase	:   Any			   =					max_resolution
								__UpperCamelCase	:   Union[str, Any]			   =					do_resize_and_center_crop
								__UpperCamelCase	:   Tuple			   =					size
								__UpperCamelCase	:   List[Any]			   =					crop_pct
								__UpperCamelCase	:   str			   =					crop_size
								__UpperCamelCase	:   List[str]			   =					do_normalize
								__UpperCamelCase	:   List[Any]			   =					image_mean
								__UpperCamelCase	:   Union[str, Any]			   =					image_std







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> List[Any]:
								return {
								    "size": self.size,
								    "do_resize_and_center_crop": self.do_resize_and_center_crop,
								    "crop_pct": self.crop_pct,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}




@require_torch
@require_vision
class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     PoolFormerImageProcessor if is_vision_available() else None







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Any:
								__UpperCamelCase	:   List[Any]			   =					PoolFormerImageProcessingTester(self						)







	@property
	def    _lowerCamelCase				(					self		:str						)		-> Tuple:
								return self.image_processor_tester.prepare_image_processor_dict()







	def    _lowerCamelCase				(					self		:List[Any]						)		-> Tuple:
								__UpperCamelCase	:   Optional[int]			   =					self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(a     ,   "do_resize_and_center_crop"						)						)
								self.assertTrue(hasattr(a     ,   "size"						)						)
								self.assertTrue(hasattr(a     ,   "crop_pct"						)						)
								self.assertTrue(hasattr(a     ,   "do_normalize"						)						)
								self.assertTrue(hasattr(a     ,   "image_mean"						)						)
								self.assertTrue(hasattr(a     ,   "image_std"						)						)







	def    _lowerCamelCase				(					self		:List[str]						)		-> List[str]:
								__UpperCamelCase	:   Tuple			   =					self.image_processing_class.from_dict(self.image_processor_dict						)
								self.assertEqual(image_processor.size     ,   {"shortest_edge": 3_0}						)
								self.assertEqual(image_processor.crop_size     ,   {"height": 3_0, "width": 3_0}						)

								__UpperCamelCase	:   Any			   =					self.image_processing_class.from_dict(self.image_processor_dict     ,   size=4_2     ,   crop_size=8_4						)
								self.assertEqual(image_processor.size     ,   {"shortest_edge": 4_2}						)
								self.assertEqual(image_processor.crop_size     ,   {"height": 8_4, "width": 8_4}						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Optional[int]:
								pass







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Optional[int]:
								# Initialize image_processing
								__UpperCamelCase	:   Optional[Any]			   =					self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								__UpperCamelCase	:   List[Any]			   =					prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=a						)
								for image in image_inputs:
															self.assertIsInstance(a     ,   Image.Image						)

								# Test not batched input
								__UpperCamelCase	:   Dict			   =					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	:   List[str]			   =					image_processing(a     ,   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		:List[str]						)		-> Optional[Any]:
								# Initialize image_processing
								__UpperCamelCase	:   Any			   =					self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								__UpperCamelCase	:   int			   =					prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=a     ,   numpify=a						)
								for image in image_inputs:
															self.assertIsInstance(a     ,   np.ndarray						)

								# Test not batched input
								__UpperCamelCase	:   List[Any]			   =					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	:   List[str]			   =					image_processing(a     ,   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		:Optional[Any]						)		-> List[Any]:
								# Initialize image_processing
								__UpperCamelCase	:   Tuple			   =					self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								__UpperCamelCase	:   int			   =					prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=a     ,   torchify=a						)
								for image in image_inputs:
															self.assertIsInstance(a     ,   torch.Tensor						)

								# Test not batched input
								__UpperCamelCase	:   List[Any]			   =					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	:   Union[str, Any]			   =					image_processing(a     ,   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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  :					List[str]													=       {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  :					Optional[Any]													=       [
		    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViTMSNModel',
		    'ViTMSNForImageClassification',
		    'ViTMSNPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		lowercase  :					Optional[Any]													=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)