'''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


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






def SCREAMING_SNAKE_CASE__   (							self    :							Optional[Any]							,     snake_case    :							str							,     snake_case    :							Tuple							,     snake_case    :							Dict							,     snake_case    :							List[str]   )					->							Optional[Any]:







     """simple docstring"""
     a :				Tuple				=      image.to(device=UpperCAmelCase__   )
     a :				int				=      image  # take image as image_embeddings
     a :				Any				=      image_embeddings.unsqueeze(1   )

     # duplicate image embeddings for each generation per prompt, using mps friendly method
     a				,       a				,       a :				Optional[int]				=      image_embeddings.shape
     a :				Optional[int]				=      image_embeddings.repeat(1							,     UpperCAmelCase__							,     1   )
     a :				Union[str, Any]				=      image_embeddings.view(bs_embed * num_images_per_prompt							,     UpperCAmelCase__							,     -1   )

     if do_classifier_free_guidance:
          a :				Any				=      torch.zeros_like(UpperCAmelCase__   )

          # 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
          a :				Union[str, Any]				=      torch.cat([uncond_embeddings, image_embeddings]   )

     return image_embeddings





class    UpperCamelCase (    a_    ):







    """simple docstring"""





    def __init__( self			:		Any				,				UpperCAmelCase_			:		PriorTransformer				,				UpperCAmelCase_			:		CLIPTokenizer				,				UpperCAmelCase_			:		CLIPTextModelWithProjection				,				UpperCAmelCase_			:		UnCLIPScheduler				,				UpperCAmelCase_			:		Optional[dict] = None				,				):


         """simple docstring"""







         super().__init__()

         a :				List[Any]				=      {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

         a :				List[Any]				=      decoder_pipe_kwargs.get('torch_dtype'				,				_UpperCamelCase) or prior.dtype

         a :				Optional[int]				=      StableDiffusionImageVariationPipeline.from_pretrained(
             'lambdalabs/sd-image-variations-diffusers'				,				**_UpperCamelCase)

         # replace `_encode_image` method
         a :				Union[str, Any]				=      types.MethodType(_encode_image				,				self.decoder_pipe)

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





    def 					SCREAMING_SNAKE_CASE_			( self			:		Dict				,				UpperCAmelCase_			:		List[str]				,				UpperCAmelCase_			:		List[str]				,				UpperCAmelCase_			:		Optional[int]				,				UpperCAmelCase_			:		Tuple				,				UpperCAmelCase_			:		Optional[Union[CLIPTextModelOutput, Tuple]] = None				,				UpperCAmelCase_			:		Optional[torch.Tensor] = None				,				):


         """simple docstring"""







         if text_model_output is None:
              a :				Optional[Any]				=      len(_UpperCamelCase) if isinstance(_UpperCamelCase				,				_UpperCamelCase) else 1
              # get prompt text embeddings
              a :				Optional[Any]				=      self.tokenizer(
                  _UpperCamelCase				,				padding='max_length'				,				max_length=self.tokenizer.model_max_length				,				return_tensors='pt'				,				)
              a :				int				=      text_inputs.input_ids
              a :				Dict				=      text_inputs.attention_mask.bool().to(_UpperCamelCase)

              if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                   a :				Tuple				=      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}""")
                   a :				Union[str, Any]				=      text_input_ids[:, : self.tokenizer.model_max_length]

              a :				Optional[int]				=      self.text_encoder(text_input_ids.to(_UpperCamelCase))

              a :				Any				=      text_encoder_output.text_embeds
              a :				List[str]				=      text_encoder_output.last_hidden_state

         else:
              a :				Any				=      text_model_output[0].shape[0]
              a				,       a :				Dict				=      text_model_output[0], text_model_output[1]
              a :				Any				=      text_attention_mask

         a :				Optional[Any]				=      text_embeddings.repeat_interleave(_UpperCamelCase				,				dim=0)
         a :				Any				=      text_encoder_hidden_states.repeat_interleave(_UpperCamelCase				,				dim=0)
         a :				int				=      text_mask.repeat_interleave(_UpperCamelCase				,				dim=0)

         if do_classifier_free_guidance:
              a :				List[str]				=      [''] * batch_size

              a :				Dict				=      self.tokenizer(
                  _UpperCamelCase				,				padding='max_length'				,				max_length=self.tokenizer.model_max_length				,				truncation=_UpperCamelCase				,				return_tensors='pt'				,				)
              a :				Optional[int]				=      uncond_input.attention_mask.bool().to(_UpperCamelCase)
              a :				Union[str, Any]				=      self.text_encoder(uncond_input.input_ids.to(_UpperCamelCase))

              a :				List[str]				=      uncond_embeddings_text_encoder_output.text_embeds
              a :				List[Any]				=      uncond_embeddings_text_encoder_output.last_hidden_state

