
"""simple docstring"""




import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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

def   UpperCamelCase_  (		self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	image.to(device=SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	image  # take image as image_embeddings
					SCREAMING_SNAKE_CASE				=	image_embeddings.unsqueeze(1			)

					# duplicate image embeddings for each generation per prompt, using mps friendly method
					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	image_embeddings.shape
					SCREAMING_SNAKE_CASE				=	image_embeddings.repeat(1,     SCREAMING_SNAKE_CASE_,     1			)
					SCREAMING_SNAKE_CASE				=	image_embeddings.view(bs_embed * num_images_per_prompt,     SCREAMING_SNAKE_CASE_,     -1			)

					if do_classifier_free_guidance:
										SCREAMING_SNAKE_CASE				=	torch.zeros_like(SCREAMING_SNAKE_CASE_			)

										# 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
										SCREAMING_SNAKE_CASE				=	torch.cat([uncond_embeddings, image_embeddings]			)

					return image_embeddings




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__ = None		,	)    ->    List[Any]:
									"""simple docstring"""







									super().__init__()

									SCREAMING_SNAKE_CASE				=	{'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

									SCREAMING_SNAKE_CASE				=	decoder_pipe_kwargs.get('torch_dtype'		,	lowercase__			) or prior.dtype

									SCREAMING_SNAKE_CASE				=	StableDiffusionImageVariationPipeline.from_pretrained(
									    'lambdalabs/sd-image-variations-diffusers'		,	**lowercase__			)

									# replace `_encode_image` method
									SCREAMING_SNAKE_CASE				=	types.MethodType(_encode_image		,	self.decoder_pipe			)

									self.register_modules(
									    prior=lowercase__		,	tokenizer=lowercase__		,	text_encoder=lowercase__		,	prior_scheduler=lowercase__		,	)


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__ = None		,	lowercase__ = None		,	)    ->    Tuple:
									"""simple docstring"""







									if text_model_output is None:
														SCREAMING_SNAKE_CASE				=	len(lowercase__			) if isinstance(lowercase__		,	lowercase__			) else 1
														# get prompt text embeddings
														SCREAMING_SNAKE_CASE				=	self.tokenizer(
														    lowercase__		,	padding='max_length'		,	max_length=self.tokenizer.model_max_length		,	return_tensors='pt'		,	)
														SCREAMING_SNAKE_CASE				=	text_inputs.input_ids
														SCREAMING_SNAKE_CASE				=	text_inputs.attention_mask.bool().to(lowercase__			)

														if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																			SCREAMING_SNAKE_CASE				=	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}'''			)
																			SCREAMING_SNAKE_CASE				=	text_input_ids[:, : self.tokenizer.model_max_length]

														SCREAMING_SNAKE_CASE				=	self.text_encoder(text_input_ids.to(lowercase__			)			)

														SCREAMING_SNAKE_CASE				=	text_encoder_output.text_embeds
														SCREAMING_SNAKE_CASE				=	text_encoder_output.last_hidden_state

									else:
														SCREAMING_SNAKE_CASE				=	text_model_output[0].shape[0]
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	text_model_output[0], text_model_output[1]
														SCREAMING_SNAKE_CASE				=	text_attention_mask

									SCREAMING_SNAKE_CASE				=	text_embeddings.repeat_interleave(lowercase__		,	dim=0			)
									SCREAMING_SNAKE_CASE				=	text_encoder_hidden_states.repeat_interleave(lowercase__		,	dim=0			)
									SCREAMING_SNAKE_CASE				=	text_mask.repeat_interleave(lowercase__		,	dim=0			)

									if do_classifier_free_guidance:
														SCREAMING_SNAKE_CASE				=	[''] * batch_size

														SCREAMING_SNAKE_CASE				=	self.tokenizer(
														    lowercase__		,	padding='max_length'		,	max_length=self.tokenizer.model_max_length		,	truncation=lowercase__		,	return_tensors='pt'		,	)
														SCREAMING_SNAKE_CASE				=	uncond_input.attention_mask.bool().to(lowercase__			)
														SCREAMING_SNAKE_CASE				=	self.text_encoder(uncond_input.input_ids.to(lowercase__			)			)

														SCREAMING_SNAKE_CASE				=	uncond_embeddings_text_encoder_output.text_embeds
														SCREAMING_SNAKE_CASE				=	uncond_embeddings_text_encoder_output.last_hidden_state

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

														SCREAMING_SNAKE_CASE				=	uncond_embeddings.shape[1]
														SCREAMING_SNAKE_CASE				=	uncond_embeddings.repeat(1		,	lowercase__			)
														SCREAMING_SNAKE_CASE				=	uncond_embeddings.view(batch_size * num_images_per_prompt		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	uncond_text_encoder_hidden_states.shape[1]
														SCREAMING_SNAKE_CASE				=	uncond_text_encoder_hidden_states.repeat(1		,	lowercase__		,	1			)
														SCREAMING_SNAKE_CASE				=	uncond_text_encoder_hidden_states.view(
														    batch_size * num_images_per_prompt		,	lowercase__		,	-1			)
														SCREAMING_SNAKE_CASE				=	uncond_text_mask.repeat_interleave(lowercase__		,	dim=0			)

