


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


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






def   __lowerCamelCase	(       self		:	int     ,       lowerCamelCase__		:	Tuple     ,       lowerCamelCase__		:	Optional[int]     ,       lowerCamelCase__		:	Tuple     ,       lowerCamelCase__		:	Any			):





			'''simple docstring'''




			lowerCamelCase								=     image.to(device=lowerCamelCase__			)
			lowerCamelCase								=     image  # take image as image_embeddings
			lowerCamelCase								=     image_embeddings.unsqueeze(1			)

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

			if do_classifier_free_guidance:
						lowerCamelCase								=     torch.zeros_like(lowerCamelCase__			)

						# 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, image_embeddings]			)

			return image_embeddings







class 					__lowercase					(       a_     ):



		"""simple docstring"""

		def __init__(   self		,			A		,			A		,			A		,			A		,			A = None		,			)     ->   Optional[Any]:







					'''simple docstring'''







					super().__init__()

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

					lowerCamelCase								=     decoder_pipe_kwargs.get("""torch_dtype"""		,			A  ) or prior.dtype

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

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

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







		def 				__A      (   self		,			A		,			A		,			A		,			A		,			A = None		,			A = None		,			)     ->   Union[str, Any]:







					'''simple docstring'''







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

								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]

								lowerCamelCase								=     self.text_encoder(text_input_ids.to(A  )  )

								lowerCamelCase								=     text_encoder_output.text_embeds
								lowerCamelCase								=     text_encoder_output.last_hidden_state

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

					lowerCamelCase								=     text_embeddings.repeat_interleave(A		,			dim=0  )
					lowerCamelCase								=     text_encoder_hidden_states.repeat_interleave(A		,			dim=0  )
					lowerCamelCase								=     text_mask.repeat_interleave(A		,			dim=0  )

					if do_classifier_free_guidance:
								lowerCamelCase								=     [""""""] * batch_size

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

								lowerCamelCase								=     uncond_embeddings_text_encoder_output.text_embeds
								lowerCamelCase								=     uncond_embeddings_text_encoder_output.last_hidden_state

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

								lowerCamelCase								=     uncond_embeddings.shape[1]
								lowerCamelCase								=     uncond_embeddings.repeat(1		,			A  )
								lowerCamelCase								=     uncond_embeddings.view(batch_size * num_images_per_prompt		,			A  )

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

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

					return text_embeddings, text_encoder_hidden_states, text_mask







		@property
		def 				__A      (   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(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







		def 				__A      (   self		,			A		,			A		,			A		,			A		,			A		,			A  )     ->   Tuple:







					'''simple docstring'''







					if latents is None:
								lowerCamelCase								=     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}'  )
								lowerCamelCase								=     latents.to(A  )

					lowerCamelCase								=     latents * scheduler.init_noise_sigma
					return latents







		def 				__A      (   self		,			A = None  )     ->   Dict:







					'''simple docstring'''







					self.decoder_pipe.to(A  )
					super().to(A  )







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







					'''simple docstring'''







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

					lowerCamelCase								=     self._execution_device

					lowerCamelCase								=     batch_size * num_images_per_prompt

					lowerCamelCase								=     prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

					lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     self._encode_prompt(
					    A		,			A		,			A		,			A		,			A		,			A  )

					# prior

					self.prior_scheduler.set_timesteps(A		,			device=A  )
					lowerCamelCase								=     self.prior_scheduler.timesteps

					lowerCamelCase								=     self.prior.config.embedding_dim

					lowerCamelCase								=     self.prepare_latents(
					    (batch_size, embedding_dim)		,			text_embeddings.dtype		,			A		,			A		,			A		,			self.prior_scheduler		,			)

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

								lowerCamelCase								=     self.prior(
								    A		,			timestep=A		,			proj_embedding=A		,			encoder_hidden_states=A		,			attention_mask=A		,			).predicted_image_embedding

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

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

								lowerCamelCase								=     self.prior_scheduler.step(
								    A		,			timestep=A		,			sample=A		,			generator=A		,			prev_timestep=A		,			).prev_sample

					lowerCamelCase								=     self.prior.post_process_latents(A  )

					lowerCamelCase								=     prior_latents

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





def   __lowerCamelCase	(       lowerCamelCase__		:	int = 100     ,       lowerCamelCase__		:	int = 100			):





			'''simple docstring'''




			return max(
			    sum(int(lowerCamelCase__			) for x in str(base**power			)			)
			    for base in range(lowerCamelCase__			)
			    for power in range(lowerCamelCase__			)			)


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()

