






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      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		def __init__(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Optional[int]=7						,_SCREAMING_SNAKE_CASE		:       List[str]=3						,_SCREAMING_SNAKE_CASE		:       Dict=1_8						,_SCREAMING_SNAKE_CASE		:       List[str]=3_0						,_SCREAMING_SNAKE_CASE		:       List[Any]=4_0_0						,_SCREAMING_SNAKE_CASE		:       Optional[int]=True						,_SCREAMING_SNAKE_CASE		:       int=3_2						,_SCREAMING_SNAKE_CASE		:       Dict=True						,)    ->   List[str]:


						'''simple docstring'''
						A	 =      parent
						A	 =      batch_size
						A	 =      num_channels
						A	 =      image_size
						A	 =      min_resolution
						A	 =      max_resolution
						A	 =      do_resize
						A	 =      size_divisor
						A	 =      do_rescale







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


						'''simple docstring'''
						return {
						    "do_resize": self.do_resize,
						    "size_divisor": self.size_divisor,
						    "do_rescale": self.do_rescale,
						}


@require_torch
@require_vision
class      UpperCamelCase (				snake_case__			,       unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   GLPNImageProcessor if is_vision_available() else None
		def       A(  self		:       Optional[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      GLPNImageProcessingTester(self		)
		@property
		def       A(  self		:       Dict		)    ->   Dict:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def       A(  self		:       Dict		)    ->   Any:


						'''simple docstring'''
						A	 =      self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'size_divisor'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'resample'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'do_rescale'		)		)
		def       A(  self		:       Optional[int]		)    ->   str:


						'''simple docstring'''
						pass
		def       A(  self		:       Union[str, Any]		)    ->   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=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE						,Image.Image		)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						A	 =      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		:       str		)    ->   int:


						'''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=_SCREAMING_SNAKE_CASE						,numpify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE						,np.ndarray		)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						A	 =      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		:       Optional[int]		)    ->   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=_SCREAMING_SNAKE_CASE						,torchify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE						,torch.Tensor		)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						A	 =      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 import deque

from .hash_table import HashTable
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       Any						,*_SCREAMING_SNAKE_CASE		:       Optional[int]						,**_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   int:


						'''simple docstring'''
						super().__init__(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   List[str]:


						'''simple docstring'''
						A	 =      deque([]		) if self.values[key] is None else self.values[key]
						self.values[key].appendleft(_SCREAMING_SNAKE_CASE		)
						A	 =      self.values[key]
		def       A(  self		:       Optional[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						return (
						    sum(self.charge_factor - len(_SCREAMING_SNAKE_CASE		) for slot in self.values		)
						    / self.size_table
						    * self.charge_factor
						)







		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str=None		)    ->   Union[str, Any]:


						'''simple docstring'''
						if not (
						    len(self.values[key]		) == self.charge_factor and self.values.count(_SCREAMING_SNAKE_CASE		) == 0
						):
										return key
						return super()._collision_resolution(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)





