
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = ['vqvae']



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       Dict:
					super().__init__()
					self.register_modules(unet=lowercase						,  scheduler=lowercase						,  mel=lowercase						,  vqvae=lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					return 50 if isinstance(self.scheduler						,  lowercase ) else 1000



	@torch.no_grad()
	def __call__(    self						,  lowercase = 1						,  lowercase = None						,  lowercase = None						,  lowercase = 0						,  lowercase = 0						,  lowercase = None						,  lowercase = None						,  lowercase = 0						,  lowercase = 0						,  lowercase = None						,  lowercase = 0						,  lowercase = None						,  lowercase = None						,  lowercase=True						,  )				->       Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
					lowerCamelCase_						=			steps or self.get_default_steps()
					self.scheduler.set_timesteps(lowercase )
					lowerCamelCase_						=			step_generator or generator
					# For backwards compatibility
					if type(self.unet.config.sample_size ) == int:
									lowerCamelCase_						=			(self.unet.config.sample_size, self.unet.config.sample_size)
					if noise is None:
									lowerCamelCase_						=			randn_tensor(
									    (
									        batch_size,
									        self.unet.config.in_channels,
									        self.unet.config.sample_size[0],
									        self.unet.config.sample_size[1],
									    )						,  generator=lowercase						,  device=self.device						,  )
					lowerCamelCase_						=			noise
					lowerCamelCase_						=			None

					if audio_file is not None or raw_audio is not None:
									self.mel.load_audio(lowercase						,  lowercase )
									lowerCamelCase_						=			self.mel.audio_slice_to_image(lowercase )
									lowerCamelCase_						=			np.frombuffer(input_image.tobytes()						,  dtype="uint8" ).reshape(
									    (input_image.height, input_image.width) )
									lowerCamelCase_						=			(input_image / 255) * 2 - 1
									lowerCamelCase_						=			torch.tensor(input_image[np.newaxis, :, :]						,  dtype=torch.float ).to(self.device )

									if self.vqvae is not None:
													lowerCamelCase_						=			self.vqvae.encode(torch.unsqueeze(lowercase						,  0 ) ).latent_dist.sample(
													    generator=lowercase )[0]
													lowerCamelCase_						=			self.vqvae.config.scaling_factor * input_images

									if start_step > 0:
													lowerCamelCase_						=			self.scheduler.add_noise(lowercase						,  lowercase						,  self.scheduler.timesteps[start_step - 1] )

									lowerCamelCase_						=			(
									    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
									)
									lowerCamelCase_						=			int(mask_start_secs * pixels_per_second )
									lowerCamelCase_						=			int(mask_end_secs * pixels_per_second )
									lowerCamelCase_						=			self.scheduler.add_noise(lowercase						,  lowercase						,  torch.tensor(self.scheduler.timesteps[start_step:] ) )

					for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
									if isinstance(self.unet						,  lowercase ):
													lowerCamelCase_						=			self.unet(lowercase						,  lowercase						,  lowercase )["sample"]
									else:
													lowerCamelCase_						=			self.unet(lowercase						,  lowercase )["sample"]

									if isinstance(self.scheduler						,  lowercase ):
													lowerCamelCase_						=			self.scheduler.step(
													    model_output=lowercase						,  timestep=lowercase						,  sample=lowercase						,  eta=lowercase						,  generator=lowercase						,  )["prev_sample"]
									else:
													lowerCamelCase_						=			self.scheduler.step(
													    model_output=lowercase						,  timestep=lowercase						,  sample=lowercase						,  generator=lowercase						,  )["prev_sample"]

									if mask is not None:
													if mask_start > 0:
																	lowerCamelCase_						=			mask[:, step, :, :mask_start]
													if mask_end > 0:
																	lowerCamelCase_						=			mask[:, step, :, -mask_end:]

					if self.vqvae is not None:
									# 0.18215 was scaling factor used in training to ensure unit variance
									lowerCamelCase_						=			1 / self.vqvae.config.scaling_factor * images
									lowerCamelCase_						=			self.vqvae.decode(lowercase )["sample"]

					lowerCamelCase_						=			(images / 2 + 0.5).clamp(0						,  1 )
					lowerCamelCase_						=			images.cpu().permute(0						,  2						,  3						,  1 ).numpy()
					lowerCamelCase_						=			(images * 255).round().astype("uint8" )
					lowerCamelCase_						=			list(
					    (Image.fromarray(_[:, :, 0] ) for _ in images)
					    if images.shape[3] == 1
					    else (Image.fromarray(lowercase						,  mode="RGB" ).convert("L" ) for _ in images) )

					lowerCamelCase_						=			[self.mel.image_to_audio(lowercase ) for _ in images]
					if not return_dict:
									return images, (self.mel.get_sample_rate(), audios)

					return BaseOutput(**AudioPipelineOutput(np.array(lowercase )[:, np.newaxis, :] )						,  **ImagePipelineOutput(lowercase ) )



