
"""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


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



def     lowerCAmelCase__ (		self ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)	->Any:
			__lowercase						      =					image.to(device=__magic_name__			)
			__lowercase						      =					image  # take image as image_embeddings
			__lowercase						      =					image_embeddings.unsqueeze(1			)

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

			if do_classifier_free_guidance:
						__lowercase						      =					torch.zeros_like(__magic_name__			)

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

			return image_embeddings







class 				__a    (   __lowerCamelCase					):



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = None     ,						) ->       List[str]:




										'''simple docstring'''
										super().__init__()

										__lowercase						      =					{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										__lowercase						      =					decoder_pipe_kwargs.get("torch_dtype"     ,						_lowerCamelCase					) or prior.dtype

										__lowercase						      =					StableDiffusionImageVariationPipeline.from_pretrained(
										    "lambdalabs/sd-image-variations-diffusers"     ,						**_lowerCamelCase					)

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

										self.register_modules(
										    prior=_lowerCamelCase     ,						tokenizer=_lowerCamelCase     ,						text_encoder=_lowerCamelCase     ,						prior_scheduler=_lowerCamelCase     ,						)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = None     ,						_lowerCamelCase = None     ,						) ->       Dict:




										'''simple docstring'''






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

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

													__lowercase						      =					self.text_encoder(text_input_ids.to(_lowerCamelCase					)					)

													__lowercase						      =					text_encoder_output.text_embeds
													__lowercase						      =					text_encoder_output.last_hidden_state

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

										__lowercase						      =					text_embeddings.repeat_interleave(_lowerCamelCase     ,						dim=0					)
										__lowercase						      =					text_encoder_hidden_states.repeat_interleave(_lowerCamelCase     ,						dim=0					)
										__lowercase						      =					text_mask.repeat_interleave(_lowerCamelCase     ,						dim=0					)

										if do_classifier_free_guidance:
													__lowercase						      =					[""] * batch_size

													__lowercase						      =					self.tokenizer(
													    _lowerCamelCase     ,						padding="max_length"     ,						max_length=self.tokenizer.model_max_length     ,						truncation=_lowerCamelCase     ,						return_tensors="pt"     ,						)
													__lowercase						      =					uncond_input.attention_mask.bool().to(_lowerCamelCase					)
													__lowercase						      =					self.text_encoder(uncond_input.input_ids.to(_lowerCamelCase					)					)

													__lowercase						      =					uncond_embeddings_text_encoder_output.text_embeds
													__lowercase						      =					uncond_embeddings_text_encoder_output.last_hidden_state

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

													__lowercase						      =					uncond_embeddings.shape[1]
													__lowercase						      =					uncond_embeddings.repeat(1     ,						_lowerCamelCase					)
													__lowercase						      =					uncond_embeddings.view(batch_size * num_images_per_prompt     ,						_lowerCamelCase					)

													__lowercase						      =					uncond_text_encoder_hidden_states.shape[1]
													__lowercase						      =					uncond_text_encoder_hidden_states.repeat(1     ,						_lowerCamelCase     ,						1					)
													__lowercase						      =					uncond_text_encoder_hidden_states.view(
													    batch_size * num_images_per_prompt     ,						_lowerCamelCase     ,						-1					)
													__lowercase						      =					uncond_text_mask.repeat_interleave(_lowerCamelCase     ,						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
													__lowercase						      =					torch.cat([uncond_embeddings, text_embeddings]					)
													__lowercase						      =					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

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

										return text_embeddings, text_encoder_hidden_states, text_mask
							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''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(_lowerCamelCase     ,						"_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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Optional[Any]:




										'''simple docstring'''






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

										__lowercase						      =					latents * scheduler.init_noise_sigma
										return latents
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase = None					) ->       List[str]:




										'''simple docstring'''
										self.decoder_pipe.to(_lowerCamelCase					)
										super().to(_lowerCamelCase					)





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




										'''simple docstring'''






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

										__lowercase						      =					self._execution_device

										__lowercase						      =					batch_size * num_images_per_prompt

										__lowercase						      =					prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										__lowercase      ,				__lowercase      ,				__lowercase						      =					self._encode_prompt(
										    _lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)

										# prior

										self.prior_scheduler.set_timesteps(_lowerCamelCase     ,						device=_lowerCamelCase					)
										__lowercase						      =					self.prior_scheduler.timesteps

										__lowercase						      =					self.prior.config.embedding_dim

										__lowercase						      =					self.prepare_latents(
										    (batch_size, embedding_dim)     ,						text_embeddings.dtype     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						self.prior_scheduler     ,						)

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

													__lowercase						      =					self.prior(
													    _lowerCamelCase     ,						timestep=_lowerCamelCase     ,						proj_embedding=_lowerCamelCase     ,						encoder_hidden_states=_lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						).predicted_image_embedding

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

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

													__lowercase						      =					self.prior_scheduler.step(
													    _lowerCamelCase     ,						timestep=_lowerCamelCase     ,						sample=_lowerCamelCase     ,						generator=_lowerCamelCase     ,						prev_timestep=_lowerCamelCase     ,						).prev_sample

										__lowercase						      =					self.prior.post_process_latents(_lowerCamelCase					)

										__lowercase						      =					prior_latents

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







"""simple docstring"""






def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->list:
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					(
			    first_input_list_length
			    if first_input_list_length > second_input_list_length
			    else second_input_list_length
			)
			__lowercase						      =					[]
			for char_count in range(__magic_name__			):
						if char_count < first_input_list_length:
									output_result_list.append(first_input_list[char_count]			)
						if char_count < second_input_list_length:
									output_result_list.append(second_input_list[char_count]			)

			return output_result_list


if __name__ == "__main__":
					print(alternative_list_arrange(['''A''', '''B''', '''C'''], [1, 2, 3, 4, 5]), end=''' ''')