														# done duplicates

														# 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
														SCREAMING_SNAKE_CASE				=	torch.cat([uncond_embeddings, text_embeddings]			)
														SCREAMING_SNAKE_CASE				=	torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]			)

														SCREAMING_SNAKE_CASE				=	torch.cat([uncond_text_mask, text_mask]			)

									return text_embeddings, text_encoder_hidden_states, text_mask


				@property
				def 							A					(				self			)    ->    int:
									"""simple docstring"""







									if self.device != torch.device('meta'			) or not hasattr(self.prior		,	'_hf_hook'			):
														return self.device
									for module in self.prior.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


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__			)    ->    str:
									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE				=	latents * scheduler.init_noise_sigma
									return latents


				def 							A					(				self		,	lowercase__ = None			)    ->    Optional[Any]:
									"""simple docstring"""







									self.decoder_pipe.to(lowercase__			)
									super().to(lowercase__			)




				@torch.no_grad()
				def __call__(				self		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = 1		,	lowercase__ = 25		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = 4.0		,	lowercase__ = 8.0		,	lowercase__ = 50		,	lowercase__ = 1		,	lowercase__ = 0.0		,	lowercase__ = "pil"		,	lowercase__ = True		,	)    ->    List[str]:
									"""simple docstring"""







									if prompt is not None:
														if isinstance(lowercase__		,	lowercase__			):
																			SCREAMING_SNAKE_CASE				=	1
														elif isinstance(lowercase__		,	lowercase__			):
																			SCREAMING_SNAKE_CASE				=	len(lowercase__			)
														else:
																			raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowercase__			)}'''			)
									else:
														SCREAMING_SNAKE_CASE				=	text_model_output[0].shape[0]

									SCREAMING_SNAKE_CASE				=	self._execution_device

									SCREAMING_SNAKE_CASE				=	batch_size * num_images_per_prompt

									SCREAMING_SNAKE_CASE				=	prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self._encode_prompt(
									    lowercase__		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__			)

									# prior

									self.prior_scheduler.set_timesteps(lowercase__		,	device=lowercase__			)
									SCREAMING_SNAKE_CASE				=	self.prior_scheduler.timesteps

									SCREAMING_SNAKE_CASE				=	self.prior.config.embedding_dim

									SCREAMING_SNAKE_CASE				=	self.prepare_latents(
									    (batch_size, embedding_dim)		,	text_embeddings.dtype		,	lowercase__		,	lowercase__		,	lowercase__		,	self.prior_scheduler		,	)

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

														SCREAMING_SNAKE_CASE				=	self.prior(
														    lowercase__		,	timestep=lowercase__		,	proj_embedding=lowercase__		,	encoder_hidden_states=lowercase__		,	attention_mask=lowercase__		,	).predicted_image_embedding

														if do_classifier_free_guidance:
																			SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	predicted_image_embedding.chunk(2			)
																			SCREAMING_SNAKE_CASE				=	predicted_image_embedding_uncond + prior_guidance_scale * (
																			    predicted_image_embedding_text - predicted_image_embedding_uncond
																			)

														if i + 1 == prior_timesteps_tensor.shape[0]:
																			SCREAMING_SNAKE_CASE				=	None
														else:
																			SCREAMING_SNAKE_CASE				=	prior_timesteps_tensor[i + 1]

														SCREAMING_SNAKE_CASE				=	self.prior_scheduler.step(
														    lowercase__		,	timestep=lowercase__		,	sample=lowercase__		,	generator=lowercase__		,	prev_timestep=lowercase__		,	).prev_sample

									SCREAMING_SNAKE_CASE				=	self.prior.post_process_latents(lowercase__			)

									SCREAMING_SNAKE_CASE				=	prior_latents

									SCREAMING_SNAKE_CASE				=	self.decoder_pipe(
									    image=lowercase__		,	height=lowercase__		,	width=lowercase__		,	num_inference_steps=lowercase__		,	guidance_scale=lowercase__		,	generator=lowercase__		,	output_type=lowercase__		,	return_dict=lowercase__		,	num_images_per_prompt=lowercase__		,	eta=lowercase__		,	)
									return output


"""simple docstring"""




from functools import lru_cache

@lru_cache
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('k must be an integer.'			)
					if k < 0:
										raise ValueError('k integer must be greater or equal to zero.'			)
					if k == 0:
										return 0
					elif k == 1:
										return 1
					else:
										return fibonacci(k - 1			) + fibonacci(k - 2			)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	len(SCREAMING_SNAKE_CASE_			)
					# Find m such that F_m >= n where F_i is the i_th fibonacci number.
					SCREAMING_SNAKE_CASE				=	0
					while True:
										if fibonacci(SCREAMING_SNAKE_CASE_			) >= len_list:
															SCREAMING_SNAKE_CASE				=	i
															break
										i += 1
					SCREAMING_SNAKE_CASE				=	0
					while fibb_k > 0:
										SCREAMING_SNAKE_CASE				=	min(
										    offset + fibonacci(fibb_k - 1			),     len_list - 1			)  # Prevent out of range
										SCREAMING_SNAKE_CASE				=	arr[index_k]
										if item_k_1 == val:
															return index_k
										elif val < item_k_1:
															fibb_k -= 1
										elif val > item_k_1:
															offset += fibonacci(fibb_k - 1			)
															fibb_k -= 2
					else:
										return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()
