'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 					__magic_name__		(					_UpperCAmelCase,  unittest.TestCase):
			UpperCamelCase__       = KandinskyInpaintPipeline
			UpperCamelCase__       = ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
			UpperCamelCase__       = [
			    '''prompt''',
			    '''negative_prompt''',
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			    '''mask_image''',
			]
			UpperCamelCase__       = [
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''latents''',
			    '''guidance_scale''',
			    '''negative_prompt''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase__       = False







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										return 32







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return 32







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										return self.time_input_dim







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										return self.time_input_dim * 4







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return 100







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Optional[int]       		= XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""					)
										return tokenizer







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										torch.manual_seed(0					)
										lowercase_      :    int       		= MCLIPConfig(
										    numDims=self.cross_attention_dim       ,    transformerDimensions=self.text_embedder_hidden_size       ,    hidden_size=self.text_embedder_hidden_size       ,    intermediate_size=37       ,    num_attention_heads=4       ,    num_hidden_layers=5       ,    vocab_size=1005       ,    )

										lowercase_      :    List[str]       		= MultilingualCLIP(lowercase_					)
										lowercase_      :    Any       		= text_encoder.eval()

										return text_encoder







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										torch.manual_seed(0					)

										lowercase_      :    int       		= {
										    """in_channels""": 9,
										    # Out channels is double in channels because predicts mean and variance
										    """out_channels""": 8,
										    """addition_embed_type""": """text_image""",
										    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
										    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
										    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
										    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    """layers_per_block""": 1,
										    """encoder_hid_dim""": self.text_embedder_hidden_size,
										    """encoder_hid_dim_type""": """text_image_proj""",
										    """cross_attention_dim""": self.cross_attention_dim,
										    """attention_head_dim""": 4,
										    """resnet_time_scale_shift""": """scale_shift""",
										    """class_embed_type""": None,
										}

										lowercase_      :    Dict       		= UNetaDConditionModel(**lowercase_					)
										return model







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										return {
										    "block_out_channels": [32, 64],
										    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 12,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										torch.manual_seed(0					)
										lowercase_      :    Optional[Any]       		= VQModel(**self.dummy_movq_kwargs					)
										return model







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    int       		= self.dummy_text_encoder
										lowercase_      :    int       		= self.dummy_tokenizer
										lowercase_      :    List[str]       		= self.dummy_unet
										lowercase_      :    int       		= self.dummy_movq

										lowercase_      :    Optional[Any]       		= DDIMScheduler(
										    num_train_timesteps=1000       ,    beta_schedule="""linear"""       ,    beta_start=0.0_00_85       ,    beta_end=0.0_12       ,    clip_sample=lowercase_       ,    set_alpha_to_one=lowercase_       ,    steps_offset=1       ,    prediction_type="""epsilon"""       ,    thresholding=lowercase_       ,    )

										lowercase_      :    Any       		= {
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						Dict=0					):
										lowercase_      :    List[str]       		= floats_tensor((1, self.cross_attention_dim)       ,    rng=random.Random(lowercase_					)					).to(lowercase_					)
										lowercase_      :    Dict       		= floats_tensor((1, self.cross_attention_dim)       ,    rng=random.Random(seed + 1					)					).to(lowercase_					)
										# create init_image
										lowercase_      :    Optional[Any]       		= floats_tensor((1, 3, 64, 64)       ,    rng=random.Random(lowercase_					)					).to(lowercase_					)
										lowercase_      :    Dict       		= image.cpu().permute(0       ,    2       ,    3       ,    1					)[0]
										lowercase_      :    Dict       		= Image.fromarray(np.uinta(lowercase_					)					).convert("""RGB"""					).resize((256, 256)					)
										# create mask
										lowercase_      :    Tuple       		= np.ones((64, 64)       ,    dtype=np.floataa					)
										lowercase_      :    Any       		= 0

										if str(lowercase_					).startswith("""mps"""					):
																	lowercase_      :    Optional[Any]       		= torch.manual_seed(lowercase_					)
										else:
																	lowercase_      :    List[Any]       		= torch.Generator(device=lowercase_					).manual_seed(lowercase_					)
										lowercase_      :    Union[str, Any]       		= {
										    """prompt""": """horse""",
										    """image""": init_image,
										    """mask_image""": mask,
										    """image_embeds""": image_embeds,
										    """negative_image_embeds""": negative_image_embeds,
										    """generator""": generator,
										    """height""": 64,
										    """width""": 64,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 4.0,
										    """output_type""": """np""",
										}
										return inputs







