
from typing import List, Optional, Union

import torch

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,
)


lowercase__		:Optional[Any]   =    logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase__		:Optional[Any]   =    "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=8		):
					'''simple docstring'''
					lowercase							=						height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					lowercase							=						width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def __init__(				self   ,A__   ,A__   ,A__   ,):
												super().__init__()

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




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__):
												if latents is None:
																	lowercase							=						randn_tensor(A__   ,generator=A__   ,device=A__   ,dtype=A__)
												else:
																	if latents.shape != shape:
																						raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}')
																	lowercase							=						latents.to(A__)

												lowercase							=						latents * scheduler.init_noise_sigma
												return latents




							def  A__      (				self   ,A__=0):
												if is_accelerate_available():
																	from accelerate import cpu_offload
												else:
																	raise ImportError('''Please install accelerate via `pip install accelerate`''')

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

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




							def  A__      (				self   ,A__=0):
												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.''')

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

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

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

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




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def  A__      (				self):
												if not hasattr(self.unet   ,'''_hf_hook'''):
																	return self.device
												for module in self.unet.modules():
																	if (
																	    hasattr(A__   ,'''_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(A__)
							def __call__(				self   ,A__   ,A__   ,A__ = 5_1_2   ,A__ = 5_1_2   ,A__ = 1_0_0   ,A__ = 4.0   ,A__ = 1   ,A__ = None   ,A__ = None   ,A__ = "pil"   ,A__ = True   ,):
												lowercase							=						self._execution_device

												lowercase							=						guidance_scale > 1.0

												if isinstance(A__   ,A__):
																	lowercase							=						torch.cat(A__   ,dim=0)
												lowercase							=						image_embeds.shape[0] * num_images_per_prompt
												if isinstance(A__   ,A__):
																	lowercase							=						torch.cat(A__   ,dim=0)

												if do_classifier_free_guidance:
																	lowercase							=						image_embeds.repeat_interleave(A__   ,dim=0)
																	lowercase							=						negative_image_embeds.repeat_interleave(A__   ,dim=0)

												lowercase							=						torch.cat([negative_image_embeds, image_embeds]   ,dim=0).to(dtype=self.unet.dtype   ,device=A__)

												self.scheduler.set_timesteps(A__   ,device=A__)
												lowercase							=						self.scheduler.timesteps

												lowercase							=						self.unet.config.in_channels

												lowercase	,						lowercase							=						downscale_height_and_width(A__   ,A__   ,self.movq_scale_factor)

												# create initial latent
												lowercase							=						self.prepare_latents(
												    (batch_size, num_channels_latents, height, width)   ,image_embeds.dtype   ,A__   ,A__   ,A__   ,self.scheduler   ,)

												for i, t in enumerate(self.progress_bar(A__)):
																	# expand the latents if we are doing classifier free guidance
																	lowercase							=						torch.cat([latents] * 2) if do_classifier_free_guidance else latents

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

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

																	# compute the previous noisy sample x_t -> x_t-1
																	lowercase							=						self.scheduler.step(
																	    A__   ,A__   ,A__   ,generator=A__   ,)[0]
												# post-processing
												lowercase							=						self.movq.decode(A__   ,force_not_quantize=A__)['''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"]:
																	lowercase							=						image * 0.5 + 0.5
																	lowercase							=						image.clamp(0   ,1)
																	lowercase							=						image.cpu().permute(0   ,2   ,3   ,1).float().numpy()

												if output_type == "pil":
																	lowercase							=						self.numpy_to_pil(A__)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=A__)











'''simple docstring'''
a__							:							Optional[Any] =256
# Modulus to hash a string
a__							:							Dict =1_000_003
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  str     ) ->      bool:



       """simple docstring"""






       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       if p_len > t_len:
              return False

       __UpperCamelCase       =  0
       __UpperCamelCase       =  0
       __UpperCamelCase       =  1

       # Calculating the hash of pattern and substring of text
       for i in range(__lowercase     ):
              __UpperCamelCase       =  (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
              __UpperCamelCase       =  (ord(text[i]     ) + text_hash * alphabet_size) % modulus
              if i == p_len - 1:
                     continue
              __UpperCamelCase       =  (modulus_power * alphabet_size) % modulus

       for i in range(0				,  t_len - p_len + 1     ):
              if text_hash == p_hash and text[i : i + p_len] == pattern:
                     return True
              if i == t_len - p_len:
                     continue
              # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
              __UpperCamelCase       =  (
                  (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
                  + ord(text[i + p_len]     )
              ) % modulus
       return False
def 				lowercase__   (   ) ->      None:



       """simple docstring"""






       __UpperCamelCase       =  'abc1abc12'
       __UpperCamelCase       =  'alskfjaldsabc1abc1abc12k23adsfabcabc'
       __UpperCamelCase       =  'alskfjaldsk23adsfabcabc'
       assert rabin_karp(__lowercase				,  __lowercase     ) and not rabin_karp(__lowercase				,  __lowercase     )

       # Test 2)
       __UpperCamelCase       =  'ABABX'
       __UpperCamelCase       =  'ABABZABABYABABX'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 3)
       __UpperCamelCase       =  'AAAB'
       __UpperCamelCase       =  'ABAAAAAB'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 4)
       __UpperCamelCase       =  'abcdabcy'
       __UpperCamelCase       =  'abcxabcdabxabcdabcdabcy'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 5)
       __UpperCamelCase       =  'Lü'
       __UpperCamelCase       =  'Lüsai'
       assert rabin_karp(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  'Lue'
       assert not rabin_karp(__lowercase				,  __lowercase     )
       print('Success.'     )


if __name__ == "__main__":
    test_rabin_karp()
