





from typing import List, Optional, Union

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

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCAmelCase_												=       logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase_												=       '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase=8				)	->		Optional[Any]:






				"""simple docstring"""


				snake_case_		:					List[Any]			    =						height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				snake_case_		:					List[Any]			    =						width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase=512				,       _UpperCamelCase=512				)	->		List[Any]:






				"""simple docstring"""


				snake_case_		:					Dict			    =						pil_image.resize((w, h)				,       resample=Image.BICUBIC				,       reducing_gap=1				)
				snake_case_		:					Any			    =						np.array(pil_image.convert('''RGB'''				)				)
				snake_case_		:					Dict			    =						arr.astype(np.floataa				) / 127.5 - 1
				snake_case_		:					Dict			    =						np.transpose(_UpperCamelCase				,       [2, 0, 1]				)
				snake_case_		:					Any			    =						torch.from_numpy(_UpperCamelCase				).unsqueeze(0				)
				return image





class       __lowerCAmelCase (    _a  ):






			def __init__(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							)  ->     List[Any]:


							'''simple docstring'''



							super().__init__()

							self.register_modules(
							    unet=__magic_name__      ,							scheduler=__magic_name__      ,							movq=__magic_name__      ,							)
							snake_case_		:					List[str]			    =						2 ** (len(self.movq.config.block_out_channels					) - 1)






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						min(int(num_inference_steps * strength					)      ,							__magic_name__					)

							snake_case_		:					Union[str, Any]			    =						max(num_inference_steps - init_timestep      ,							0					)
							snake_case_		:					List[Any]			    =						self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__=None					)  ->     Tuple:


							'''simple docstring'''



							if not isinstance(__magic_name__      ,							(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(__magic_name__					)}'''					)

							snake_case_		:					Dict			    =						image.to(device=__magic_name__      ,							dtype=__magic_name__					)

							snake_case_		:					List[str]			    =						batch_size * num_images_per_prompt

							if image.shape[1] == 4:
											snake_case_		:					Any			    =						image

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

											elif isinstance(__magic_name__      ,							__magic_name__					):
															snake_case_		:					Union[str, Any]			    =						[
															    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(__magic_name__					)
															]
															snake_case_		:					str			    =						torch.cat(__magic_name__      ,							dim=0					)
											else:
															snake_case_		:					Any			    =						self.movq.encode(__magic_name__					).latent_dist.sample(__magic_name__					)

											snake_case_		:					Tuple			    =						self.movq.config.scaling_factor * init_latents

							snake_case_		:					Optional[int]			    =						torch.cat([init_latents]      ,							dim=0					)

							snake_case_		:					Union[str, Any]			    =						init_latents.shape
							snake_case_		:					Optional[Any]			    =						randn_tensor(__magic_name__      ,							generator=__magic_name__      ,							device=__magic_name__      ,							dtype=__magic_name__					)

							# get latents
							snake_case_		:					Optional[int]			    =						self.scheduler.add_noise(__magic_name__      ,							__magic_name__      ,							__magic_name__					)

							snake_case_		:					Tuple			    =						init_latents

							return latents






			def     lowerCamelCase						(self      ,							__magic_name__=0					)  ->     Tuple:


							'''simple docstring'''



							if is_accelerate_available():
											from accelerate import cpu_offload
							else:
											raise ImportError('''Please install accelerate via `pip install accelerate`'''					)

							snake_case_		:					Any			    =						torch.device(F'''cuda:{gpu_id}'''					)

							snake_case_		:					Union[str, Any]			    =						[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
															cpu_offload(__magic_name__      ,							__magic_name__					)






			def     lowerCamelCase						(self      ,							__magic_name__=0					)  ->     List[str]:


							'''simple docstring'''



							if is_accelerate_available() and is_accelerate_version('''>='''      ,							'''0.17.0.dev0'''					):
											from accelerate import cpu_offload_with_hook
							else:
											raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''					)

							snake_case_		:					int			    =						torch.device(F'''cuda:{gpu_id}'''					)

							if self.device.type != "cpu":
											self.to('''cpu'''      ,							silence_dtype_warnings=__magic_name__					)
											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							snake_case_		:					Optional[Any]			    =						None
							for cpu_offloaded_model in [self.unet, self.movq]:
											snake_case_    ,							snake_case_		:					Optional[Any]			    =						cpu_offload_with_hook(__magic_name__      ,							__magic_name__      ,							prev_module_hook=__magic_name__					)

