






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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


class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					Tuple:







							'''simple docstring'''





							super().__init__()
							self.register_modules(
							    vae=__UpperCAmelCase		,    text_encoder=__UpperCAmelCase		,    tokenizer=__UpperCAmelCase		,    unet=__UpperCAmelCase		,    scheduler=__UpperCAmelCase		,    safety_checker=__UpperCAmelCase		,    feature_extractor=__UpperCAmelCase		,    )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase = "auto"       )->					Dict:







							'''simple docstring'''





							if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													lowerCAmelCase__       =						self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							self.enable_attention_slicing(__UpperCAmelCase       )


	@torch.no_grad()
	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = 512		,    __UpperCAmelCase = 512		,    __UpperCAmelCase = 50		,    __UpperCAmelCase = 7.5		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 1		,    __UpperCAmelCase = 0.0		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = "pil"		,    __UpperCAmelCase = True		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 1		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					List[str]:







							'''simple docstring'''





							if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						1
							elif isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						len(__UpperCAmelCase       )
							else:
													raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__UpperCAmelCase       )}"       )

							if height % 8 != 0 or width % 8 != 0:
													raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}."       )

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ) or callback_steps <= 0)
							):
													raise ValueError(
													    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
													    F" {type(__UpperCAmelCase       )}."       )

							# get prompt text embeddings
							lowerCAmelCase__       =						self.tokenizer(
							    __UpperCAmelCase		,    padding="max_length"		,    max_length=self.tokenizer.model_max_length		,    return_tensors="pt"		,    )
							lowerCAmelCase__       =						text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													lowerCAmelCase__       =						self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
													logger.warning(
													    "The following part of your input was truncated because CLIP can only handle sequences up to"
													    F" {self.tokenizer.model_max_length} tokens: {removed_text}"       )
													lowerCAmelCase__       =						text_input_ids[:, : self.tokenizer.model_max_length]

							if text_embeddings is None:
													lowerCAmelCase__       =						self.text_encoder(text_input_ids.to(self.device       )       )[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						text_embeddings.shape
							lowerCAmelCase__       =						text_embeddings.repeat(1		,    __UpperCAmelCase		,    1       )
							lowerCAmelCase__       =						text_embeddings.view(bs_embed * num_images_per_prompt		,    __UpperCAmelCase		,    -1       )

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							lowerCAmelCase__       =						guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
													lowerCAmelCase__       =						42
													if negative_prompt is None:
																			lowerCAmelCase__       =						[""]
													elif type(__UpperCAmelCase       ) is not type(__UpperCAmelCase       ):
																			raise TypeError(
																			    F"`negative_prompt` should be the same type to `prompt`, but got {type(__UpperCAmelCase       )} !="
																			    F" {type(__UpperCAmelCase       )}."       )
													elif isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
																			lowerCAmelCase__       =						[negative_prompt]
													elif batch_size != len(__UpperCAmelCase       ):
																			raise ValueError(
																			    F"`negative_prompt`: {negative_prompt} has batch size {len(__UpperCAmelCase       )}, but `prompt`:"
																			    F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
																			    " the batch size of `prompt`."       )
													else:
																			lowerCAmelCase__       =						negative_prompt

													lowerCAmelCase__       =						text_input_ids.shape[-1]
													lowerCAmelCase__       =						self.tokenizer(
													    __UpperCAmelCase		,    padding="max_length"		,    max_length=__UpperCAmelCase		,    truncation=__UpperCAmelCase		,    return_tensors="pt"		,    )
													lowerCAmelCase__       =						self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													lowerCAmelCase__       =						uncond_embeddings.shape[1]
													lowerCAmelCase__       =						uncond_embeddings.repeat(__UpperCAmelCase		,    __UpperCAmelCase		,    1       )
													lowerCAmelCase__       =						uncond_embeddings.view(batch_size * num_images_per_prompt		,    __UpperCAmelCase		,    -1       )

													# For classifier free guidance, we need to do two forward passes.
													# Here we concatenate the unconditional and text embeddings into a single batch
													# to avoid doing two forward passes
													lowerCAmelCase__       =						torch.cat([uncond_embeddings, text_embeddings]       )

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							lowerCAmelCase__       =						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							lowerCAmelCase__       =						(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
							lowerCAmelCase__       =						text_embeddings.dtype
							if latents is None:
													if self.device.type == "mps":
																			# randn does not exist on mps
																			lowerCAmelCase__       =						torch.randn(
																			    __UpperCAmelCase		,    generator=__UpperCAmelCase		,    device="cpu"		,    dtype=__UpperCAmelCase       ).to(self.device       )
																			lowerCAmelCase__       =						torch.randn(__UpperCAmelCase		,    generator=__UpperCAmelCase		,    device="cpu"		,    dtype=__UpperCAmelCase       ).to(
																			    self.device       )
													else:
																			lowerCAmelCase__       =						torch.randn(
																			    __UpperCAmelCase		,    generator=__UpperCAmelCase		,    device=self.device		,    dtype=__UpperCAmelCase       )
																			lowerCAmelCase__       =						torch.randn(__UpperCAmelCase		,    generator=__UpperCAmelCase		,    device=self.device		,    dtype=__UpperCAmelCase       )
							else:
													if latents_reference.shape != latents_shape:
																			raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"       )
													lowerCAmelCase__       =						latents_reference.to(self.device       )
													lowerCAmelCase__       =						latents.to(self.device       )

