






def   lowerCamelCase      (		a_				,		a_			)			->    list[list[int]]:
							lowerCAmelCase_       		=  []
							lowerCAmelCase_       		=  len(a_			)
							for i in range(a_			):
														lowerCAmelCase_       		=  []
														for j in range(a_			):
																					lowerCAmelCase_       		=  0
																					for k in range(a_			):
																												lowerCAmelCase_       		=  val + matrix_a[i][k] * matrix_b[k][j]
																					list_a.append(a_			)
														matrix_c.append(a_			)
							return matrix_c
def   lowerCamelCase      (		a_			)			->    list[list[int]]:
							return [[int(row == column			) for column in range(a_			)] for row in range(a_			)]
def   lowerCamelCase      (		a_			)			->    int:
							if n <= 1:
														return n
							lowerCAmelCase_       		=  identity(2			)
							lowerCAmelCase_       		=  [[1, 1], [1, 0]]
							lowerCAmelCase_       		=  n - 1
							while n > 0:
														if n % 2 == 1:
																					lowerCAmelCase_       		=  multiply(a_				,		a_			)
														lowerCAmelCase_       		=  multiply(a_				,		a_			)
														lowerCAmelCase_       		=  int(n / 2			)
							return res_matrix[0][0]
def   lowerCamelCase      (		a_			)			->    int:
							if n <= 1:
														return n
							lowerCAmelCase_       		=  0
							lowerCAmelCase_       		=  1
							for _ in range(2				,		n + 1			):
														lowerCAmelCase_		,     lowerCAmelCase_       		=  fiba, fiba + fiba
							return fiba
def   lowerCamelCase      (		)			->    None:
							for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
														lowerCAmelCase_       		=  int(''.join(c for c in ordinal if c in '0123456789'			)			)  # 1000th --> 1000
														print(
														    F'''{ordinal} fibonacci number using matrix exponentiation is '''
														    F'''{nth_fibonacci_matrix(a_			)} and using bruteforce is '''
														    F'''{nth_fibonacci_bruteforce(a_			)}\n'''			)
							# from timeit import timeit
							# print(timeit("nth_fibonacci_matrix(1000000)",
							#              "from main import nth_fibonacci_matrix", number=5))
							# print(timeit("nth_fibonacci_bruteforce(1000000)",
							#              "from main import nth_fibonacci_bruteforce", number=5))
							# 2.3342058970001744
							# 57.256506615000035


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()











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      (		a_				,		a_				,		a_=8			)			->    Tuple:
							lowerCAmelCase_       		=  height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							lowerCAmelCase_       		=  width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor
def   lowerCamelCase      (		a_				,		a_=512				,		a_=512			)			->    Any:
							lowerCAmelCase_       		=  pil_image.resize((w, h)				,		resample=Image.BICUBIC				,		reducing_gap=1			)
							lowerCAmelCase_       		=  np.array(pil_image.convert('RGB'			)			)
							lowerCAmelCase_       		=  arr.astype(np.floataa			) / 127.5 - 1
							lowerCAmelCase_       		=  np.transpose(a_				,		[2, 0, 1]			)
							lowerCAmelCase_       		=  torch.from_numpy(a_			).unsqueeze(0			)
							return image




class 							a_				(		a_	):


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    )      ->      List[Any]:
														'''simple docstring'''

														super().__init__()

														self.register_modules(
														    unet=lowercase_    ,    scheduler=lowercase_    ,    movq=lowercase_    ,    )
														lowerCAmelCase_       		=  2 ** (len(self.movq.config.block_out_channels     ) - 1)

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_     )      ->      Tuple:
														'''simple docstring'''

														lowerCAmelCase_       		=  min(int(num_inference_steps * strength     )    ,    lowercase_     )

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

														return timesteps, num_inference_steps - t_start

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_=None     )      ->      str:
														'''simple docstring'''

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

														lowerCAmelCase_       		=  image.to(device=lowercase_    ,    dtype=lowercase_     )

														lowerCAmelCase_       		=  batch_size * num_images_per_prompt

														if image.shape[1] == 4:
																					lowerCAmelCase_       		=  image

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

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

																					lowerCAmelCase_       		=  self.movq.config.scaling_factor * init_latents

														lowerCAmelCase_       		=  torch.cat([init_latents]    ,    dim=0     )

														lowerCAmelCase_       		=  init_latents.shape
														lowerCAmelCase_       		=  randn_tensor(lowercase_    ,    generator=lowercase_    ,    device=lowercase_    ,    dtype=lowercase_     )

