


"""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 PoolFormerImageProcessor
class snake_case      (  unittest.TestCase):







		def __init__(					self      :       Optional[Any]    ,   a__      :       Optional[int]    ,   a__      :       Union[str, Any]=7    ,   a__      :       Optional[Any]=3    ,   a__      :       List[str]=30    ,   a__      :       Union[str, Any]=4_00    ,   a__      :       Tuple=True    ,   a__      :       Any=None    ,   a__      :       Tuple=0.9    ,   a__      :       Dict=None    ,   a__      :       Dict=True    ,   a__      :       Tuple=[0.5, 0.5, 0.5]    ,   a__      :       Tuple=[0.5, 0.5, 0.5]    ,   )       ->						Any:





						'''simple docstring'''







						_A					  =			size if size is not None else {"shortest_edge": 30}
						_A					  =			crop_size if crop_size is not None else {"height": 30, "width": 30}
						_A					  =			parent
						_A					  =			batch_size
						_A					  =			num_channels
						_A					  =			min_resolution
						_A					  =			max_resolution
						_A					  =			do_resize_and_center_crop
						_A					  =			size
						_A					  =			crop_pct
						_A					  =			crop_size
						_A					  =			do_normalize
						_A					  =			image_mean
						_A					  =			image_std







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





						'''simple docstring'''







						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 snake_case      (  _UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			PoolFormerImageProcessor if is_vision_available() else None







		def  a_ (					self      :       Tuple		)       ->						int:





						'''simple docstring'''







						_A					  =			PoolFormerImageProcessingTester(self		)







		@property
		def  a_ (					self      :       str		)       ->						Any:





						'''simple docstring'''







						return self.image_processor_tester.prepare_image_processor_dict()







		def  a_ (					self      :       Any		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			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  a_ (					self      :       int		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size    ,   {"shortest_edge": 30}		)
						self.assertEqual(image_processor.crop_size    ,   {"height": 30, "width": 30}		)

						_A					  =			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      :       Optional[int]		)       ->						Optional[int]:





						'''simple docstring'''







						pass







		def  a_ (					self      :       str		)       ->						Optional[Any]:





						'''simple docstring'''







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

						# Test not batched input
						_A					  =			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
						_A					  =			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  a_ (					self      :       Any		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						_A					  =			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
						_A					  =			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
						_A					  =			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  a_ (					self      :       Union[str, Any]		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						_A					  =			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
						_A					  =			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
						_A					  =			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"],
						    )    ,   )



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a_       							=    logging.get_logger(__name__)
class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       str    ,   *a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						None:





						'''simple docstring'''







						warnings.warn(
						    "The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use GLPNImageProcessor instead."    ,   a__    ,   )
						super().__init__(*a__    ,   **a__		)