





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: Tuple					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: Any=7					,   _SCREAMING_SNAKE_CASE: Dict=3					,   _SCREAMING_SNAKE_CASE: Any=18					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=30					,   _SCREAMING_SNAKE_CASE: Tuple=400					,   _SCREAMING_SNAKE_CASE: Optional[int]=True					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=None					,   _SCREAMING_SNAKE_CASE: Tuple=True					,   _SCREAMING_SNAKE_CASE: Any=None					,   )       -> Union[str, Any]:




														"""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 lowercase (		self: List[Any]		)       -> Optional[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     ):
							_UpperCamelCase				:					Tuple         =   MobileNetVaImageProcessor if is_vision_available() else None

							def lowercase (		self: int		)       -> Dict:




														"""simple docstring"""


														UpperCamelCase_          =  MobileNetVaImageProcessingTester(self		)

							@property
							def lowercase (		self: str		)       -> int:




														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()

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




														"""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 lowercase (		self: List[Any]		)       -> Union[str, 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 lowercase (		self: Optional[Any]		)       -> str:




														"""simple docstring"""


														pass

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




														"""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 lowercase (		self: Tuple		)       -> List[Any]:




														"""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 lowercase (		self: Optional[int]		)       -> 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"],
														    )					,   )








_UpperCAmelCase        =				{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

