




"""simple docstring"""







from .number_theory.prime_numbers import next_prime

class 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				)							->      None:
       UpperCamelCase										=  size_table
       UpperCamelCase										=  [None] * self.size_table
       UpperCamelCase										=  0.75 if lim_charge is None else lim_charge
       UpperCamelCase										=  1 if charge_factor is None else charge_factor
       UpperCamelCase										=  []
       UpperCamelCase										=  {}







   def      UpperCAmelCase__ (		self)							->      Tuple:
       return self._keys







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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Union[str, Any]:
       return key % self.size_table







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Union[str, Any]:
       print(F'step {step_ord}')
       print(list(range(len(self.values))))
       print(self.values)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Dict:
       UpperCamelCase										=  1
       UpperCamelCase										=  values
       for value in values:
           self.insert_data(lowerCamelCase_)
           self._step_by_step(lowerCamelCase_)
           i += 1







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_)							->      Any:
       UpperCamelCase										=  data
       UpperCamelCase										=  data







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_=None)							->      Union[str, Any]:
       UpperCamelCase										=  self.hash_function(key + 1)

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

       return new_key







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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[int]:
       UpperCamelCase										=  self.hash_function(lowerCamelCase_)

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

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

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





"""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, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=7					,				lowerCamelCase_=3					,				lowerCamelCase_=1_0					,				lowerCamelCase_=1_8					,				lowerCamelCase_=3_0					,				lowerCamelCase_=4_0_0					,				lowerCamelCase_=True					,				lowerCamelCase_=None					,				lowerCamelCase_=True					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=None					,				)							->      Optional[Any]:
       UpperCamelCase										=  size if size is not None else {'''shortest_edge''': 1_8}
       UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

       UpperCamelCase										=  parent
       UpperCamelCase										=  batch_size
       UpperCamelCase										=  num_channels
       UpperCamelCase										=  num_frames
       UpperCamelCase										=  image_size
       UpperCamelCase										=  min_resolution
       UpperCamelCase										=  max_resolution
       UpperCamelCase										=  do_resize
       UpperCamelCase										=  size
       UpperCamelCase										=  do_normalize
       UpperCamelCase										=  image_mean
       UpperCamelCase										=  image_std
       UpperCamelCase										=  crop_size







   def      UpperCAmelCase__ (		self)							->      Tuple:
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }

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







   """simple docstring"""




   A_										=					VideoMAEImageProcessor if is_vision_available() else None







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  VideoMAEImageProcessingTester(self)







   @property
   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       return self.image_processor_tester.prepare_image_processor_dict()







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_mean'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_std'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_normalize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_resize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_center_crop'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''size'''))







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict)
       self.assertEqual(image_processor.size					,				{'''shortest_edge''': 1_8})
       self.assertEqual(image_processor.crop_size					,				{'''height''': 1_8, '''width''': 1_8})

       UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict					,				size=4_2					,				crop_size=8_4)
       self.assertEqual(image_processor.size					,				{'''shortest_edge''': 4_2})
       self.assertEqual(image_processor.crop_size					,				{'''height''': 8_4, '''width''': 8_4})







   def      UpperCAmelCase__ (		self)							->      List[str]:
       pass







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PIL videos
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				Image.Image)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random numpy tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				numpify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				np.ndarray)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PyTorch tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				torchify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				torch.Tensor)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)