	@torch.no_grad()
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = 50 )				->       np.ndarray:
					assert isinstance(self.scheduler						,  lowercase )
					self.scheduler.set_timesteps(lowercase )
					lowerCamelCase_						=			np.array(
					    [np.frombuffer(image.tobytes()						,  dtype="uint8" ).reshape((1, image.height, image.width) ) for image in images] )
					lowerCamelCase_						=			(sample / 255) * 2 - 1
					lowerCamelCase_						=			torch.Tensor(lowercase ).to(self.device )

					for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,  (0,) ) ):
									lowerCamelCase_						=			t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
									lowerCamelCase_						=			self.scheduler.alphas_cumprod[t]
									lowerCamelCase_						=			(
									    self.scheduler.alphas_cumprod[prev_timestep]
									    if prev_timestep >= 0
									    else self.scheduler.final_alpha_cumprod
									)
									lowerCamelCase_						=			1 - alpha_prod_t
									lowerCamelCase_						=			self.unet(lowercase						,  lowercase )["sample"]
									lowerCamelCase_						=			(1 - alpha_prod_t_prev) ** 0.5 * model_output
									lowerCamelCase_						=			(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
									lowerCamelCase_						=			sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

					return sample



	@staticmethod
	def     SCREAMING_SNAKE_CASE_(    lowercase						,  lowercase						,  lowercase )				->       torch.Tensor:
					lowerCamelCase_						=			acos(torch.dot(torch.flatten(lowercase )						,  torch.flatten(lowercase ) ) / torch.norm(lowercase ) / torch.norm(lowercase ) )
					return sin((1 - alpha) * theta ) * xa / sin(lowercase ) + sin(alpha * theta ) * xa / sin(lowercase )




import math





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = 0	, lowerCamelCase__ = 0    ):
				lowerCamelCase_						=			end or len(lowerCamelCase__    )
				for i in range(lowerCamelCase__	, lowerCamelCase__    ):
								lowerCamelCase_						=			i
								lowerCamelCase_						=			array[i]
								while temp_index != start and temp_index_value < array[temp_index - 1]:
												lowerCamelCase_						=			array[temp_index - 1]
												temp_index -= 1
								lowerCamelCase_						=			temp_index_value
				return array





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):  # Max Heap
				lowerCamelCase_						=			index
				lowerCamelCase_						=			2 * index + 1  # Left Node
				lowerCamelCase_						=			2 * index + 2  # Right Node

				if left_index < heap_size and array[largest] < array[left_index]:
								lowerCamelCase_						=			left_index

				if right_index < heap_size and array[largest] < array[right_index]:
								lowerCamelCase_						=			right_index

				if largest != index:
								lowerCamelCase_	,    lowerCamelCase_						=			array[largest], array[index]
								heapify(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			len(lowerCamelCase__    )

				for i in range(n // 2	, -1	, -1    ):
								heapify(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )

				for i in range(n - 1	, 0	, -1    ):
								lowerCamelCase_	,    lowerCamelCase_						=			array[0], array[i]
								heapify(lowerCamelCase__	, 0	, lowerCamelCase__    )

				return array





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				if (array[first_index] > array[middle_index]) != (
				    array[first_index] > array[last_index]
				):
								return array[first_index]
				elif (array[middle_index] > array[first_index]) != (
				    array[middle_index] > array[last_index]
				):
								return array[middle_index]
				else:
								return array[last_index]





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			low
				lowerCamelCase_						=			high
				while True:
								while array[i] < pivot:
												i += 1
								j -= 1
								while pivot < array[j]:
												j -= 1
								if i >= j:
												return i
								lowerCamelCase_	,    lowerCamelCase_						=			array[j], array[i]
								i += 1





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				if len(lowerCamelCase__    ) == 0:
								return array
				lowerCamelCase_						=			2 * math.ceil(math.loga(len(lowerCamelCase__    )    )    )
				lowerCamelCase_						=			1_6
				return intro_sort(lowerCamelCase__	, 0	, len(lowerCamelCase__    )	, lowerCamelCase__	, lowerCamelCase__    )





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				while end - start > size_threshold:
								if max_depth == 0:
												return heap_sort(lowerCamelCase__    )
								max_depth -= 1
								lowerCamelCase_						=			median_of_a(lowerCamelCase__	, lowerCamelCase__	, start + ((end - start) // 2) + 1	, end - 1    )
								lowerCamelCase_						=			partition(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
								intro_sort(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
								lowerCamelCase_						=			p
				return insertion_sort(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__A				=input('''Enter numbers separated by a comma : ''').strip()
			__A				=[float(item) for item in user_input.split(''',''')]
			print(sort(unsorted))