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Optional[int]       		= """cpu"""

										lowercase_      :    Tuple       		= self.get_dummy_components()

										lowercase_      :    List[Any]       		= self.pipeline_class(**lowercase_					)
										lowercase_      :    Dict       		= pipe.to(lowercase_					)

										pipe.set_progress_bar_config(disable=lowercase_					)

										lowercase_      :    Union[str, Any]       		= pipe(**self.get_dummy_inputs(lowercase_					)					)
										lowercase_      :    Dict       		= output.images

										lowercase_      :    str       		= pipe(
										    **self.get_dummy_inputs(lowercase_					)       ,    return_dict=lowercase_       ,    )[0]

										lowercase_      :    List[Any]       		= image[0, -3:, -3:, -1]
										lowercase_      :    Optional[Any]       		= image_from_tuple[0, -3:, -3:, -1]

										print(f'''image.shape {image.shape}'''					)

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

										lowercase_      :    Optional[Any]       		= np.array(
										    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]					)

										assert (
										    np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
										), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
										assert (
										    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2
										), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										super().test_inference_batch_single_identical(expected_max_diff=3E-3					)





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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Any       		= load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""					)

										lowercase_      :    str       		= load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
										lowercase_      :    Dict       		= np.ones((768, 768)       ,    dtype=np.floataa					)
										lowercase_      :    Union[str, Any]       		= 0

										lowercase_      :    Optional[Any]       		= """a hat"""

										lowercase_      :    List[str]       		= KandinskyPriorPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-prior"""       ,    torch_dtype=torch.floataa					)
										pipe_prior.to(lowercase_					)

										lowercase_      :    Optional[Any]       		= KandinskyInpaintPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-inpaint"""       ,    torch_dtype=torch.floataa					)
										lowercase_      :    Tuple       		= pipeline.to(lowercase_					)
										pipeline.set_progress_bar_config(disable=lowercase_					)

										lowercase_      :    Optional[Any]       		= torch.Generator(device="""cpu"""					).manual_seed(0					)
										lowercase_		,		lowercase_      :    Any       		= pipe_prior(
										    lowercase_       ,    generator=lowercase_       ,    num_inference_steps=5       ,    negative_prompt=""""""       ,    ).to_tuple()

										lowercase_      :    Optional[int]       		= pipeline(
										    lowercase_       ,    image=lowercase_       ,    mask_image=lowercase_       ,    image_embeds=lowercase_       ,    negative_image_embeds=lowercase_       ,    generator=lowercase_       ,    num_inference_steps=100       ,    height=768       ,    width=768       ,    output_type="""np"""       ,    )

										lowercase_      :    Optional[int]       		= output.images[0]

										assert image.shape == (768, 768, 3)

										assert_mean_pixel_difference(lowercase_       ,    lowercase_					)






'''simple docstring'''




_lowercase		:				int	       =		"ABCDEFGHIJKLMNOPQRSTUVWXYZ"




def 							lowerCamelCase			(   )    ->   None:
							lowercase_      :    List[Any]       		= input("""Enter message: """				)
							lowercase_      :    str       		= input("""Enter key [alphanumeric]: """				)
							lowercase_      :    List[Any]       		= input("""Encrypt/Decrypt [e/d]: """				)

							if mode.lower().startswith("""e"""				):
														lowercase_      :    List[str]       		= """encrypt"""
														lowercase_      :    Optional[int]       		= encrypt_message(UpperCAmelCase__   ,   UpperCAmelCase__				)
							elif mode.lower().startswith("""d"""				):
														lowercase_      :    Any       		= """decrypt"""
														lowercase_      :    Optional[Any]       		= decrypt_message(UpperCAmelCase__   ,   UpperCAmelCase__				)

							print(F'''\n{mode.title()}ed message:'''				)
							print(UpperCAmelCase__				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      str				)    ->   str:
							return translate_message(UpperCAmelCase__   ,   UpperCAmelCase__   ,   """encrypt"""				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      str				)    ->   str:
							return translate_message(UpperCAmelCase__   ,   UpperCAmelCase__   ,   """decrypt"""				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      str				)    ->   str:
							lowercase_      :    Union[str, Any]       		= []
							lowercase_      :    List[Any]       		= 0
							lowercase_      :    str       		= key.upper()

							for symbol in message:
														lowercase_      :    Tuple       		= LETTERS.find(symbol.upper()				)
														if num != -1:
																					if mode == "encrypt":
																												num += LETTERS.find(key[key_index]				)
																					elif mode == "decrypt":
																												num -= LETTERS.find(key[key_index]				)

																					num %= len(UpperCAmelCase__				)

																					if symbol.isupper():
																												translated.append(LETTERS[num]				)
																					elif symbol.islower():
																												translated.append(LETTERS[num].lower()				)

																					key_index += 1
																					if key_index == len(UpperCAmelCase__				):
																												lowercase_      :    Any       		= 0
														else:
																					translated.append(UpperCAmelCase__				)
							return "".join(UpperCAmelCase__				)


if __name__ == "__main__":
						main()





