

"""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							:				Union[str, Any]     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				Union[str, Any]=7     ,  UpperCamelCase__							:				List[str]=3     ,  UpperCamelCase__							:				Union[str, Any]=3_0     ,  UpperCamelCase__							:				Optional[Any]=4_0_0     ,  UpperCamelCase__							:				List[str]=True     ,  UpperCamelCase__							:				List[str]=None     ,  UpperCamelCase__							:				Dict=0.9     ,  UpperCamelCase__							:				Dict=None     ,  UpperCamelCase__							:				Any=True     ,  UpperCamelCase__							:				str=[0.5, 0.5, 0.5]     ,  UpperCamelCase__							:				Any=[0.5, 0.5, 0.5]     ,  )->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			size if size is not None else {"shortest_edge": 3_0}
						__lowerCAmelCase:       str          =			crop_size if crop_size is not None else {"height": 3_0, "width": 3_0}
						__lowerCAmelCase:       Optional[int]          =			parent
						__lowerCAmelCase:       Optional[Any]          =			batch_size
						__lowerCAmelCase:       Dict          =			num_channels
						__lowerCAmelCase:       Tuple          =			min_resolution
						__lowerCAmelCase:       Tuple          =			max_resolution
						__lowerCAmelCase:       Optional[int]          =			do_resize_and_center_crop
						__lowerCAmelCase:       str          =			size
						__lowerCAmelCase:       List[Any]          =			crop_pct
						__lowerCAmelCase:       Dict          =			crop_size
						__lowerCAmelCase:       int          =			do_normalize
						__lowerCAmelCase:       int          =			image_mean
						__lowerCAmelCase:       int          =			image_std




				def 							lowercase_     (	self							:				Dict)->   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	(				__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      int         =  PoolFormerImageProcessor if is_vision_available() else None

				def 							lowercase_     (	self							:				Union[str, Any])->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			PoolFormerImageProcessingTester(self)

				@property
				def 							lowercase_     (	self							:				Any)->   Optional[int]:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

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



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__     ,  "do_resize_and_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "size"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "crop_pct"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "image_std"))

				def 							lowercase_     (	self							:				Tuple)->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			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})

						__lowerCAmelCase:       str          =			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 							lowercase_     (	self							:				Dict)->   List[Any]:



						'''simple docstring'''


						pass

				def 							lowercase_     (	self							:				Dict)->   int:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:       Tuple          =			prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__     ,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:       str          =			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
						__lowerCAmelCase:       Dict          =			image_processing(UpperCamelCase__     ,  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							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase:       Union[str, Any]          =			prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase__     ,  numpify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__     ,  np.ndarray)

						# Test not batched input
						__lowerCAmelCase:       Optional[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
						__lowerCAmelCase:       Optional[int]          =			image_processing(UpperCamelCase__     ,  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							:				Union[str, Any])->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:       Union[str, Any]          =			prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase__     ,  torchify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__     ,  torch.Tensor)

						# Test not batched input
						__lowerCAmelCase:       List[str]          =			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
						__lowerCAmelCase:       Dict          =			image_processing(UpperCamelCase__     ,  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 copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union





@dataclass
class     snake_case	:
				SCREAMING_SNAKE_CASE_   :      Optional[Union[str, Path]]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      Optional[Dict]         =  None
				SCREAMING_SNAKE_CASE_   :      Optional[str]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  True
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  None
				SCREAMING_SNAKE_CASE_   :      int         =  1
				SCREAMING_SNAKE_CASE_   :      Optional[Union[str, bool]]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      Optional[Dict]         =  None
				SCREAMING_SNAKE_CASE_   :      Optional[str]         =  None

				def 							lowercase_     (	self							:				str)->   "DownloadConfig":



						'''simple docstring'''


						return self.__class__(**{k: copy.deepcopy(UpperCamelCase__) for k, v in self.__dict__.items()})


