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,
            )							,      )









"""simple docstring"""
# Lint as: python3

import hashlib
from typing import Union






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, int, bytes]						)    ->       bytes:
 if isinstance(__lowerCAmelCase  ,	__lowerCAmelCase						):
  # Data already in bytes, returns as it as
  return hash_data
 elif isinstance(__lowerCAmelCase  ,	__lowerCAmelCase						):
  # We keep the data as it as for it ot be later encoded to UTF-8
  # However replace `\\` with `/` for Windows compatibility
  _UpperCamelCase      :	int					      =  hash_data.replace("\\"  ,	"/"						)
 elif isinstance(__lowerCAmelCase  ,	__lowerCAmelCase						):
  _UpperCamelCase      :	Union[str, Any]					      =  str(__lowerCAmelCase						)
 else:
  # If data is not of the required type, raise error
  raise InvalidKeyError(__lowerCAmelCase						)

 return hash_data.encode("utf-8"						)







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 def __init__(self					,     lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	int					      =  "\nFAILURE TO GENERATE DATASET: Invalid key type detected"
  _UpperCamelCase      :	Tuple					      =  F"\nFound Key {hash_data} of type {type(lowerCAmelCase__   )}"
  _UpperCamelCase      :	str					      =  "\nKeys should be either str, int or bytes type"
  super().__init__(F"{self.prefix}{self.err_msg}{self.suffix}"   )







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 def __init__(self					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=""   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[int]					      =  key
  _UpperCamelCase      :	Tuple					      =  duplicate_key_indices
  _UpperCamelCase      :	int					      =  fix_msg
  _UpperCamelCase      :	List[Any]					      =  "Found multiple examples generated with the same key"
  if len(lowerCAmelCase__   ) <= 20:
   _UpperCamelCase      :	Optional[Any]					      =  F"\nThe examples at index {', '.join(lowerCAmelCase__   )} have the key {key}"
  else:
   _UpperCamelCase      :	Union[str, Any]					      =  F"\nThe examples at index {', '.join(duplicate_key_indices[:20]   )}... ({len(lowerCAmelCase__   ) - 20} more) have the key {key}"
  _UpperCamelCase      :	List[Any]					      =  "\n" + fix_msg if fix_msg else ""
  super().__init__(F"{self.prefix}{self.err_msg}{self.suffix}"   )







class  _SCREAMING_SNAKE_CASE       :



 '''simple docstring'''





 def __init__(self					,     lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	Union[str, Any]					      =  hashlib.mda(_as_bytes(lowerCAmelCase__   )   )






 def lowercase_      (self					,     lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	Any					      =  self._split_mda.copy()
  _UpperCamelCase      :	List[str]					      =  _as_bytes(lowerCAmelCase__   )
  mda.update(lowerCAmelCase__   )
  # Convert to integer with hexadecimal conversion
  return int(mda.hexdigest()					,     16   )



