



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      ):

					"""simple docstring"""






					def __init__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     List[Any]=7				,       SCREAMING_SNAKE_CASE :     Any=3				,       SCREAMING_SNAKE_CASE :     Tuple=30				,       SCREAMING_SNAKE_CASE :     Any=400				,       SCREAMING_SNAKE_CASE :     Optional[Any]=True				,       SCREAMING_SNAKE_CASE :     str=None				,       SCREAMING_SNAKE_CASE :     List[str]=0.9				,       SCREAMING_SNAKE_CASE :     List[str]=None				,       SCREAMING_SNAKE_CASE :     str=True				,       SCREAMING_SNAKE_CASE :     Optional[int]=[0.5, 0.5, 0.5]				,       SCREAMING_SNAKE_CASE :     List[Any]=[0.5, 0.5, 0.5]				,       ):
									lowercase__					:   Any													=					size if size is not None else {"shortest_edge": 30}
									lowercase__					:   List[Any]													=					crop_size if crop_size is not None else {"height": 30, "width": 30}
									lowercase__					:   List[Any]													=					parent
									lowercase__					:   Optional[Any]													=					batch_size
									lowercase__					:   int													=					num_channels
									lowercase__					:   Tuple													=					min_resolution
									lowercase__					:   List[Any]													=					max_resolution
									lowercase__					:   Optional[int]													=					do_resize_and_center_crop
									lowercase__					:   List[str]													=					size
									lowercase__					:   Union[str, Any]													=					crop_pct
									lowercase__					:   str													=					crop_size
									lowercase__					:   int													=					do_normalize
									lowercase__					:   Optional[int]													=					image_mean
									lowercase__					:   str													=					image_std







					def 			snake_case					(							self :     Dict					):
									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      ):

					"""simple docstring"""






					lowercase_								=		PoolFormerImageProcessor if is_vision_available() else None







					def 			snake_case					(							self :     int					):
									lowercase__					:   Tuple													=					PoolFormerImageProcessingTester(self					)







					@property
					def 			snake_case					(							self :     str					):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 			snake_case					(							self :     Dict					):
									lowercase__					:   Dict													=					self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "do_resize_and_center_crop"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "size"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "crop_pct"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "do_normalize"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "image_mean"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "image_std"					)					)







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   Dict													=					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}					)

									lowercase__					:   int													=					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 			snake_case					(							self :     Tuple					):
									pass







					def 			snake_case					(							self :     str					):
									# Initialize image_processing
									lowercase__					:   Any													=					self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									lowercase__					:   List[Any]													=					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
									lowercase__					:   Tuple													=					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
									lowercase__					:   Optional[int]													=					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 			snake_case					(							self :     int					):
									# Initialize image_processing
									lowercase__					:   str													=					self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									lowercase__					:   str													=					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
									lowercase__					:   int													=					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
									lowercase__					:   Dict													=					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 			snake_case					(							self :     List[Any]					):
									# Initialize image_processing
									lowercase__					:   Optional[Any]													=					self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									lowercase__					:   Optional[Any]													=					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
									lowercase__					:   Dict													=					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
									lowercase__					:   Optional[Any]													=					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"],
									    )				,       )









from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCAmelCase__					  =  TypeVar('''KEY''')
lowerCAmelCase__					  =  TypeVar('''VAL''')

@dataclass(frozen=_UpperCamelCase    ,      slots=_UpperCamelCase      )
class 				snake_case__(Generic[KEY, VAL]      ):

					"""simple docstring"""






					lowercase_								=		42
					lowercase_								=		42






class 				snake_case__(_Item      ):

					"""simple docstring"""






					def __init__(							self :     List[str]					):
									super().__init__(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def __bool__(							self :     Tuple					):
									return False






lowerCAmelCase__					  =  _DeletedItem()

class 				snake_case__(MutableMapping[KEY, VAL]      ):

					"""simple docstring"""






					def __init__(							self :     Any				,       SCREAMING_SNAKE_CASE :     int = 8				,       SCREAMING_SNAKE_CASE :     float = 0.75					):
									lowercase__					:   Any													=					initial_block_size
									lowercase__					:   list[_Item | None]													=					[None] * initial_block_size
									assert 0.0 < capacity_factor < 1.0
									lowercase__					:   Dict													=					capacity_factor
									lowercase__					:   Optional[int]													=					0







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     KEY					):
									return hash(SCREAMING_SNAKE_CASE					) % len(self._buckets					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     int					):
									return (ind + 1) % len(self._buckets					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     KEY				,       SCREAMING_SNAKE_CASE :     VAL					):
									lowercase__					:   Tuple													=					self._buckets[ind]
									if not stored:
													lowercase__					:   int													=					_Item(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
													self._len += 1
													return True
									elif stored.key == key:
													lowercase__					:   str													=					_Item(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
													return True
									else:
													return False







					def 			snake_case					(							self :     str					):
									lowercase__					:   str													=					len(self._buckets					) * self._capacity_factor
									return len(self					) >= int(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[str]					):
									if len(self._buckets					) <= self._initial_block_size:
													return False
									lowercase__					:   Optional[Any]													=					len(self._buckets					) * self._capacity_factor / 2
									return len(self					) < limit







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     int					):
									lowercase__					:   Tuple													=					self._buckets
									lowercase__					:   Optional[int]													=					[None] * new_size
									lowercase__					:   int													=					0
									for item in old_buckets:
													if item:
																	self._add_item(item.key				,       item.val					)







					def 			snake_case					(							self :     int					):
									self._resize(len(self._buckets					) * 2					)







					def 			snake_case					(							self :     Optional[Any]					):
									self._resize(len(self._buckets					) // 2					)







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     KEY					):
									lowercase__					:   Tuple													=					self._get_bucket_index(SCREAMING_SNAKE_CASE					)
									for _ in range(len(self._buckets					)					):
													yield ind
													lowercase__					:   Union[str, Any]													=					self._get_next_ind(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     KEY				,       SCREAMING_SNAKE_CASE :     VAL					):
									for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE					):
													if self._try_set(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
																	break







					def __setitem__(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     KEY				,       SCREAMING_SNAKE_CASE :     VAL					):
									if self._is_full():
													self._size_up()

									self._add_item(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def __delitem__(							self :     int				,       SCREAMING_SNAKE_CASE :     KEY					):
									for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE					):
													lowercase__					:   Union[str, Any]													=					self._buckets[ind]
													if item is None:
																	raise KeyError(SCREAMING_SNAKE_CASE					)
													if item is _deleted:
																	continue
													if item.key == key:
																	lowercase__					:   Optional[int]													=					_deleted
																	self._len -= 1
																	break
									if self._is_sparse():
													self._size_down()







					def __getitem__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     KEY					):
									for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE					):
													lowercase__					:   Any													=					self._buckets[ind]
													if item is None:
																	break
													if item is _deleted:
																	continue
													if item.key == key:
																	return item.val
									raise KeyError(SCREAMING_SNAKE_CASE					)







					def __len__(							self :     Optional[Any]					):
									return self._len







					def __iter__(							self :     List[str]					):
									yield from (item.key for item in self._buckets if item)







					def __repr__(							self :     str					):
									lowercase__					:   int													=					" ,".join(
									    f"""{item.key}: {item.val}""" for item in self._buckets if item					)
									return f"""HashMap({val_string})"""




