
from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

SCREAMING_SNAKE_CASE							:List[str]										=    (3, 9, -11, 0, 7, 5, 1, -1)
SCREAMING_SNAKE_CASE							:Union[str, Any]										=    (4, 6, 2, 0, 8, 10, 3, -2)





@dataclass
class 							UpperCAmelCase				:




	'''simple docstring'''





	snake_case_    =					42
	snake_case_    =					42





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			int							,A :			Iterable[int]			):
					__A             =			None
					for i in sorted(A							,reverse=A			):
									__A             =			Node(A							,self.head			)






	def __iter__(	self :			Optional[int]			):
					__A             =			self.head
					while node:
									yield node.data
									__A             =			node.next_node






	def __len__(	self :			List[str]			):
					return sum(1 for _ in self			)







	def __str__(	self :			List[str]			):
					return " -> ".join([str(A			) for node in self]			)





def 						UpperCAmelCase      (     a_		,       a_						)		->     SortedLinkedList:







				"""simple docstring"""



				return SortedLinkedList(list(a_						) + list(a_						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					SCREAMING_SNAKE_CASE							:List[Any]										=    SortedLinkedList
					print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))


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 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(	self :			Optional[int]							,A :			List[str]							,A :			List[Any]=7							,A :			Any=3							,A :			int=30							,A :			List[Any]=4_00							,A :			str=True							,A :			int=None							,A :			List[str]=0.9							,A :			Dict=None							,A :			int=True							,A :			Any=[0.5, 0.5, 0.5]							,A :			Optional[int]=[0.5, 0.5, 0.5]							,):
					__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     UpperCamelCase_  (	self :			int			):
					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 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					PoolFormerImageProcessor if is_vision_available() else None






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			PoolFormerImageProcessingTester(self			)






	@property
	def     UpperCamelCase_  (	self :			List[Any]			):
					return self.image_processor_tester.prepare_image_processor_dict()






	def     UpperCamelCase_  (	self :			Tuple			):
					__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     UpperCamelCase_  (	self :			Union[str, Any]			):
					__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     UpperCamelCase_  (	self :			List[str]			):
					pass






	def     UpperCamelCase_  (	self :			Optional[int]			):
					# Initialize image_processing
					__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     UpperCamelCase_  (	self :			List[Any]			):
					# Initialize image_processing
					__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     UpperCamelCase_  (	self :			List[Any]			):
					# Initialize image_processing
					__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"],
					    )							,)
