'''simple docstring'''







import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Any     		= StableDiffusionInstructPixaPixPipeline
						A_			:							Optional[Any]     		= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
						A_			:							Optional[int]     		= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						A_			:							Optional[Any]     		= IMAGE_TO_IMAGE_IMAGE_PARAMS
						A_			:							Any     		= IMAGE_TO_IMAGE_IMAGE_PARAMS




						def      __lowerCamelCase       (    self :       Dict      )     ->				Any:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :str						    = UNetaDConditionModel(
													    block_out_channels=(32, 64)							,							layers_per_block=2							,							sample_size=32							,							in_channels=8							,							out_channels=4							,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,							cross_attention_dim=32							,							)
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = PNDMScheduler(skip_prk_steps=UpperCamelCase_      )
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :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      )
													SCREAMING_SNAKE_CASE__     :str						    = 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							,							)
													SCREAMING_SNAKE_CASE__     :int						    = CLIPTextModel(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

													SCREAMING_SNAKE_CASE__     :Dict						    = {
													    'unet': unet,
													    'scheduler': scheduler,
													    'vae': vae,
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'safety_checker': None,
													    'feature_extractor': None,
													}
													return components




						def      __lowerCamelCase       (    self :       Tuple							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       int=0      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = floats_tensor((1, 3, 32, 32)							,							rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = image.cpu().permute(0							,							2							,							3							,							1      )[0]
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('RGB'      )
													if str(UpperCamelCase_      ).startswith('mps'      ):
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.manual_seed(UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :int						    = torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'image': image,
													    'generator': generator,
													    'num_inference_steps': 2,
													    'guidance_scale': 6.0,
													    'image_guidance_scale': 1,
													    'output_type': 'numpy',
													}
													return inputs




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :List[str]						    = 'cpu'  # ensure determinism for the device-dependent torch.Generator
													SCREAMING_SNAKE_CASE__     :str						    = self.get_dummy_components()
													SCREAMING_SNAKE_CASE__     :Dict						    = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = sd_pipe.to(UpperCamelCase_      )
													sd_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.get_dummy_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = sd_pipe(**UpperCamelCase_      ).images
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = image[0, -3:, -3:, -1]
													assert image.shape == (1, 32, 32, 3)
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




						def      __lowerCamelCase       (    self :       Dict      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Dict						    = 'cpu'  # ensure determinism for the device-dependent torch.Generator
													SCREAMING_SNAKE_CASE__     :str						    = self.get_dummy_components()
													SCREAMING_SNAKE_CASE__     :str						    = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = sd_pipe.to(UpperCamelCase_      )
													sd_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.get_dummy_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 'french fries'
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = sd_pipe(**UpperCamelCase_							,							negative_prompt=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = output.images
													SCREAMING_SNAKE_CASE__     :int						    = image[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Any						    = 'cpu'  # ensure determinism for the device-dependent torch.Generator
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.get_dummy_components()
													SCREAMING_SNAKE_CASE__     :str						    = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = sd_pipe.to(UpperCamelCase_      )
													sd_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = self.get_dummy_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = [inputs['prompt']] * 2

													SCREAMING_SNAKE_CASE__     :Any						    = np.array(inputs['image']      ).astype(np.floataa      ) / 255.0
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.from_numpy(UpperCamelCase_      ).unsqueeze(0      ).to(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image / 2 + 0.5
													SCREAMING_SNAKE_CASE__     :str						    = image.permute(0							,							3							,							1							,							2      )
													SCREAMING_SNAKE_CASE__     :Any						    = image.repeat(2							,							1							,							1							,							1      )

													SCREAMING_SNAKE_CASE__     :int						    = sd_pipe(**UpperCamelCase_      ).images
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = image[-1, -3:, -3:, -1]

													assert image.shape == (2, 32, 32, 3)
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




						def      __lowerCamelCase       (    self :       Any      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 'cpu'  # ensure determinism for the device-dependent torch.Generator
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.get_dummy_components()
													SCREAMING_SNAKE_CASE__     :List[Any]						    = EulerAncestralDiscreteScheduler(
													    beta_start=0.0_0085							,							beta_end=0.012							,							beta_schedule='scaled_linear'      )
													SCREAMING_SNAKE_CASE__     :Dict						    = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = sd_pipe.to(UpperCamelCase_      )
													sd_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :int						    = self.get_dummy_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = sd_pipe(**UpperCamelCase_      ).images
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = image[0, -3:, -3:, -1]

