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 GLPNImageProcessor






class      __lowercase					(    unittest.TestCase    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      int=7							,							__lowerCamelCase				:      Dict=3							,							__lowerCamelCase				:      str=18							,							__lowerCamelCase				:      List[Any]=30							,							__lowerCamelCase				:      str=4_00							,							__lowerCamelCase				:      Optional[Any]=True							,							__lowerCamelCase				:      str=32							,							__lowerCamelCase				:      Optional[Any]=True							,							)							->		str:


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		num_channels
									lowercase							=		image_size
									lowercase							=		min_resolution
									lowercase							=		max_resolution
									lowercase							=		do_resize
									lowercase							=		size_divisor
									lowercase							=		do_rescale



						def        __a		(	self				:      Union[str, Any]   )							->		Optional[Any]:


									'''simple docstring'''

									return {
									    "do_resize": self.do_resize,
									    "size_divisor": self.size_divisor,
									    "do_rescale": self.do_rescale,
									}






@require_torch
@require_vision
class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     GLPNImageProcessor if is_vision_available() else None

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


									'''simple docstring'''

									lowercase							=		GLPNImageProcessingTester(self   )

						@property
						def        __a		(	self				:      int   )							->		Union[str, Any]:


									'''simple docstring'''

									return self.image_processor_tester.prepare_image_processor_dict()

						def        __a		(	self				:      Union[str, Any]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_resize'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''size_divisor'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''resample'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_rescale'''   )   )

						def        __a		(	self				:      Dict   )							->		List[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Tuple   )							->		Union[str, Any]:


									'''simple docstring'''

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

									# Test not batched input (GLPNImageProcessor doesn't support batching)
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
									self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )

						def        __a		(	self				:      int   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random numpy tensors
									lowercase							=		prepare_image_inputs(self.image_processor_tester							,							equal_resolution=__lowerCamelCase							,							numpify=__lowerCamelCase   )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase							,							np.ndarray   )

									# Test not batched input (GLPNImageProcessor doesn't support batching)
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
									self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )



						def        __a		(	self				:      List[str]   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random PyTorch tensors
									lowercase							=		prepare_image_inputs(self.image_processor_tester							,							equal_resolution=__lowerCamelCase							,							torchify=__lowerCamelCase   )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase							,							torch.Tensor   )

									# Test not batched input (GLPNImageProcessor doesn't support batching)
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
									self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )






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

A_      =      TypeVar("KEY")
A_      =      TypeVar("VAL")






@dataclass(frozen=_A       ,     slots=_A    )
class      __lowercase					(    Generic[KEY, VAL]    ):
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _Item    ):

						def __init__(	self				:      Optional[int]   )							->		None:


									'''simple docstring'''

									super().__init__(__lowerCamelCase							,							__lowerCamelCase   )



						def __bool__(	self				:      str   )							->		bool:


									'''simple docstring'''

									return False





A_      =      _DeletedItem()






class      __lowercase					(    MutableMapping[KEY, VAL]    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      int = 8							,							__lowerCamelCase				:      float = 0.75   )							->		None:


									'''simple docstring'''

									lowercase							=		initial_block_size
									lowercase							=		[None] * initial_block_size
									assert 0.0 < capacity_factor < 1.0
									lowercase							=		capacity_factor
									lowercase							=		0

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KEY   )							->		int:


									'''simple docstring'''

									return hash(__lowerCamelCase   ) % len(self._buckets   )

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      int   )							->		int:


									'''simple docstring'''

									return (ind + 1) % len(self._buckets   )

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		bool:


									'''simple docstring'''

									lowercase							=		self._buckets[ind]
									if not stored:
												lowercase							=		_Item(__lowerCamelCase							,							__lowerCamelCase   )
												self._len += 1
												return True
									elif stored.key == key:
												lowercase							=		_Item(__lowerCamelCase							,							__lowerCamelCase   )
												return True
									else:
												return False

						def        __a		(	self				:      Optional[Any]   )							->		bool:


									'''simple docstring'''

									lowercase							=		len(self._buckets   ) * self._capacity_factor
									return len(self   ) >= int(__lowerCamelCase   )

						def        __a		(	self				:      Tuple   )							->		bool:


									'''simple docstring'''

									if len(self._buckets   ) <= self._initial_block_size:
												return False
									lowercase							=		len(self._buckets   ) * self._capacity_factor / 2
									return len(self   ) < limit

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      int   )							->		None:


									'''simple docstring'''

									lowercase							=		self._buckets
									lowercase							=		[None] * new_size
									lowercase							=		0
									for item in old_buckets:
												if item:
															self._add_item(item.key							,							item.val   )

						def        __a		(	self				:      List[str]   )							->		None:


									'''simple docstring'''

									self._resize(len(self._buckets   ) * 2   )

						def        __a		(	self				:      Any   )							->		None:


									'''simple docstring'''

									self._resize(len(self._buckets   ) // 2   )

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY   )							->		Iterator[int]:


									'''simple docstring'''

									lowercase							=		self._get_bucket_index(__lowerCamelCase   )
									for _ in range(len(self._buckets   )   ):
												yield ind
												lowercase							=		self._get_next_ind(__lowerCamelCase   )

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		None:


									'''simple docstring'''

									for ind in self._iterate_buckets(__lowerCamelCase   ):
												if self._try_set(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   ):
															break

						def __setitem__(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		None:


									'''simple docstring'''

									if self._is_full():
												self._size_up()

									self._add_item(__lowerCamelCase							,							__lowerCamelCase   )

						def __delitem__(	self				:      List[str]							,							__lowerCamelCase				:      KEY   )							->		None:


									'''simple docstring'''

									for ind in self._iterate_buckets(__lowerCamelCase   ):
												lowercase							=		self._buckets[ind]
												if item is None:
															raise KeyError(__lowerCamelCase   )
												if item is _deleted:
															continue
												if item.key == key:
															lowercase							=		_deleted
															self._len -= 1
															break
									if self._is_sparse():
												self._size_down()

						def __getitem__(	self				:      Any							,							__lowerCamelCase				:      KEY   )							->		VAL:


									'''simple docstring'''

									for ind in self._iterate_buckets(__lowerCamelCase   ):
												lowercase							=		self._buckets[ind]
												if item is None:
															break
												if item is _deleted:
															continue
												if item.key == key:
															return item.val
									raise KeyError(__lowerCamelCase   )

						def __len__(	self				:      str   )							->		int:


									'''simple docstring'''

									return self._len

						def __iter__(	self				:      Union[str, Any]   )							->		Iterator[KEY]:


									'''simple docstring'''

									yield from (item.key for item in self._buckets if item)



						def __repr__(	self				:      Any   )							->		str:


									'''simple docstring'''

									lowercase							=		''' ,'''.join(
									    f'{item.key}: {item.val}' for item in self._buckets if item   )
									return f'HashMap({val_string})'





