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 SwinaSRImageProcessor
 from transformers.image_transforms import get_image_size




class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[int]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int=7							,      lowerCamelCase							:		Any=3							,      lowerCamelCase							:		List[Any]=1_8							,      lowerCamelCase							:		List[Any]=3_0							,      lowerCamelCase							:		Any=4_0_0							,      lowerCamelCase							:		Optional[Any]=True							,      lowerCamelCase							:		int=1 / 2_5_5							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		Optional[int]=8							,      )			->				Dict:
        _UpperCamelCase  :							List[Any]	    =	parent
        _UpperCamelCase  :							Dict	    =	batch_size
        _UpperCamelCase  :							Optional[Any]	    =	num_channels
        _UpperCamelCase  :							Union[str, Any]	    =	image_size
        _UpperCamelCase  :							int	    =	min_resolution
        _UpperCamelCase  :							List[str]	    =	max_resolution
        _UpperCamelCase  :							str	    =	do_rescale
        _UpperCamelCase  :							Tuple	    =	rescale_factor
        _UpperCamelCase  :							Optional[int]	    =	do_pad
        _UpperCamelCase  :							int	    =	pad_size

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Any:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		str=False							,      lowerCamelCase							:		Union[str, Any]=False							,      lowerCamelCase							:		List[str]=False	)			->				Dict:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
               _UpperCamelCase  :							Optional[Any]	    =	[]
               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:
               _UpperCamelCase  :							Any	    =	[]
               for i in range(self.batch_size	):
                      _UpperCamelCase    ,   _UpperCamelCase  :							int	    =	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
               _UpperCamelCase  :							Tuple	    =	[Image.fromarray(np.moveaxis(lowerCamelCase							,      0							,      -1	)	) for x in image_inputs]

        if torchify:
               _UpperCamelCase  :							int	    =	[torch.from_numpy(lowerCamelCase	) for x in image_inputs]

        return image_inputs







@require_torch
@require_vision
class  __lowerCAmelCase	(							a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			SwinaSRImageProcessor if is_vision_available() else None

 def        __snake_case       (      self							:		Union[str, Any]	)			->				List[Any]:
        _UpperCamelCase  :							int	    =	SwinaSRImageProcessingTester(self	)

 @property
 def        __snake_case       (      self							:		Union[str, Any]	)			->				int:
        return self.image_processor_tester.prepare_image_processor_dict()

 def        __snake_case       (      self							:		int	)			->				int:
        _UpperCamelCase  :							str	    =	self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(lowerCamelCase							,      'do_rescale'	)	)
        self.assertTrue(hasattr(lowerCamelCase							,      'rescale_factor'	)	)
        self.assertTrue(hasattr(lowerCamelCase							,      'do_pad'	)	)
        self.assertTrue(hasattr(lowerCamelCase							,      'pad_size'	)	)

 def        __snake_case       (      self							:		Any	)			->				Dict:
        pass

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Tuple	)			->				int:
        _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	get_image_size(lowerCamelCase	)
        _UpperCamelCase  :							int	    =	self.image_processor_tester.pad_size

        _UpperCamelCase  :							List[Any]	    =	(old_height // size + 1) * size - old_height
        _UpperCamelCase  :							Union[str, Any]	    =	(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width

 def        __snake_case       (      self							:		Any	)			->				Optional[Any]:
        # Initialize image_processor
        _UpperCamelCase  :							List[str]	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _UpperCamelCase  :							Any	    =	self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase	)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase							,      Image.Image	)

        # Test not batched input
        _UpperCamelCase  :							Dict	    =	image_processor(image_inputs[0]							,      return_tensors='pt'	).pixel_values
        _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	self.calculate_expected_size(np.array(image_inputs[0]	)	)
        self.assertEqual(
            encoded_images.shape							,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							,      )

 def        __snake_case       (      self							:		Any	)			->				Tuple:
        # Initialize image_processor
        _UpperCamelCase  :							Optional[int]	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        _UpperCamelCase  :							Tuple	    =	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
        _UpperCamelCase  :							Optional[Any]	    =	image_processor(image_inputs[0]							,      return_tensors='pt'	).pixel_values
        _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	self.calculate_expected_size(image_inputs[0]	)
        self.assertEqual(
            encoded_images.shape							,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							,      )

 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        # Initialize image_processor
        _UpperCamelCase  :							Optional[Any]	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        _UpperCamelCase  :							Any	    =	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
        _UpperCamelCase  :							List[str]	    =	image_processor(image_inputs[0]							,      return_tensors='pt'	).pixel_values
        _UpperCamelCase    ,   _UpperCamelCase  :							Optional[Any]	    =	self.calculate_expected_size(image_inputs[0]	)
        self.assertEqual(
            encoded_images.shape							,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							,      )




from .number_theory.prime_numbers import next_prime




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int | None = None							,      lowerCamelCase							:		float | None = None							,      )			->				None:
        _UpperCamelCase  :							Tuple	    =	size_table
        _UpperCamelCase  :							Tuple	    =	[None] * self.size_table
        _UpperCamelCase  :							int	    =	0.7_5 if lim_charge is None else lim_charge
        _UpperCamelCase  :							Tuple	    =	1 if charge_factor is None else charge_factor
        _UpperCamelCase  :							list	    =	[]
        _UpperCamelCase  :							dict	    =	{}

 def        __snake_case       (      self							:		List[Any]	)			->				Union[str, Any]:
        return self._keys

 def        __snake_case       (      self							:		List[Any]	)			->				Optional[int]:
        return sum(1 for slot in self.values if slot is not None	) / (
            self.size_table * self.charge_factor
        )

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		int	)			->				Any:
        return key % self.size_table

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				Optional[Any]:
        print(f"step {step_ord}"	)
        print(list(range(len(self.values	)	)	)	)
        print(self.values	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							int	    =	1
        _UpperCamelCase  :							Union[str, Any]	    =	values
        for value in values:
               self.insert_data(lowerCamelCase	)
               self._step_by_step(lowerCamelCase	)
               i += 1

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							int	    =	data
        _UpperCamelCase  :							Optional[int]	    =	data

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		Optional[int]							,      lowerCamelCase							:		str=None	)			->				List[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	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  :							Optional[int]	    =	self.hash_function(new_key + 1	)
               else:
                      _UpperCamelCase  :							Tuple	    =	None
                      break

        return new_key

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

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		str	)			->				Union[str, Any]:
        _UpperCamelCase  :							Dict	    =	self.hash_function(lowerCamelCase	)

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

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

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



