"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class   __snake_case			(	__lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, unittest.TestCase							):
      a__								=						StableDiffusionModelEditingPipeline
      a__								=						TEXT_TO_IMAGE_PARAMS
      a__								=						TEXT_TO_IMAGE_BATCH_PARAMS
      a__								=						TEXT_TO_IMAGE_IMAGE_PARAMS
      a__								=						TEXT_TO_IMAGE_IMAGE_PARAMS



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:	Tuple     				=      UNetaDConditionModel(
            block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=4       ,     out_channels=4       ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,     cross_attention_dim=32       ,     )
        a__:	Tuple     				=      DDIMScheduler()
        torch.manual_seed(0)
        a__:	List[str]     				=      AutoencoderKL(
            block_out_channels=[32, 64]       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,     latent_channels=4       ,     )
        torch.manual_seed(0)
        a__:	Any     				=      CLIPTextConfig(
            bos_token_id=0       ,     eos_token_id=2       ,     hidden_size=32       ,     intermediate_size=37       ,     layer_norm_eps=1e-05       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     pad_token_id=1       ,     vocab_size=10_00       ,     )
        a__:	str     				=      CLIPTextModel(lowercase)
        a__:	Any     				=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

        a__:	List[Any]     				=      {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase=0)      ->     int:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      torch.manual_seed(lowercase)
        a__:	Optional[int]     				=      {
            'prompt': 'A field of roses',
            'generator': generator,
            # Setting height and width to None to prevent OOMs on CPU.
            'height': None,
            'width': None,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	List[str]     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	Union[str, Any]     				=      self.get_dummy_components()
        a__:	List[Any]     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	int     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	str     				=      self.get_dummy_inputs(lowercase)
        a__:	str     				=      sd_pipe(**lowercase).images
        a__:	Optional[Any]     				=      image[0, -3:, -3:, -1]
        assert image.shape == (1, 64, 64, 3)

        a__:	Dict     				=      np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	List[Any]     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	List[Any]     				=      self.get_dummy_components()
        a__:	Union[str, Any]     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	Union[str, Any]     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	Dict     				=      self.get_dummy_inputs(lowercase)
        a__:	Optional[int]     				=      'french fries'
        a__:	List[str]     				=      sd_pipe(**lowercase       ,     negative_prompt=lowercase)
        a__:	str     				=      output.images
        a__:	Optional[int]     				=      image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        a__:	List[Any]     				=      np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2



      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	str     				=      self.get_dummy_components()
        a__:	List[Any]     				=      EulerAncestralDiscreteScheduler(
            beta_start=0.00085       ,     beta_end=0.012       ,     beta_schedule='scaled_linear')
        a__:	List[Any]     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	str     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	Optional[Any]     				=      self.get_dummy_inputs(lowercase)
        a__:	Optional[Any]     				=      sd_pipe(**lowercase).images
        a__:	List[str]     				=      image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        a__:	Any     				=      np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2



      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        a__:	str     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	Union[str, Any]     				=      self.get_dummy_components()
        a__:	str     				=      PNDMScheduler()
        a__:	Dict     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	Tuple     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	Optional[Any]     				=      self.get_dummy_inputs(lowercase)
        # the pipeline does not expect pndm so test if it raises error.
        with self.assertRaises(lowercase):
          a__:	str     				=      sd_pipe(**lowercase).images



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        super().test_inference_batch_single_identical(expected_max_diff=5e-3)



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        super().test_attention_slicing_forward_pass(expected_max_diff=5e-3)







@slow
@require_torch_gpu
class   __snake_case			(	unittest.TestCase							):



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



      def     lowerCamelCase_  (		self       ,     lowercase=0)      ->     int:





        '''simple docstring'''





        a__:	Any     				=      torch.manual_seed(lowercase)
        a__:	Any     				=      {
            'prompt': 'A field of roses',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      'CompVis/stable-diffusion-v1-4'
        a__:	List[str]     				=      StableDiffusionModelEditingPipeline.from_pretrained(lowercase       ,     safety_checker=lowercase)
        pipe.to(lowercase)
        pipe.set_progress_bar_config(disable=lowercase)
        pipe.enable_attention_slicing()

        a__:	Optional[int]     				=      self.get_inputs()
        a__:	Optional[int]     				=      pipe(**lowercase).images
        a__:	int     				=      image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_12, 5_12, 3)

        a__:	Optional[Any]     				=      np.array(
            [0.6749496, 0.6386453, 0.51443267, 0.66094905, 0.61921215, 0.5491332, 0.5744417, 0.58075106, 0.5174658])

        assert np.abs(expected_slice - image_slice).max() < 1e-2

        # make sure image changes after editing
        pipe.edit_model('A pack of roses'       ,     'A pack of blue roses')

        a__:	Dict     				=      pipe(**lowercase).images
        a__:	str     				=      image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_12, 5_12, 3)

        assert np.abs(expected_slice - image_slice).max() > 1e-1



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        a__:	str     				=      'CompVis/stable-diffusion-v1-4'
        a__:	int     				=      DDIMScheduler.from_pretrained(lowercase       ,     subfolder='scheduler')
        a__:	Any     				=      StableDiffusionModelEditingPipeline.from_pretrained(
            lowercase       ,     scheduler=lowercase       ,     safety_checker=lowercase)
        a__:	List[str]     				=      pipe.to(lowercase)
        pipe.set_progress_bar_config(disable=lowercase)
        pipe.enable_attention_slicing(1)
        pipe.enable_sequential_cpu_offload()

        a__:	Dict     				=      self.get_inputs()
        a__:	Tuple     				=      pipe(**lowercase)

