

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_a:    List[str] 							=			transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def       __lowerCAmelCase      (						A    ):
	if isinstance(A       ,     torch.Tensor    ):
		return image
	elif isinstance(A       ,     PIL.Image.Image    ):
		UpperCAmelCase_       				=			[image]

	UpperCAmelCase_       				=			[trans(img.convert("RGB"    )    ) for img in image]
	UpperCAmelCase_       				=			torch.stack(A    )
	return image




class 						__UpperCamelCase    (					lowercase						):




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		super().__init__()

		# make sure scheduler can always be converted to DDIM
		UpperCAmelCase_       				=			DDIMScheduler.from_config(scheduler.config							)

		self.register_modules(unet=lowerCAmelCase		,	scheduler=lowerCAmelCase							)




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		if strength < 0 or strength > 1:
			raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"							)




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			min(int(num_inference_steps * strength							)		,	lowerCAmelCase							)

		UpperCAmelCase_       				=			max(num_inference_steps - init_timestep		,	0							)
		UpperCAmelCase_       				=			self.scheduler.timesteps[t_start:]

		return timesteps, num_inference_steps - t_start




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						List[Any]=None							):


		'''simple docstring'''







		if not isinstance(lowerCAmelCase		,	(torch.Tensor, PIL.Image.Image, list)							):
			raise ValueError(
			    F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase							)}"							)

		UpperCAmelCase_       				=			image.to(device=lowerCAmelCase		,	dtype=lowerCAmelCase							)

		if isinstance(lowerCAmelCase		,	lowerCAmelCase							) and len(lowerCAmelCase							) != batch_size:
			raise ValueError(
			    F"You have passed a list of generators of length {len(lowerCAmelCase							)}, but requested an effective batch"
			    F" size of {batch_size}. Make sure the batch size matches the length of the generators."							)

		UpperCAmelCase_       				=			init_latents.shape
		UpperCAmelCase_       				=			randn_tensor(lowerCAmelCase		,	generator=lowerCAmelCase		,	device=lowerCAmelCase		,	dtype=lowerCAmelCase							)

		# get latents
		print("add noise to latents at timestep"		,	lowerCAmelCase							)
		UpperCAmelCase_       				=			self.scheduler.add_noise(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
		UpperCAmelCase_       				=			init_latents

		return latents




	@torch.no_grad()
	def __call__(		self	:						Optional[int]		,	lowerCAmelCase	:						Union[torch.FloatTensor, PIL.Image.Image] = None		,	lowerCAmelCase	:						float = 0.8		,	lowerCAmelCase	:						int = 1		,	lowerCAmelCase	:						Optional[Union[torch.Generator, List[torch.Generator]]] = None		,	lowerCAmelCase	:						float = 0.0		,	lowerCAmelCase	:						int = 50		,	lowerCAmelCase	:						Optional[bool] = None		,	lowerCAmelCase	:						Optional[str] = "pil"		,	lowerCAmelCase	:						bool = True		,	):


		'''simple docstring'''







		self.check_inputs(lowerCAmelCase							)

		# 2. Preprocess image
		UpperCAmelCase_       				=			preprocess(lowerCAmelCase							)

		# 3. set timesteps
		self.scheduler.set_timesteps(lowerCAmelCase		,	device=self.device							)
		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.get_timesteps(lowerCAmelCase		,	lowerCAmelCase		,	self.device							)
		UpperCAmelCase_       				=			timesteps[:1].repeat(lowerCAmelCase							)

		# 4. Prepare latent variables
		UpperCAmelCase_       				=			self.prepare_latents(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	self.unet.dtype		,	self.device		,	lowerCAmelCase							)
		UpperCAmelCase_       				=			latents

		# 5. Denoising loop
		for t in self.progress_bar(lowerCAmelCase							):
			# 1. predict noise model_output
			UpperCAmelCase_       				=			self.unet(lowerCAmelCase		,	lowerCAmelCase							).sample

