
"""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_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import OwlViTImageProcessor






class   _UpperCAmelCase					(       unittest.TestCase):
    def __init__(     self    ,     snake_case_    ,     snake_case_=7    ,     snake_case_=3    ,     snake_case_=18    ,     snake_case_=30    ,     snake_case_=4_00    ,     snake_case_=True    ,     snake_case_=None    ,     snake_case_=True    ,     snake_case_=None    ,     snake_case_=True    ,     snake_case_=[0.48145466, 0.4578275, 0.40821073]    ,     snake_case_=[0.26862954, 0.26130258, 0.27577711]    ,     snake_case_=True    ,     ):
      _snake_case  :    str            = parent
      _snake_case  :    Dict            = batch_size
      _snake_case  :    List[Any]            = num_channels
      _snake_case  :    int            = image_size
      _snake_case  :    Optional[int]            = min_resolution
      _snake_case  :    Optional[Any]            = max_resolution
      _snake_case  :    Tuple            = do_resize
      _snake_case  :    int            = size if size is not None else {"height": 18, "width": 18}
      _snake_case  :    Dict            = do_center_crop
      _snake_case  :    Optional[Any]            = crop_size if crop_size is not None else {"height": 18, "width": 18}
      _snake_case  :    Union[str, Any]            = do_normalize
      _snake_case  :    Dict            = image_mean
      _snake_case  :    Tuple            = image_std
      _snake_case  :    Any            = do_convert_rgb


    def        lowerCamelCase__						(     self  ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
      }






@require_torch
@require_vision
class   _UpperCAmelCase					(       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       OwlViTImageProcessor if is_vision_available() else None
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = OwlViTImageProcessingTester(self  )
    @property
    def        lowerCamelCase__						(     self  ):
      return self.image_processor_tester.prepare_image_processor_dict()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(snake_case_    ,     "do_resize"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "size"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_center_crop"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "center_crop"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_normalize"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_mean"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_std"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_convert_rgb"  )  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.image_processing_class.from_dict(self.image_processor_dict  )
      self.assertEqual(image_processor.size    ,     {"height": 18, "width": 18}  )
      self.assertEqual(image_processor.crop_size    ,     {"height": 18, "width": 18}  )

      _snake_case  :    str            = self.image_processing_class.from_dict(self.image_processor_dict    ,     size=42    ,     crop_size=84  )
      self.assertEqual(image_processor.size    ,     {"height": 42, "width": 42}  )
      self.assertEqual(image_processor.crop_size    ,     {"height": 84, "width": 84}  )
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    str            = self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _snake_case  :    Tuple            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_  )

      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     Image.Image  )

      # Test not batched input
      _snake_case  :    Dict            = image_processing(image_inputs[0]    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,     )

      # Test batched
      _snake_case  :    Dict            = image_processing(snake_case_    ,     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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,     )
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    Union[str, Any]            = self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _snake_case  :    Union[str, Any]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     numpify=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     np.ndarray  )

      # Test not batched input
      _snake_case  :    Tuple            = image_processing(image_inputs[0]    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,     )

      # Test batched
      _snake_case  :    Optional[int]            = image_processing(snake_case_    ,     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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,     )


    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    str            = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _snake_case  :    Optional[int]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     torchify=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     torch.Tensor  )

      # Test not batched input
      _snake_case  :    Tuple            = image_processing(image_inputs[0]    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,     )

      # Test batched
      _snake_case  :    Optional[Any]            = image_processing(snake_case_    ,     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.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		)