        a__:	Any     				=      torch.cuda.max_memory_allocated()
        # make sure that less than 4.4 GB is allocated
        assert mem_bytes < 4.4 * 10**9








import os
import sys

from . import rsa_key_generator as rkg

UpperCAmelCase_    =128
UpperCAmelCase_    =256



def    UpperCAmelCase							(  _snake_case    ,			_snake_case = DEFAULT_BLOCK_SIZE				):
							lowerCAmelCase =		message.encode('''ascii'''				)

							lowerCAmelCase =		[]
							for block_start in range(0    ,			len(_snake_case				)    ,			_snake_case				):
														lowerCAmelCase =		0
														for i in range(_snake_case    ,			min(block_start + block_size    ,			len(_snake_case				)				)				):
																					block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
														block_ints.append(_snake_case				)
							return block_ints



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case = DEFAULT_BLOCK_SIZE				):
							lowerCAmelCase =		[]
							for block_int in block_ints:
														lowerCAmelCase =		[]
														for i in range(block_size - 1    ,			-1    ,			-1				):
																					if len(_snake_case				) + i < message_length:
																												lowerCAmelCase =		block_int // (BYTE_SIZE**i)
																												lowerCAmelCase =		block_int % (BYTE_SIZE**i)
																												block_message.insert(0    ,			chr(_snake_case				)				)
														message.extend(_snake_case				)
							return "".join(_snake_case				)



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case = DEFAULT_BLOCK_SIZE				):
							lowerCAmelCase =		[]
							lowerCAmelCase ,  lowerCAmelCase =		key

							for block in get_blocks_from_text(_snake_case    ,			_snake_case				):
														encrypted_blocks.append(pow(_snake_case    ,			_snake_case    ,			_snake_case				)				)
							return encrypted_blocks



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = DEFAULT_BLOCK_SIZE    ,			):
							lowerCAmelCase =		[]
							lowerCAmelCase ,  lowerCAmelCase =		key
							for block in encrypted_blocks:
														decrypted_blocks.append(pow(_snake_case    ,			_snake_case    ,			_snake_case				)				)
							return get_text_from_blocks(_snake_case    ,			_snake_case    ,			_snake_case				)



def    UpperCAmelCase							(  _snake_case				):
							with open(_snake_case				) as fo:
														lowerCAmelCase =		fo.read()
							lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		content.split(''','''				)
							return (int(_snake_case				), int(_snake_case				), int(_snake_case				))



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = DEFAULT_BLOCK_SIZE    ,			):
							lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		read_key_file(_snake_case				)
							if key_size < block_size * 8:
														sys.exit(
														    '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
														    '''requires the block size to be equal to or greater than the key size. '''
														    '''Either decrease the block size or use different keys.'''.format(
														        block_size * 8    ,			_snake_case				)				)

							lowerCAmelCase =		[str(_snake_case				) for i in encrypt_message(_snake_case    ,			(n, e)    ,			_snake_case				)]

							lowerCAmelCase =		''','''.join(_snake_case				)
							lowerCAmelCase =		F"""{len(_snake_case				)}_{block_size}_{encrypted_content}"""
							with open(_snake_case    ,			'''w'''				) as fo:
														fo.write(_snake_case				)
							return encrypted_content



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		read_key_file(_snake_case				)
							with open(_snake_case				) as fo:
														lowerCAmelCase =		fo.read()
							lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		content.split('''_'''				)
							lowerCAmelCase =		int(_snake_case				)
							lowerCAmelCase =		int(_snake_case				)

							if key_size < block_size * 8:
														sys.exit(
														    '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
														    '''requires the block size to be equal to or greater than the key size. '''
														    '''Did you specify the correct key file and encrypted file?'''.format(
														        block_size * 8    ,			_snake_case				)				)

							lowerCAmelCase =		[]
							for block in encrypted_message.split(''','''				):
														encrypted_blocks.append(int(_snake_case				)				)

							return decrypt_message(_snake_case    ,			_snake_case    ,			(n, d)    ,			_snake_case				)



def    UpperCAmelCase							(  ):
							lowerCAmelCase =		'''encrypted_file.txt'''
							lowerCAmelCase =		input(R'''Encrypt\Decrypt [e\d]: '''				)

							if response.lower().startswith('''e'''				):
														lowerCAmelCase =		'''encrypt'''
							elif response.lower().startswith('''d'''				):
														lowerCAmelCase =		'''decrypt'''

							if mode == "encrypt":
														if not os.path.exists('''rsa_pubkey.txt'''				):
																					rkg.make_key_files('''rsa'''    ,			1024				)

														lowerCAmelCase =		input('''\nEnter message: '''				)
														lowerCAmelCase =		'''rsa_pubkey.txt'''
														print(F"""Encrypting and writing to {filename}..."""				)
														lowerCAmelCase =		encrypt_and_write_to_file(_snake_case    ,			_snake_case    ,			_snake_case				)

														print('''\nEncrypted text:'''				)
														print(_snake_case				)

							elif mode == "decrypt":
														lowerCAmelCase =		'''rsa_privkey.txt'''
														print(F"""Reading from {filename} and decrypting..."""				)
														lowerCAmelCase =		read_from_file_and_decrypt(_snake_case    ,			_snake_case				)
														print('''writing decryption to rsa_decryption.txt...'''				)
														with open('''rsa_decryption.txt'''    ,			'''w'''				) as dec:
																					dec.write(_snake_case				)

														print('''\nDecryption:'''				)
														print(_snake_case				)


if __name__ == "__main__":
		main()