													SCREAMING_SNAKE_CASE__     :int						    = [round(UpperCamelCase_							,							4      ) for x in image_slice.flatten().tolist()]
													print(','.join([str(UpperCamelCase_      ) for x in slice]      )      )

													assert image.shape == (1, 32, 32, 3)
													SCREAMING_SNAKE_CASE__     :List[str]						    = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				Optional[Any]:
													super().test_inference_batch_single_identical(expected_max_diff=3e-3      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.get_dummy_components()
													SCREAMING_SNAKE_CASE__     :List[str]						    = StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = VaeImageProcessor(do_resize=UpperCamelCase_							,							do_normalize=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Dict						    = pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_							,							input_image_type='pt'      )      )[0]

													SCREAMING_SNAKE_CASE__     :str						    = components['vae']
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.get_dummy_inputs_by_type(UpperCamelCase_							,							input_image_type='pt'      )

													for image_param in self.image_latents_params:
																				if image_param in inputs.keys():
																											SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = vae.encode(inputs[image_param]      ).latent_dist.mode()

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = pipe(**UpperCamelCase_      )[0]

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = np.abs(out - out_latents_inputs      ).max()
													self.assertLess(UpperCamelCase_							,							1e-4							,							'passing latents as image input generate different result from passing image'      )




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




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Any:
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       int=0      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.manual_seed(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = load_image(
													    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'      )
													SCREAMING_SNAKE_CASE__     :Any						    = {
													    'prompt': 'turn him into a cyborg',
													    'image': image,
													    'generator': generator,
													    'num_inference_steps': 3,
													    'guidance_scale': 7.5,
													    'image_guidance_scale': 1.0,
													    'output_type': 'numpy',
													}
													return inputs




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = StableDiffusionInstructPixaPixPipeline.from_pretrained(
													    'timbrooks/instruct-pix2pix'							,							safety_checker=UpperCamelCase_      )
													pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )
													pipe.enable_attention_slicing()

													SCREAMING_SNAKE_CASE__     :int						    = self.get_inputs()
													SCREAMING_SNAKE_CASE__     :Any						    = pipe(**UpperCamelCase_      ).images
													SCREAMING_SNAKE_CASE__     :Any						    = image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 5_12, 5_12, 3)
													SCREAMING_SNAKE_CASE__     :Dict						    = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]      )

													assert np.abs(expected_slice - image_slice      ).max() < 1e-3




						def      __lowerCamelCase       (    self :       Any      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = StableDiffusionInstructPixaPixPipeline.from_pretrained(
													    'timbrooks/instruct-pix2pix'							,							safety_checker=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
													pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )
													pipe.enable_attention_slicing()

													SCREAMING_SNAKE_CASE__     :Any						    = self.get_inputs()
													SCREAMING_SNAKE_CASE__     :List[str]						    = pipe(**UpperCamelCase_      ).images
													SCREAMING_SNAKE_CASE__     :Dict						    = image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 5_12, 5_12, 3)
													SCREAMING_SNAKE_CASE__     :Dict						    = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]      )

													assert np.abs(expected_slice - image_slice      ).max() < 1e-3




						def      __lowerCamelCase       (    self :       int      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = StableDiffusionInstructPixaPixPipeline.from_pretrained(
													    'timbrooks/instruct-pix2pix'							,							safety_checker=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = DDIMScheduler.from_config(pipe.scheduler.config      )
													pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )
													pipe.enable_attention_slicing()

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.get_inputs()
													SCREAMING_SNAKE_CASE__     :int						    = pipe(**UpperCamelCase_      ).images
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 5_12, 5_12, 3)
													SCREAMING_SNAKE_CASE__     :Any						    = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]      )

													assert np.abs(expected_slice - image_slice      ).max() < 1e-3




						def      __lowerCamelCase       (    self :       Tuple      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Dict						    = 0

													def callback_fn(UpperCamelCase_ :       int							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       torch.FloatTensor      ) -> None:
																				SCREAMING_SNAKE_CASE__     :int						    = True
																				nonlocal number_of_steps
																				number_of_steps += 1
																				if step == 1:
																											SCREAMING_SNAKE_CASE__     :Any						    = latents.detach().cpu().numpy()
																											assert latents.shape == (1, 4, 64, 64)
																											SCREAMING_SNAKE_CASE__     :int						    = latents[0, -3:, -3:, -1]
																											SCREAMING_SNAKE_CASE__     :Any						    = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]      )

																											assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2
																				elif step == 2:
																											SCREAMING_SNAKE_CASE__     :int						    = latents.detach().cpu().numpy()
																											assert latents.shape == (1, 4, 64, 64)
																											SCREAMING_SNAKE_CASE__     :Any						    = latents[0, -3:, -3:, -1]
																											SCREAMING_SNAKE_CASE__     :Any						    = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]      )

																											assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = False

													SCREAMING_SNAKE_CASE__     :List[Any]						    = StableDiffusionInstructPixaPixPipeline.from_pretrained(
													    'timbrooks/instruct-pix2pix'							,							safety_checker=UpperCamelCase_							,							torch_dtype=torch.floataa      )
													SCREAMING_SNAKE_CASE__     :Dict						    = pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )
													pipe.enable_attention_slicing()

