




'''simple docstring'''







from .number_theory.prime_numbers import next_prime





class      lowerCamelCase_							:




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase = None,  __UpperCamelCase = None,  ):
							_snake_case	       =       size_table
							_snake_case	       =       [None] * self.size_table
							_snake_case	       =       0.75 if lim_charge is None else lim_charge
							_snake_case	       =       1 if charge_factor is None else charge_factor
							_snake_case	       =       []
							_snake_case	       =       {}






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return self._keys






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return sum(1 for slot in self.values if slot is not None ) / (
							    self.size_table * self.charge_factor
							)






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							return key % self.size_table






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							print(f'''step {step_ord}''' )
							print(list(range(len(self.values ) ) ) )
							print(self.values )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_snake_case	       =       1
							_snake_case	       =       values
							for value in values:
									self.insert_data(__UpperCamelCase )
									self._step_by_step(__UpperCamelCase )
									i += 1






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase ):
							_snake_case	       =       data
							_snake_case	       =       data






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase=None ):
							_snake_case	       =       self.hash_function(key + 1 )

							while self.values[new_key] is not None and self.values[new_key] != key:
									if self.values.count(__UpperCamelCase ) > 0:
											_snake_case	       =       self.hash_function(new_key + 1 )
									else:
											_snake_case	       =       None
											break

							return new_key






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       [value for value in self.values if value is not None]
							_snake_case	       =       next_prime(self.size_table,  factor=2 )
							self._keys.clear()
							_snake_case	       =       [None] * self.size_table  # hell's pointers D: don't DRY ;/
							for value in survivor_values:
									self.insert_data(__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_snake_case	       =       self.hash_function(__UpperCamelCase )

							if self.values[key] is None:
									self._set_value(__UpperCamelCase,  __UpperCamelCase )

							elif self.values[key] == data:
									pass

							else:
									_snake_case	       =       self._collision_resolution(__UpperCamelCase,  __UpperCamelCase )
									if collision_resolution is not None:
											self._set_value(__UpperCamelCase,  __UpperCamelCase )
									else:
											self.rehashing()
											self.insert_data(__UpperCamelCase )










"""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


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import BlipImageProcessor


class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=7 ,				lowerCamelCase__=3 ,				lowerCamelCase__=1_8 ,				lowerCamelCase__=3_0 ,				lowerCamelCase__=4_0_0 ,				lowerCamelCase__=True ,				lowerCamelCase__=None ,				lowerCamelCase__=True ,				lowerCamelCase__=False ,				lowerCamelCase__=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] ,				lowerCamelCase__=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] ,				lowerCamelCase__=True ,				):
									_lowerCamelCase					     = size if size is not None else {'''height''': 2_0, '''width''': 2_0}
									_lowerCamelCase					     = parent
									_lowerCamelCase					     = batch_size
									_lowerCamelCase					     = num_channels
									_lowerCamelCase					     = image_size
									_lowerCamelCase					     = min_resolution
									_lowerCamelCase					     = max_resolution
									_lowerCamelCase					     = do_resize
									_lowerCamelCase					     = size
									_lowerCamelCase					     = do_normalize
									_lowerCamelCase					     = image_mean
									_lowerCamelCase					     = image_std
									_lowerCamelCase					     = do_pad
									_lowerCamelCase					     = do_convert_rgb

					def 	snake_case__					(			self      ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_convert_rgb": self.do_convert_rgb,
									    "do_pad": self.do_pad,
									}





					def 	snake_case__					(			self ,				lowerCamelCase__=False ,				lowerCamelCase__=False ,				lowerCamelCase__=False      ):
									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
													_lowerCamelCase					     = []
													for i in range(self.batch_size      ):
																	image_inputs.append(
																	    np.random.randint(
																	        2_5_5 ,				size=(self.num_channels, self.max_resolution, self.max_resolution) ,				dtype=np.uinta      )      )
									else:
													_lowerCamelCase					     = []
													for i in range(self.batch_size      ):
																	_lowerCamelCase     ,   _lowerCamelCase					     = np.random.choice(np.arange(self.min_resolution ,				self.max_resolution      ) ,				2      )
																	image_inputs.append(np.random.randint(2_5_5 ,				size=(self.num_channels, width, height) ,				dtype=np.uinta      )      )

									if not numpify and not torchify:
													# PIL expects the channel dimension as last dimension
													_lowerCamelCase					     = [Image.fromarray(np.moveaxis(lowerCamelCase__ ,				0 ,				-1      )      ) for x in image_inputs]

									if torchify:
													_lowerCamelCase					     = [torch.from_numpy(lowerCamelCase__      ) for x in image_inputs]

									return image_inputs


@require_torch
@require_vision
class 							lowerCamelCase_(      A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Any        =       BlipImageProcessor if is_vision_available() else None

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = BlipImageProcessingTester(self      )

					@property
					def 	snake_case__					(			self      ):
									return self.image_processor_tester.prepare_image_processor_dict()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_resize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''size'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_normalize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_mean'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_std'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_convert_rgb'''      )      )

					def 	snake_case__					(			self      ):
									pass

					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				Image.Image      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(image_inputs[0] ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(lowerCamelCase__ ,				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.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__ ,				numpify=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				np.ndarray      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(image_inputs[0] ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(lowerCamelCase__ ,				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.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)





					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__ ,				torchify=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				torch.Tensor      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(image_inputs[0] ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(lowerCamelCase__ ,				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.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)


@require_torch
@require_vision
class 							lowerCamelCase_(      A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Tuple        =       BlipImageProcessor if is_vision_available() else None

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = BlipImageProcessingTester(self ,				num_channels=4      )
									_lowerCamelCase					     = 3

					@property
					def 	snake_case__					(			self      ):
									return self.image_processor_tester.prepare_image_processor_dict()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_resize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''size'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_normalize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_mean'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_std'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_convert_rgb'''      )      )

					def 	snake_case__					(			self      ):
									pass





					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				Image.Image      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(image_inputs[0] ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        1,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(lowerCamelCase__ ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        self.image_processor_tester.batch_size,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)