							# This is the key part of the pipeline where we
							# try to ensure that the generated images w/ the same seed
							# but different sizes actually result in similar images
							lowerCAmelCase__       =						(latents_shape[3] - latents_shape_reference[3]) // 2
							lowerCAmelCase__       =						(latents_shape[2] - latents_shape_reference[2]) // 2
							lowerCAmelCase__       =						latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
							lowerCAmelCase__       =						latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
							lowerCAmelCase__       =						0 if dx < 0 else dx
							lowerCAmelCase__       =						0 if dy < 0 else dy
							lowerCAmelCase__       =						max(-dx		,    0       )
							lowerCAmelCase__       =						max(-dy		,    0       )
							# import pdb
							# pdb.set_trace()
							lowerCAmelCase__       =						latents_reference[:, :, dy : dy + h, dx : dx + w]

							# set timesteps
							self.scheduler.set_timesteps(__UpperCAmelCase       )

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							lowerCAmelCase__       =						self.scheduler.timesteps.to(self.device       )

							# scale the initial noise by the standard deviation required by the scheduler
							lowerCAmelCase__       =						latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							lowerCAmelCase__       =						"eta" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
							lowerCAmelCase__       =						{}
							if accepts_eta:
													lowerCAmelCase__       =						eta

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

													# predict the noise residual
													lowerCAmelCase__       =						self.unet(__UpperCAmelCase		,    __UpperCAmelCase		,    encoder_hidden_states=__UpperCAmelCase       ).sample

													# perform guidance
													if do_classifier_free_guidance:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						noise_pred.chunk(2       )
																			lowerCAmelCase__       =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# compute the previous noisy sample x_t -> x_t-1
													lowerCAmelCase__       =						self.scheduler.step(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    **__UpperCAmelCase       ).prev_sample

													# call the callback, if provided
													if callback is not None and i % callback_steps == 0:
																			callback(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						1 / 0.18_215 * latents
							lowerCAmelCase__       =						self.vae.decode(__UpperCAmelCase       ).sample

							lowerCAmelCase__       =						(image / 2 + 0.5).clamp(0		,    1       )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							lowerCAmelCase__       =						image.cpu().permute(0		,    2		,    3		,    1       ).float().numpy()

							if self.safety_checker is not None:
													lowerCAmelCase__       =						self.feature_extractor(self.numpy_to_pil(__UpperCAmelCase       )		,    return_tensors="pt"       ).to(
													    self.device       )
													lowerCAmelCase__      ,							lowerCAmelCase__       =						self.safety_checker(
													    images=__UpperCAmelCase		,    clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype       )       )
							else:
													lowerCAmelCase__       =						None

							if output_type == "pil":
													lowerCAmelCase__       =						self.numpy_to_pil(__UpperCAmelCase       )

							if not return_dict:
													return (image, has_nsfw_concept)

							return StableDiffusionPipelineOutput(images=__UpperCAmelCase		,    nsfw_content_detected=__UpperCAmelCase       )














def        _a  (     UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				List[str]   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						0
						lowerCAmelCase__       =						len(UpperCamelCase_   ) - 1

						while left <= right:
												# avoid divided by 0 during interpolation
												if sorted_collection[left] == sorted_collection[right]:
																		if sorted_collection[left] == item:
																								return left
																		else:
																								return None

												lowerCAmelCase__       =						left + ((item - sorted_collection[left]) * (right - left)) // (
												    sorted_collection[right] - sorted_collection[left]
												)

												# out of range check
												if point < 0 or point >= len(UpperCamelCase_   ):
																		return None

												lowerCAmelCase__       =						sorted_collection[point]
												if current_item == item:
																		return point
												else:
																		if point < left:
																								lowerCAmelCase__       =						left
																								lowerCAmelCase__       =						point
																		elif point > right:
																								lowerCAmelCase__       =						right
																								lowerCAmelCase__       =						point
																		else:
																								if item < current_item:
																														lowerCAmelCase__       =						point - 1
																								else:
																														lowerCAmelCase__       =						point + 1
						return None


def        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				int   ) ->   List[Any]:
						"""simple docstring"""







						if sorted_collection[left] == sorted_collection[right]:
												if sorted_collection[left] == item:
																		return left
												else:
																		return None

						lowerCAmelCase__       =						left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(UpperCamelCase_   ):
												return None

						if sorted_collection[point] == item:
												return point
						elif point < left:
												return interpolation_search_by_recursion(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						elif point > right:
												return interpolation_search_by_recursion(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						else:
												if sorted_collection[point] > item:
																		return interpolation_search_by_recursion(
																		    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   point - 1   )
												else:
																		return interpolation_search_by_recursion(
																		    UpperCamelCase_    ,   UpperCamelCase_    ,   point + 1    ,   UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Optional[Any]   ) ->   Tuple:
						"""simple docstring"""







						if collection != sorted(UpperCamelCase_   ):
												raise ValueError("Collection must be ascending sorted"   )
						return True


if __name__ == "__main__":
	import sys
	a_							=    0
	if debug == 1:
		a_							=    [10, 30, 40, 45, 50, 66, 77, 93]
		try:
			__assert_sorted(collection)
		except ValueError:
			sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
		a_							=    67

	a_							=    interpolation_search(collection, target)
	if result is not None:
		print(F"{target} found at positions: {result}")
	else:
		print('''Not found''')