													SCREAMING_SNAKE_CASE__     :Tuple						    = self.get_inputs()
													pipe(**UpperCamelCase_							,							callback=UpperCamelCase_							,							callback_steps=1      )
													assert callback_fn.has_been_called
													assert number_of_steps == 3




						def      __lowerCamelCase       (    self :       Dict      )     ->				Optional[Any]:
													torch.cuda.empty_cache()
													torch.cuda.reset_max_memory_allocated()
													torch.cuda.reset_peak_memory_stats()

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = StableDiffusionInstructPixaPixPipeline.from_pretrained(
													    'timbrooks/instruct-pix2pix'							,							safety_checker=UpperCamelCase_							,							torch_dtype=torch.floataa      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )
													pipe.enable_attention_slicing(1      )
													pipe.enable_sequential_cpu_offload()

													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.get_inputs()
													SCREAMING_SNAKE_CASE__     :Dict						    = pipe(**UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = torch.cuda.max_memory_allocated()
													# make sure that less than 2.2 GB is allocated
													assert mem_bytes < 2.2 * 10**9




						def      __lowerCamelCase       (    self :       int      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.get_inputs()
													# resize to resolution that is divisible by 8 but not 16 or 32
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = inputs['image'].resize((5_04, 5_04)      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = 'timbrooks/instruct-pix2pix'
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = StableDiffusionInstructPixaPixPipeline.from_pretrained(
													    UpperCamelCase_							,							safety_checker=UpperCamelCase_							,							)
													pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )
													pipe.enable_attention_slicing()

													SCREAMING_SNAKE_CASE__     :Tuple						    = pipe(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = output.images[0]

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = image[2_55:2_58, 3_83:3_86, -1]

													assert image.shape == (5_04, 5_04, 3)
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-3







'''simple docstring'''







def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  int		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :list[list[str]]						    = [[] for _ in range(UpperCAmelCase__		)]
							SCREAMING_SNAKE_CASE__     :Any						    = key - 1

							if key <= 0:
														raise ValueError('Height of grid can\'t be 0 or negative'		)
							if key == 1 or len(UpperCAmelCase__		) <= key:
														return input_string

							for position, character in enumerate(UpperCAmelCase__		):
														SCREAMING_SNAKE_CASE__     :Dict						    = position % (lowest * 2)  # puts it in bounds
														SCREAMING_SNAKE_CASE__     :Tuple						    = min(UpperCAmelCase__				,						lowest * 2 - num		)  # creates zigzag pattern
														temp_grid[num].append(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :int						    = [''.join(UpperCAmelCase__		) for row in temp_grid]
							SCREAMING_SNAKE_CASE__     :str						    = ''.join(UpperCAmelCase__		)

							return output_string


def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  int		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[Any]						    = []
							SCREAMING_SNAKE_CASE__     :str						    = key - 1

							if key <= 0:
														raise ValueError('Height of grid can\'t be 0 or negative'		)
							if key == 1:
														return input_string

							SCREAMING_SNAKE_CASE__     :list[list[str]]						    = [[] for _ in range(UpperCAmelCase__		)]  # generates template
							for position in range(len(UpperCAmelCase__		)		):
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = position % (lowest * 2)  # puts it in bounds
														SCREAMING_SNAKE_CASE__     :Dict						    = min(UpperCAmelCase__				,						lowest * 2 - num		)  # creates zigzag pattern
														temp_grid[num].append('*'		)

							SCREAMING_SNAKE_CASE__     :Any						    = 0
							for row in temp_grid:  # fills in the characters
														SCREAMING_SNAKE_CASE__     :int						    = input_string[counter : counter + len(UpperCAmelCase__		)]
														grid.append(list(UpperCAmelCase__		)		)
														counter += len(UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :Tuple						    = ''  # reads as zigzag
							for position in range(len(UpperCAmelCase__		)		):
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = position % (lowest * 2)  # puts it in bounds
														SCREAMING_SNAKE_CASE__     :Any						    = min(UpperCAmelCase__				,						lowest * 2 - num		)  # creates zigzag pattern
														output_string += grid[num][0]
														grid[num].pop(0		)
							return output_string


def    lowerCamelCase       (			UpperCAmelCase__				:  str		)			->	dict[int, str]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :int						    = {}
							for key_guess in range(1				,						len(UpperCAmelCase__		)		):  # tries every key
														SCREAMING_SNAKE_CASE__     :List[str]						    = decrypt(UpperCAmelCase__				,						UpperCAmelCase__		)
							return results


if __name__ == "__main__":
		import doctest

		doctest.testmod()






