
"""simple docstring"""





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class __lowerCAmelCase					(       _lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					KandinskyVaaPriorPipeline
			snake_case						   =					["prompt"]
			snake_case						   =					["prompt", "negative_prompt"]
			snake_case						   =					[
			    "num_images_per_prompt",
			    "generator",
			    "num_inference_steps",
			    "latents",
			    "negative_prompt",
			    "guidance_scale",
			    "output_type",
			    "return_dict",
			]
			snake_case						   =					False




			@property
			def   lowerCamelCase__  (					self    :       int						)   ->			Dict:







								"""simple docstring"""
								return 32




			@property
			def   lowerCamelCase__  (					self    :       str						)   ->			int:







								"""simple docstring"""
								return 32




			@property
			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								return self.time_input_dim




			@property
			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			List[str]:







								"""simple docstring"""
								return self.time_input_dim * 4




			@property
			def   lowerCamelCase__  (					self    :       str						)   ->			Optional[int]:







								"""simple docstring"""
								return 100




			@property
			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)
								return tokenizer




			@property
			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Optional[Any]:







								"""simple docstring"""
								torch.manual_seed(0						)
								A_								=    CLIPTextConfig(
								    bos_token_id=0							, eos_token_id=2							, hidden_size=self.text_embedder_hidden_size							, projection_dim=self.text_embedder_hidden_size							, intermediate_size=37							, layer_norm_eps=1e-05							, num_attention_heads=4							, num_hidden_layers=5							, pad_token_id=1							, vocab_size=1_000							, )
								return CLIPTextModelWithProjection(_snake_case						)




			@property
			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			List[Any]:







								"""simple docstring"""
								torch.manual_seed(0						)

								A_								=    {
								    "num_attention_heads": 2,
								    "attention_head_dim": 12,
								    "embedding_dim": self.text_embedder_hidden_size,
								    "num_layers": 1,
								}

								A_								=    PriorTransformer(**_snake_case						)
								# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
								A_								=    nn.Parameter(torch.ones(model.clip_std.shape						)						)
								return model




			@property
			def   lowerCamelCase__  (					self    :       Dict						)   ->			str:







								"""simple docstring"""
								torch.manual_seed(0						)
								A_								=    CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size							, image_size=224							, projection_dim=self.text_embedder_hidden_size							, intermediate_size=37							, num_attention_heads=4							, num_channels=3							, num_hidden_layers=5							, patch_size=14							, )

								A_								=    CLIPVisionModelWithProjection(_snake_case						)
								return model




			@property
			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			Any:







								"""simple docstring"""
								A_								=    CLIPImageProcessor(
								    crop_size=224							, do_center_crop=_snake_case							, do_normalize=_snake_case							, do_resize=_snake_case							, image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							, image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							, resample=3							, size=224							, )

								return image_processor




			def   lowerCamelCase__  (					self    :       int						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    self.dummy_prior
								A_								=    self.dummy_image_encoder
								A_								=    self.dummy_text_encoder
								A_								=    self.dummy_tokenizer
								A_								=    self.dummy_image_processor

								A_								=    UnCLIPScheduler(
								    variance_type="fixed_small_log"							, prediction_type="sample"							, num_train_timesteps=1_000							, clip_sample=_snake_case							, clip_sample_range=1_0.0							, )

								A_								=    {
								    "prior": prior,
								    "image_encoder": image_encoder,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "scheduler": scheduler,
								    "image_processor": image_processor,
								}

								return components




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       Optional[int]							, _snake_case    :       Dict=0						)   ->			str:







								"""simple docstring"""
								if str(_snake_case						).startswith("mps"						):
													A_								=    torch.manual_seed(_snake_case						)
								else:
													A_								=    torch.Generator(device=_snake_case						).manual_seed(_snake_case						)
								A_								=    {
								    "prompt": "horse",
								    "generator": generator,
								    "guidance_scale": 4.0,
								    "num_inference_steps": 2,
								    "output_type": "np",
								}
								return inputs




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







								"""simple docstring"""
								A_								=    "cpu"

								A_								=    self.get_dummy_components()

								A_								=    self.pipeline_class(**_snake_case						)
								A_								=    pipe.to(_snake_case						)

								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    pipe(**self.get_dummy_inputs(_snake_case						)						)
								A_								=    output.image_embeds

								A_								=    pipe(
								    **self.get_dummy_inputs(_snake_case						)							, return_dict=_snake_case							, )[0]

								A_								=    image[0, -10:]
								A_								=    image_from_tuple[0, -10:]

								assert image.shape == (1, 32)

								A_								=    np.array(
								    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]						)

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




			@skip_mps
			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    torch_device == "cpu"
								A_								=    True
								A_								=    False

								self._test_inference_batch_single_identical(
								    test_max_difference=_snake_case							, relax_max_difference=_snake_case							, test_mean_pixel_difference=_snake_case							, )






			@skip_mps
			def   lowerCamelCase__  (					self    :       Any						)   ->			List[str]:







								"""simple docstring"""
								A_								=    torch_device == "cpu"
								A_								=    False

								self._test_attention_slicing_forward_pass(
								    test_max_difference=_snake_case							, test_mean_pixel_difference=_snake_case							, )






"""simple docstring"""





def 				A_  (__a					):

					'''simple docstring'''





					A_								=    len(__a					)
					while cur > 1:
										# Find the maximum number in arr
										A_								=    arr.index(max(arr[0:cur]					)					)
										# Reverse from 0 to mi
										A_								=    arr[mi::-1] + arr[mi + 1 : len(__a					)]
										# Reverse whole list
										A_								=    arr[cur - 1 :: -1] + arr[cur : len(__a					)]
										cur -= 1
					return arr


if __name__ == "__main__":
		UpperCamelCase_       :    Optional[Any]						      =			input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase_       :    Any						      =			[int(item) for item in user_input.split(''',''')]
		print(pancake_sort(unsorted))