														# get latents
														lowerCAmelCase_       		=  self.scheduler.add_noise(lowercase_    ,    lowercase_    ,    lowercase_     )

														lowerCAmelCase_       		=  init_latents

														return latents

							def   _lowercase (	self    ,    lowercase_=0     )      ->      int:
														'''simple docstring'''

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

														lowerCAmelCase_       		=  torch.device(f'''cuda:{gpu_id}'''     )

														lowerCAmelCase_       		=  [
														    self.unet,
														    self.movq,
														]
														for cpu_offloaded_model in models:
																					if cpu_offloaded_model is not None:
																												cpu_offload(lowercase_    ,    lowercase_     )

							def   _lowercase (	self    ,    lowercase_=0     )      ->      Union[str, Any]:
														'''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.'     )

														lowerCAmelCase_       		=  torch.device(f'''cuda:{gpu_id}'''     )

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

														lowerCAmelCase_       		=  None
														for cpu_offloaded_model in [self.unet, self.movq]:
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  cpu_offload_with_hook(lowercase_    ,    lowercase_    ,    prev_module_hook=lowercase_     )

														# We'll offload the last model manually.
														lowerCAmelCase_       		=  hook

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

														if not hasattr(self.unet    ,    '_hf_hook'     ):
																					return self.device
														for module in self.unet.modules():
																					if (
																					    hasattr(lowercase_    ,    '_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(lowercase_     )
							def __call__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_ = 5_1_2    ,    lowercase_ = 5_1_2    ,    lowercase_ = 1_0_0    ,    lowercase_ = 4.0    ,    lowercase_ = 0.3    ,    lowercase_ = 1    ,    lowercase_ = None    ,    lowercase_ = "pil"    ,    lowercase_ = True    ,    )      ->      int:
														'''simple docstring'''

														lowerCAmelCase_       		=  self._execution_device

														lowerCAmelCase_       		=  guidance_scale > 1.0

														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )
														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )
														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )

														lowerCAmelCase_       		=  image_embeds.shape[0]

														if do_classifier_free_guidance:
																					lowerCAmelCase_       		=  image_embeds.repeat_interleave(lowercase_    ,    dim=0     )
																					lowerCAmelCase_       		=  negative_image_embeds.repeat_interleave(lowercase_    ,    dim=0     )
																					lowerCAmelCase_       		=  hint.repeat_interleave(lowercase_    ,    dim=0     )

														lowerCAmelCase_       		=  torch.cat([negative_image_embeds, image_embeds]    ,    dim=0     ).to(dtype=self.unet.dtype    ,    device=lowercase_     )
														lowerCAmelCase_       		=  torch.cat([hint, hint]    ,    dim=0     ).to(dtype=self.unet.dtype    ,    device=lowercase_     )

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

														lowerCAmelCase_       		=  torch.cat([prepare_image(lowercase_    ,    lowercase_    ,    lowercase_     ) for i in image]    ,    dim=0     )
														lowerCAmelCase_       		=  image.to(dtype=image_embeds.dtype    ,    device=lowercase_     )

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

																					lowerCAmelCase_       		=  {'image_embeds': image_embeds, 'hint': hint}
																					lowerCAmelCase_       		=  self.unet(
																					    sample=lowercase_    ,    timestep=lowercase_    ,    encoder_hidden_states=lowercase_    ,    added_cond_kwargs=lowercase_    ,    return_dict=lowercase_    ,    )[0]

																					if do_classifier_free_guidance:
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  noise_pred.split(latents.shape[1]    ,    dim=1     )
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  noise_pred.chunk(2     )
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  variance_pred.chunk(2     )
																												lowerCAmelCase_       		=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																												lowerCAmelCase_       		=  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"]
																					):
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  noise_pred.split(latents.shape[1]    ,    dim=1     )

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

																					lowerCAmelCase_       		=  self.scheduler.step(
																					    lowercase_    ,    lowercase_    ,    lowercase_    ,    generator=lowercase_    ,    )[0]

														# post-processing
														lowerCAmelCase_       		=  self.movq.decode(lowercase_    ,    force_not_quantize=lowercase_     )['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"]:
																					lowerCAmelCase_       		=  image * 0.5 + 0.5
																					lowerCAmelCase_       		=  image.clamp(0    ,    1     )
																					lowerCAmelCase_       		=  image.cpu().permute(0    ,    2    ,    3    ,    1     ).float().numpy()

														if output_type == "pil":
																					lowerCAmelCase_       		=  self.numpy_to_pil(lowercase_     )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowercase_     )