							# We'll offload the last model manually.
							snake_case_		:					Optional[Any]			    =						hook






			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def     lowerCamelCase						(self					)  ->     Dict:


							'''simple docstring'''



							if not hasattr(self.unet      ,							'''_hf_hook'''					):
											return self.device
							for module in self.unet.modules():
											if (
											    hasattr(__magic_name__      ,							'''_hf_hook'''					)
											    and hasattr(module._hf_hook      ,							'''execution_device'''					)
											    and module._hf_hook.execution_device is not None
											):
															return torch.device(module._hf_hook.execution_device					)
							return self.device






			@torch.no_grad()
			@replace_example_docstring(__magic_name__					)
			def __call__(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__ = 512      ,							__magic_name__ = 512      ,							__magic_name__ = 100      ,							__magic_name__ = 4.0      ,							__magic_name__ = 0.3      ,							__magic_name__ = 1      ,							__magic_name__ = None      ,							__magic_name__ = "pil"      ,							__magic_name__ = True      ,							)  ->     List[str]:


							'''simple docstring'''



							snake_case_		:					Dict			    =						self._execution_device

							snake_case_		:					Tuple			    =						guidance_scale > 1.0

							if isinstance(__magic_name__      ,							__magic_name__					):
											snake_case_		:					Union[str, Any]			    =						torch.cat(__magic_name__      ,							dim=0					)
							if isinstance(__magic_name__      ,							__magic_name__					):
											snake_case_		:					Dict			    =						torch.cat(__magic_name__      ,							dim=0					)
							if isinstance(__magic_name__      ,							__magic_name__					):
											snake_case_		:					str			    =						torch.cat(__magic_name__      ,							dim=0					)

							snake_case_		:					List[Any]			    =						image_embeds.shape[0]

							if do_classifier_free_guidance:
											snake_case_		:					str			    =						image_embeds.repeat_interleave(__magic_name__      ,							dim=0					)
											snake_case_		:					Dict			    =						negative_image_embeds.repeat_interleave(__magic_name__      ,							dim=0					)
											snake_case_		:					str			    =						hint.repeat_interleave(__magic_name__      ,							dim=0					)

							snake_case_		:					Tuple			    =						torch.cat([negative_image_embeds, image_embeds]      ,							dim=0					).to(dtype=self.unet.dtype      ,							device=__magic_name__					)
							snake_case_		:					int			    =						torch.cat([hint, hint]      ,							dim=0					).to(dtype=self.unet.dtype      ,							device=__magic_name__					)

							if not isinstance(__magic_name__      ,							__magic_name__					):
											snake_case_		:					Optional[Any]			    =						[image]
							if not all(isinstance(__magic_name__      ,							(PIL.Image.Image, torch.Tensor)					) for i in image					):
											raise ValueError(
											    F'''Input is in incorrect format: {[type(__magic_name__					) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''					)

							snake_case_		:					Tuple			    =						torch.cat([prepare_image(__magic_name__      ,							__magic_name__      ,							__magic_name__					) for i in image]      ,							dim=0					)
							snake_case_		:					Tuple			    =						image.to(dtype=image_embeds.dtype      ,							device=__magic_name__					)

							snake_case_		:					List[str]			    =						self.movq.encode(__magic_name__					)['''latents''']
							snake_case_		:					List[str]			    =						latents.repeat_interleave(__magic_name__      ,							dim=0					)
							self.scheduler.set_timesteps(__magic_name__      ,							device=__magic_name__					)
							snake_case_    ,							snake_case_		:					Dict			    =						self.get_timesteps(__magic_name__      ,							__magic_name__      ,							__magic_name__					)
							snake_case_		:					str			    =						timesteps[:1].repeat(batch_size * num_images_per_prompt					)
							snake_case_    ,							snake_case_		:					Optional[int]			    =						downscale_height_and_width(__magic_name__      ,							__magic_name__      ,							self.movq_scale_factor					)
							snake_case_		:					str			    =						self.prepare_latents(
							    __magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							image_embeds.dtype      ,							__magic_name__      ,							__magic_name__					)
							for i, t in enumerate(self.progress_bar(__magic_name__					)					):
											# expand the latents if we are doing classifier free guidance
											snake_case_		:					List[Any]			    =						torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

											snake_case_		:					Union[str, Any]			    =						{'''image_embeds''': image_embeds, '''hint''': hint}
											snake_case_		:					Dict			    =						self.unet(
											    sample=__magic_name__      ,							timestep=__magic_name__      ,							encoder_hidden_states=__magic_name__      ,							added_cond_kwargs=__magic_name__      ,							return_dict=__magic_name__      ,							)[0]

