"""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 ViTImageProcessor
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=13   ,      snake_case_=3   ,      snake_case_=224   ,      snake_case_=30   ,      snake_case_=400   ,      snake_case_=True   ,      snake_case_=None   ,      snake_case_=True   ,      snake_case_=[0.5, 0.5, 0.5]   ,      snake_case_=[0.5, 0.5, 0.5]   ,      ):







										'''simple docstring'''


										A__       :     List[str]								=			size if size is not None else {'''height''': 18, '''width''': 18}
										A__       :     List[Any]								=			parent
										A__       :     Any								=			batch_size
										A__       :     Dict								=			num_channels
										A__       :     Optional[int]								=			image_size
										A__       :     str								=			min_resolution
										A__       :     str								=			max_resolution
										A__       :     Any								=			do_resize
										A__       :     List[Any]								=			size
										A__       :     Optional[Any]								=			do_normalize
										A__       :     str								=			image_mean
										A__       :     str								=			image_std





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}





@require_torch
@require_vision
class 						__UpperCAmelCase    (a_							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[int]					     =				ViTImageProcessor if is_vision_available() else None





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			EfficientFormerImageProcessorTester(self							)





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return self.image_proc_tester.prepare_image_processor_dict()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Dict								=			self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(lowercase_   ,      """image_mean"""							)							)
										self.assertTrue(hasattr(lowercase_   ,      """image_std"""							)							)
										self.assertTrue(hasattr(lowercase_   ,      """do_normalize"""							)							)
										self.assertTrue(hasattr(lowercase_   ,      """do_resize"""							)							)
										self.assertTrue(hasattr(lowercase_   ,      """size"""							)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										A__       :     Tuple								=			prepare_image_inputs(self.image_proc_tester   ,      equal_resolution=lowercase_							)
										for image in image_inputs:
																	self.assertIsInstance(lowercase_   ,      Image.Image							)

										# Test not batched input
										A__       :     int								=			image_processor(image_inputs[0]   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )   ,      )

										# Test batched
										A__       :     Dict								=			image_processor(lowercase_   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Tuple								=			self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										A__       :     Optional[int]								=			prepare_image_inputs(self.image_proc_tester   ,      equal_resolution=lowercase_   ,      numpify=lowercase_							)
										for image in image_inputs:
																	self.assertIsInstance(lowercase_   ,      np.ndarray							)

										# Test not batched input
										A__       :     List[Any]								=			image_processor(image_inputs[0]   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )   ,      )

										# Test batched
										A__       :     int								=			image_processor(lowercase_   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Tuple								=			self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										A__       :     List[Any]								=			prepare_image_inputs(self.image_proc_tester   ,      equal_resolution=lowercase_   ,      torchify=lowercase_							)
										for image in image_inputs:
																	self.assertIsInstance(lowercase_   ,      torch.Tensor							)

										# Test not batched input
										A__       :     List[Any]								=			image_processor(image_inputs[0]   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )   ,      )

										# Test batched
										A__       :     List[str]								=			image_processor(lowercase_   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )   ,      )








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    : str				 =						{
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : List[Any]				 =						[
							    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Swinv2ForImageClassification''',
							    '''Swinv2ForMaskedImageModeling''',
							    '''Swinv2Model''',
							    '''Swinv2PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swinva import (
														    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinvaForImageClassification,
														    SwinvaForMaskedImageModeling,
														    SwinvaModel,
														    SwinvaPreTrainedModel,
														)


else:
							import sys

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




