





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 ConvNextImageProcessor



class 				_UpperCamelCase       (						unittest.TestCase  ):

       def __init__(		self:			str       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			str=7       , _SCREAMING_SNAKE_CASE:			int=3       , _SCREAMING_SNAKE_CASE:			Optional[int]=18       , _SCREAMING_SNAKE_CASE:			List[Any]=30       , _SCREAMING_SNAKE_CASE:			List[str]=400       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=True       , _SCREAMING_SNAKE_CASE:			str=None       , _SCREAMING_SNAKE_CASE:			str=0.8_75       , _SCREAMING_SNAKE_CASE:			int=True       , _SCREAMING_SNAKE_CASE:			Optional[int]=[0.5, 0.5, 0.5]       , _SCREAMING_SNAKE_CASE:			Optional[Any]=[0.5, 0.5, 0.5]       , )							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	size if size is not None else {"shortest_edge": 20}
              UpperCamelCase_ 							=	parent
              UpperCamelCase_ 							=	batch_size
              UpperCamelCase_ 							=	num_channels
              UpperCamelCase_ 							=	image_size
              UpperCamelCase_ 							=	min_resolution
              UpperCamelCase_ 							=	max_resolution
              UpperCamelCase_ 							=	do_resize
              UpperCamelCase_ 							=	size
              UpperCamelCase_ 							=	crop_pct
              UpperCamelCase_ 							=	do_normalize
              UpperCamelCase_ 							=	image_mean
              UpperCamelCase_ 							=	image_std



       def lowercase			(		self:			List[Any]		)							->			Dict:




              """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,
                  "crop_pct": self.crop_pct,
              }



@require_torch
@require_vision
class 				_UpperCamelCase       (						_lowercase	,    unittest.TestCase  ):
       _UpperCamelCase					:       Optional[Any]       = ConvNextImageProcessor if is_vision_available() else None

       def lowercase			(		self:			Optional[int]		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	ConvNextImageProcessingTester(self		)

       @property
       def lowercase			(		self:			List[Any]		)							->			Dict:




              """simple docstring"""


              return self.image_processor_tester.prepare_image_processor_dict()

       def lowercase			(		self:			Optional[int]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.image_processing_class(**self.image_processor_dict		)
              self.assertTrue(hasattr(lowerCamelCase__       , "do_resize"		)		)
              self.assertTrue(hasattr(lowerCamelCase__       , "size"		)		)
              self.assertTrue(hasattr(lowerCamelCase__       , "crop_pct"		)		)
              self.assertTrue(hasattr(lowerCamelCase__       , "do_normalize"		)		)
              self.assertTrue(hasattr(lowerCamelCase__       , "image_mean"		)		)
              self.assertTrue(hasattr(lowerCamelCase__       , "image_std"		)		)

       def lowercase			(		self:			List[Any]		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	self.image_processing_class.from_dict(self.image_processor_dict		)
              self.assertEqual(image_processor.size       , {"shortest_edge": 20}		)

              UpperCamelCase_ 							=	self.image_processing_class.from_dict(self.image_processor_dict       , size=42		)
              self.assertEqual(image_processor.size       , {"shortest_edge": 42}		)

       def lowercase			(		self:			Tuple		)							->			int:




              """simple docstring"""


              pass

       def lowercase			(		self:			str		)							->			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=lowerCamelCase__		)
              for image in image_inputs:
                     self.assertIsInstance(lowerCamelCase__       , 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.size["shortest_edge"],
                      self.image_processor_tester.size["shortest_edge"],
                  )       , )

              # Test batched
              UpperCamelCase_ 							=	image_processing(lowerCamelCase__       , 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.size["shortest_edge"],
                      self.image_processor_tester.size["shortest_edge"],
                  )       , )

       def lowercase			(		self:			Dict		)							->			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=lowerCamelCase__       , numpify=lowerCamelCase__		)
              for image in image_inputs:
                     self.assertIsInstance(lowerCamelCase__       , 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.size["shortest_edge"],
                      self.image_processor_tester.size["shortest_edge"],
                  )       , )

              # Test batched
              UpperCamelCase_ 							=	image_processing(lowerCamelCase__       , 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.size["shortest_edge"],
                      self.image_processor_tester.size["shortest_edge"],
                  )       , )



       def lowercase			(		self:			List[str]		)							->			str:




              """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=lowerCamelCase__       , torchify=lowerCamelCase__		)
              for image in image_inputs:
                     self.assertIsInstance(lowerCamelCase__       , 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.size["shortest_edge"],
                      self.image_processor_tester.size["shortest_edge"],
                  )       , )

              # Test batched
              UpperCamelCase_ 							=	image_processing(lowerCamelCase__       , 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.size["shortest_edge"],
                      self.image_processor_tester.size["shortest_edge"],
                  )       , )





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


lowercase  		=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileViTV2ForImageClassification""",
					    """MobileViTV2ForSemanticSegmentation""",
					    """MobileViTV2Model""",
					    """MobileViTV2PreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileViTVaConfig,
					    MobileViTVaOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilevitva import (
										    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileViTVaForImageClassification,
										    MobileViTVaForSemanticSegmentation,
										    MobileViTVaModel,
										    MobileViTVaPreTrainedModel,
										)

else:
					import sys

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





