




"""simple docstring"""

import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor
class 			lowerCamelCase__    (  unittest.TestCase			):


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE = False							):



					"""simple docstring"""







					snake_case		:						Any								=				10

					if only_cross_attention:
							snake_case		:						Tuple								=				12
					else:
							# when only cross attention is not set, the cross attention dim must be the same as the query dim
							snake_case		:						Optional[int]								=				query_dim

					return {
					    "query_dim": query_dim,
					    "cross_attention_dim": cross_attention_dim,
					    "heads": 2,
					    "dim_head": 4,
					    "added_kv_proj_dim": 6,
					    "norm_num_groups": 1,
					    "only_cross_attention": only_cross_attention,
					    "processor": AttnAddedKVProcessor(),
					}


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						Tuple								=				2

					snake_case		:						Any								=				torch.rand(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, 3				, 2							)
					snake_case		:						int								=				torch.rand(SCREAMING_SNAKE_CASE				, 4				, SCREAMING_SNAKE_CASE							)
					snake_case		:						str								=				None

					return {
					    "hidden_states": hidden_states,
					    "encoder_hidden_states": encoder_hidden_states,
					    "attention_mask": attention_mask,
					}



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					torch.manual_seed(0							)

					snake_case		:						List[Any]								=				self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE							)
					snake_case		:						int								=				Attention(**SCREAMING_SNAKE_CASE							)

					self.assertTrue(attn.to_k is not None							)
					self.assertTrue(attn.to_v is not None							)

					snake_case		:						int								=				self.get_forward_arguments(
					    query_dim=constructor_args["query_dim"]				, added_kv_proj_dim=constructor_args["added_kv_proj_dim"]							)

					snake_case		:						str								=				attn(**SCREAMING_SNAKE_CASE							)

					# only self attention

					torch.manual_seed(0							)

					snake_case		:						Optional[Any]								=				self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE							)
					snake_case		:						int								=				Attention(**SCREAMING_SNAKE_CASE							)

					self.assertTrue(attn.to_k is None							)
					self.assertTrue(attn.to_v is None							)

					snake_case		:						Union[str, Any]								=				self.get_forward_arguments(
					    query_dim=constructor_args["query_dim"]				, added_kv_proj_dim=constructor_args["added_kv_proj_dim"]							)

					snake_case		:						Any								=				attn(**SCREAMING_SNAKE_CASE							)

					self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()							)







class 			lowerCamelCase__    (  unittest.TestCase			):


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						str								=				DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe"				, safety_checker=SCREAMING_SNAKE_CASE							)

					snake_case		:						List[str]								=				pipe(
					    "foo"				, num_inference_steps=2				, generator=torch.Generator("cpu"							).manual_seed(0							)				, output_type="np"				, ).images

					# the initial conversion succeeds
					snake_case		:						Optional[Any]								=				DiffusionPipeline.from_pretrained(
					    "hf-internal-testing/tiny-stable-diffusion-pipe"				, device_map="sequential"				, safety_checker=SCREAMING_SNAKE_CASE							)

					snake_case		:						Dict								=				pipe(
					    "foo"				, num_inference_steps=2				, generator=torch.Generator("cpu"							).manual_seed(0							)				, output_type="np"				, ).images

					with tempfile.TemporaryDirectory() as tmpdir:
							# save the converted model
							pipe.save_pretrained(SCREAMING_SNAKE_CASE							)

							# can also load the converted weights
							snake_case		:						Tuple								=				DiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE				, device_map="sequential"				, safety_checker=SCREAMING_SNAKE_CASE							)

					snake_case		:						List[Any]								=				pipe(
					    "foo"				, num_inference_steps=2				, generator=torch.Generator("cpu"							).manual_seed(0							)				, output_type="np"				, ).images

					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, atol=1E-5							)							)
					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, atol=1E-5							)							)







"""simple docstring"""

import random



def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):

		def is_sorted(lowercase__  :     int   ):
				for i in range(len(lowercase__   ) - 1   ):
						if collection[i] > collection[i + 1]:
								return False
				return True

		while not is_sorted(lowercase__   ):
				random.shuffle(lowercase__   )
		return collection


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