			# 2. predict previous mean of image x_t-1 and add variance depending on eta
			# eta corresponds to η in paper and should be between [0, 1]
			# do x_t -> x_t-1
			UpperCAmelCase_       				=			self.scheduler.step(
			    lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	eta=lowerCAmelCase		,	use_clipped_model_output=lowerCAmelCase		,	generator=lowerCAmelCase		,	).prev_sample

		UpperCAmelCase_       				=			(image / 2 + 0.5).clamp(0		,	1							)
		UpperCAmelCase_       				=			image.cpu().permute(0		,	2		,	3		,	1							).numpy()
		if output_type == "pil":
			UpperCAmelCase_       				=			self.numpy_to_pil(lowerCAmelCase							)

		if not return_dict:
			return (image, latent_timestep.item())

		return ImagePipelineOutput(images=lowerCAmelCase							)


def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			generate_pascal_triangle(A    )
	for row_idx in range(A    ):
		# Print left spaces
		for _ in range(num_rows - row_idx - 1    ):
			print(end=" "    )
		# Print row values
		for col_idx in range(row_idx + 1    ):
			if col_idx != row_idx:
				print(triangle[row_idx][col_idx]       ,     end=" "    )
			else:
				print(triangle[row_idx][col_idx]       ,     end=""    )
		print()



def       __lowerCAmelCase      (						A    ):

	if not isinstance(A       ,     A    ):
		raise TypeError("The input value of 'num_rows' should be 'int'"    )

	if num_rows == 0:
		return []
	elif num_rows < 0:
		raise ValueError(
		    "The input value of 'num_rows' should be greater than or equal to 0"    )

	UpperCAmelCase_       				=			[]
	for current_row_idx in range(A    ):
		UpperCAmelCase_       				=			populate_current_row(A       ,     A    )
		triangle.append(A    )
	return triangle



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			[-1] * (current_row_idx + 1)
	# first and last elements of current row are equal to 1
	UpperCAmelCase_				,   UpperCAmelCase_       				=			1, 1
	for current_col_idx in range(1       ,     A    ):
		calculate_current_element(
		    A       ,     A       ,     A       ,     A    )
	return current_row



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A       ,     ):
	UpperCAmelCase_       				=			triangle[current_row_idx - 1][current_col_idx - 1]
	UpperCAmelCase_       				=			triangle[current_row_idx - 1][current_col_idx]
	UpperCAmelCase_       				=			above_to_left_elt + above_to_right_elt



def       __lowerCAmelCase      (						A    ):

	if not isinstance(A       ,     A    ):
		raise TypeError("The input value of 'num_rows' should be 'int'"    )

	if num_rows == 0:
		return []
	elif num_rows < 0:
		raise ValueError(
		    "The input value of 'num_rows' should be greater than or equal to 0"    )

	UpperCAmelCase_       				=			[[1]]

	for row_index in range(1       ,     A    ):
		UpperCAmelCase_       				=			[0] + result[-1] + [0]
		UpperCAmelCase_       				=			row_index + 1
		# Calculate the number of distinct elements in a row
		UpperCAmelCase_       				=			sum(divmod(A       ,     2    )    )
		UpperCAmelCase_       				=			[
		    temp_row[i - 1] + temp_row[i] for i in range(1       ,     distinct_elements + 1    )
		]
		UpperCAmelCase_       				=			row_first_half[: (row_index + 1) // 2]
		row_second_half.reverse()
		UpperCAmelCase_       				=			row_first_half + row_second_half
		result.append(A    )

	return result



def       __lowerCAmelCase      (						):
	from collections.abc import Callable
	from timeit import timeit

	def benchmark_a_function(A       ,     A    ) -> None:
		UpperCAmelCase_       				=			F"{func.__name__}({value})"
		UpperCAmelCase_       				=			timeit(F"__main__.{call}"       ,     setup="import __main__"    )
		# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
		print(F"{call:38} -- {timing:.4f} seconds"    )

	for value in range(15    ):  # (1, 7, 14):
		for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
			benchmark_a_function(A       ,     A    )
		print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()