											if do_classifier_free_guidance:
															snake_case_    ,							snake_case_		:					List[str]			    =						noise_pred.split(latents.shape[1]      ,							dim=1					)
															snake_case_    ,							snake_case_		:					Optional[int]			    =						noise_pred.chunk(2					)
															snake_case_    ,							snake_case_		:					Optional[Any]			    =						variance_pred.chunk(2					)
															snake_case_		:					Union[str, Any]			    =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
															snake_case_		:					Any			    =						torch.cat([noise_pred, variance_pred_text]      ,							dim=1					)

											if not (
											    hasattr(self.scheduler.config      ,							'''variance_type'''					)
											    and self.scheduler.config.variance_type in ["learned", "learned_range"]
											):
															snake_case_    ,							snake_case_		:					Tuple			    =						noise_pred.split(latents.shape[1]      ,							dim=1					)

											# compute the previous noisy sample x_t -> x_t-1

											snake_case_		:					List[Any]			    =						self.scheduler.step(
											    __magic_name__      ,							__magic_name__      ,							__magic_name__      ,							generator=__magic_name__      ,							)[0]

							# post-processing
							snake_case_		:					Union[str, Any]			    =						self.movq.decode(__magic_name__      ,							force_not_quantize=__magic_name__					)['''sample''']

							if output_type not in ["pt", "np", "pil"]:
											raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''					)

							if output_type in ["np", "pil"]:
											snake_case_		:					str			    =						image * 0.5 + 0.5
											snake_case_		:					Union[str, Any]			    =						image.clamp(0      ,							1					)
											snake_case_		:					int			    =						image.cpu().permute(0      ,							2      ,							3      ,							1					).float().numpy()

							if output_type == "pil":
											snake_case_		:					Optional[Any]			    =						self.numpy_to_pil(__magic_name__					)

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=__magic_name__					)







from math import radians as angle_to_radians
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
lowerCAmelCase_												=       9.80_665






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		None:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase				,       (int, float)				):
								raise TypeError('''Invalid velocity. Should be a positive number.'''				)

				if not isinstance(_UpperCamelCase				,       (int, float)				):
								raise TypeError('''Invalid angle. Range is 1-90 degrees.'''				)

				# Ensure valid angle
				if angle > 90 or angle < 1:
								raise ValueError('''Invalid angle. Range is 1-90 degrees.'''				)

				# Ensure valid velocity
				if init_velocity < 0:
								raise ValueError('''Invalid velocity. Should be a positive number.'''				)






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		float:






				"""simple docstring"""


				check_args(_UpperCamelCase				,       _UpperCamelCase				)
				snake_case_		:					List[str]			    =						angle_to_radians(2 * angle				)
				return round(init_velocity**2 * sin(_UpperCamelCase				) / g				,       2				)






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		float:






				"""simple docstring"""


				check_args(_UpperCamelCase				,       _UpperCamelCase				)
				snake_case_		:					Tuple			    =						angle_to_radians(_UpperCamelCase				)
				return round(init_velocity**2 * sin(_UpperCamelCase				) ** 2 / (2 * g)				,       2				)






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		float:






				"""simple docstring"""


				check_args(_UpperCamelCase				,       _UpperCamelCase				)
				snake_case_		:					Optional[Any]			    =						angle_to_radians(_UpperCamelCase				)
				return round(2 * init_velocity * sin(_UpperCamelCase				) / g				,       2				)






def   lowerCamelCase_		(       )	->		None:






				"""simple docstring"""


				snake_case_    ,							snake_case_		:					Dict			    =						25, 20
				assert horizontal_distance(_UpperCamelCase				,       _UpperCamelCase				) == 40.97
				assert max_height(_UpperCamelCase				,       _UpperCamelCase				) == 3.73
				assert total_time(_UpperCamelCase				,       _UpperCamelCase				) == 1.74


if __name__ == "__main__":
	from doctest import testmod

	testmod()

	# Get input from user
	lowerCAmelCase_												=       float(input('''Initial Velocity: ''').strip())

	# Get input from user
	lowerCAmelCase_												=       float(input('''angle: ''').strip())

	# Print results
	print()
	print('''Results: ''')
	print(F'''Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]''')
	print(F'''Maximum Height: {max_height(init_vel, angle)!s} [m]''')
	print(F'''Total Time: {total_time(init_vel, angle)!s} [s]''')
