"""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   UpperCAmelCase_	(				unittest.TestCase   ):
    def __init__(	self			:       Union[str, Any]   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Tuple=7   ,						_lowercase			:       List[str]=3   ,						_lowercase			:       Optional[Any]=1_0   ,						_lowercase			:       List[Any]=1_8   ,						_lowercase			:       Tuple=3_0   ,						_lowercase			:       List[Any]=4_0_0   ,						_lowercase			:       Optional[int]=True   ,						_lowercase			:       Optional[int]=None   ,						_lowercase			:       List[Any]=True   ,						_lowercase			:       Optional[Any]=[0.5, 0.5, 0.5]   ,						_lowercase			:       Optional[int]=[0.5, 0.5, 0.5]   ,						_lowercase			:       List[Any]=None   ,						)						->    Any:
       _lowercase							     =       size if size is not None else {"shortest_edge": 1_8}
       _lowercase							     =       crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}

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




    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Optional[Any]:
       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   UpperCAmelCase_	(				lowercase__,    unittest.TestCase   ):
    snake_case_											=      VideoMAEImageProcessor if is_vision_available() else None
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Dict:
       _lowercase							     =       VideoMAEImageProcessingTester(self		)
    @property
    def 		_lowerCamelCase      (	self			:       Any		)						->    List[Any]:
       return self.image_processor_tester.prepare_image_processor_dict()
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    Optional[int]:
       _lowercase							     =       self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(_lowercase   ,						"image_mean"		)		)
       self.assertTrue(hasattr(_lowercase   ,						"image_std"		)		)
       self.assertTrue(hasattr(_lowercase   ,						"do_normalize"		)		)
       self.assertTrue(hasattr(_lowercase   ,						"do_resize"		)		)
       self.assertTrue(hasattr(_lowercase   ,						"do_center_crop"		)		)
       self.assertTrue(hasattr(_lowercase   ,						"size"		)		)
    def 		_lowerCamelCase      (	self			:       str		)						->    Tuple:
       _lowercase							     =       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}		)

       _lowercase							     =       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 		_lowerCamelCase      (	self			:       str		)						->    List[str]:
       pass
    def 		_lowerCamelCase      (	self			:       List[str]		)						->    List[str]:
       # Initialize image_processing
       _lowercase							     =       self.image_processing_class(**self.image_processor_dict		)
       # create random PIL videos
       _lowercase							     =       prepare_video_inputs(self.image_processor_tester   ,						equal_resolution=_lowercase		)
       for video in video_inputs:
          self.assertIsInstance(_lowercase   ,						_lowercase		)
          self.assertIsInstance(video[0]   ,						Image.Image		)

       # Test not batched input
       _lowercase							     =       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
       _lowercase							     =       image_processing(_lowercase   ,						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 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    Tuple:
       # Initialize image_processing
       _lowercase							     =       self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       _lowercase							     =       prepare_video_inputs(self.image_processor_tester   ,						equal_resolution=_lowercase   ,						numpify=_lowercase		)
       for video in video_inputs:
          self.assertIsInstance(_lowercase   ,						_lowercase		)
          self.assertIsInstance(video[0]   ,						np.ndarray		)

       # Test not batched input
       _lowercase							     =       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
       _lowercase							     =       image_processing(_lowercase   ,						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 		_lowerCamelCase      (	self			:       str		)						->    Union[str, Any]:
       # Initialize image_processing
       _lowercase							     =       self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       _lowercase							     =       prepare_video_inputs(self.image_processor_tester   ,						equal_resolution=_lowercase   ,						torchify=_lowercase		)
       for video in video_inputs:
          self.assertIsInstance(_lowercase   ,						_lowercase		)
          self.assertIsInstance(video[0]   ,						torch.Tensor		)

       # Test not batched input
       _lowercase							     =       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
       _lowercase							     =       image_processing(_lowercase   ,						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"],
           )   ,						)
"""simple docstring"""


from .number_theory.prime_numbers import next_prime
class   UpperCAmelCase_	:
    def __init__(	self			:       str   ,						_lowercase			:       int   ,						_lowercase			:       int | None = None   ,						_lowercase			:       float | None = None   ,						)						->    None:
       _lowercase							     =       size_table
       _lowercase							     =       [None] * self.size_table
       _lowercase							     =       0.75 if lim_charge is None else lim_charge
       _lowercase							     =       1 if charge_factor is None else charge_factor
       _lowercase							     =       []
       _lowercase							     =       {}
    def 		_lowerCamelCase      (	self			:       Any		)						->    Optional[Any]:
       return self._keys
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Dict:
       return sum(1 for slot in self.values if slot is not None		) / (
           self.size_table * self.charge_factor
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[str]		)						->    str:
       return key % self.size_table
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Union[str, Any]		)						->    Optional[Any]:
       print(f"""step {step_ord}"""		)
       print(list(range(len(self.values		)		)		)		)
       print(self.values		)
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       List[Any]		)						->    List[Any]:
       _lowercase							     =       1
       _lowercase							     =       values
       for value in values:
          self.insert_data(_lowercase		)
          self._step_by_step(_lowercase		)
          i += 1
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Union[str, Any]		)						->    List[Any]:
       _lowercase							     =       data
       _lowercase							     =       data
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Tuple=None		)						->    Any:
       _lowercase							     =       self.hash_function(key + 1		)

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

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




    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       self.hash_function(_lowercase		)

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

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

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