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

              a :				Any				=      uncond_embeddings.shape[1]
              a :				Optional[Any]				=      uncond_embeddings.repeat(1				,				_UpperCamelCase)
              a :				Optional[int]				=      uncond_embeddings.view(batch_size * num_images_per_prompt				,				_UpperCamelCase)

              a :				List[str]				=      uncond_text_encoder_hidden_states.shape[1]
              a :				str				=      uncond_text_encoder_hidden_states.repeat(1				,				_UpperCamelCase				,				1)
              a :				Tuple				=      uncond_text_encoder_hidden_states.view(
                  batch_size * num_images_per_prompt				,				_UpperCamelCase				,				-1)
              a :				str				=      uncond_text_mask.repeat_interleave(_UpperCamelCase				,				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
              a :				List[Any]				=      torch.cat([uncond_embeddings, text_embeddings])
              a :				int				=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])

              a :				int				=      torch.cat([uncond_text_mask, text_mask])

         return text_embeddings, text_encoder_hidden_states, text_mask





    @property
    def 					SCREAMING_SNAKE_CASE_			( 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(_UpperCamelCase				,				'_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			:		List[Any]				,				UpperCAmelCase_			:		Any				,				UpperCAmelCase_			:		Dict				,				UpperCAmelCase_			:		Tuple				,				UpperCAmelCase_			:		Any				,				UpperCAmelCase_			:		Optional[int]				,				UpperCAmelCase_			:		Dict):


         """simple docstring"""







         if latents is None:
              a :				List[str]				=      randn_tensor(_UpperCamelCase				,				generator=_UpperCamelCase				,				device=_UpperCamelCase				,				dtype=_UpperCamelCase)
         else:
              if latents.shape != shape:
                   raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""")
              a :				Dict				=      latents.to(_UpperCamelCase)

         a :				Any				=      latents * scheduler.init_noise_sigma
         return latents





    def 					SCREAMING_SNAKE_CASE_			( self			:		Optional[int]				,				UpperCAmelCase_			:		Optional[Union[str, torch.device]] = None):


         """simple docstring"""







         self.decoder_pipe.to(_UpperCamelCase)
         super().to(_UpperCamelCase)







    @torch.no_grad()
    def __call__( self			:		Optional[Any]				,				UpperCAmelCase_			:		Optional[Union[str, List[str]]] = None				,				UpperCAmelCase_			:		Optional[int] = None				,				UpperCAmelCase_			:		Optional[int] = None				,				UpperCAmelCase_			:		int = 1				,				UpperCAmelCase_			:		int = 2_5				,				UpperCAmelCase_			:		Optional[torch.Generator] = None				,				UpperCAmelCase_			:		Optional[torch.FloatTensor] = None				,				UpperCAmelCase_			:		Optional[Union[CLIPTextModelOutput, Tuple]] = None				,				UpperCAmelCase_			:		Optional[torch.Tensor] = None				,				UpperCAmelCase_			:		float = 4.0				,				UpperCAmelCase_			:		float = 8.0				,				UpperCAmelCase_			:		int = 5_0				,				UpperCAmelCase_			:		Optional[int] = 1				,				UpperCAmelCase_			:		float = 0.0				,				UpperCAmelCase_			:		Optional[str] = "pil"				,				UpperCAmelCase_			:		bool = True				,				):


         """simple docstring"""







         if prompt is not None:
              if isinstance(_UpperCamelCase				,				_UpperCamelCase):
                   a :				Tuple				=      1
              elif isinstance(_UpperCamelCase				,				_UpperCamelCase):
                   a :				int				=      len(_UpperCamelCase)
              else:
                   raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase)}""")
         else:
              a :				Optional[Any]				=      text_model_output[0].shape[0]

         a :				Any				=      self._execution_device

         a :				Any				=      batch_size * num_images_per_prompt

         a :				Optional[Any]				=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

         a				,       a				,       a :				Dict				=      self._encode_prompt(
             _UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase)

         # prior

         self.prior_scheduler.set_timesteps(_UpperCamelCase				,				device=_UpperCamelCase)
         a :				Dict				=      self.prior_scheduler.timesteps

         a :				int				=      self.prior.config.embedding_dim

         a :				Tuple				=      self.prepare_latents(
             (batch_size, embedding_dim)				,				text_embeddings.dtype				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				self.prior_scheduler				,				)

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

              a :				Optional[int]				=      self.prior(
                  _UpperCamelCase				,				timestep=_UpperCamelCase				,				proj_embedding=_UpperCamelCase				,				encoder_hidden_states=_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				).predicted_image_embedding

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

              if i + 1 == prior_timesteps_tensor.shape[0]:
                   a :				Optional[int]				=      None
              else:
                   a :				List[Any]				=      prior_timesteps_tensor[i + 1]

              a :				Any				=      self.prior_scheduler.step(
                  _UpperCamelCase				,				timestep=_UpperCamelCase				,				sample=_UpperCamelCase				,				generator=_UpperCamelCase				,				prev_timestep=_UpperCamelCase				,				).prev_sample

         a :				Optional[int]				=      self.prior.post_process_latents(_UpperCamelCase)

         a :				Any				=      prior_latents

         a :				Any				=      self.decoder_pipe(
             image=_UpperCamelCase				,				height=_UpperCamelCase				,				width=_UpperCamelCase				,				num_inference_steps=_UpperCamelCase				,				guidance_scale=_UpperCamelCase				,				generator=_UpperCamelCase				,				output_type=_UpperCamelCase				,				return_dict=_UpperCamelCase				,				num_images_per_prompt=_UpperCamelCase				,				eta=_UpperCamelCase				,				)
         return output


from __future__ import annotations

from enum import Enum


class   lowercase			(   a   ):
							lowercase__  :				List[str]									=							24
							lowercase__  :				int									=							21
							lowercase__  :				Union[str, Any]									=							18
							lowercase__  :				Dict									=							15
							lowercase__  :				Any									=							12
							lowercase__  :				List[Any]									=							9
							lowercase__  :				Optional[int]									=							6
							lowercase__  :				List[Any]									=							3
							lowercase__  :				int									=							2
							lowercase__  :				Optional[int]									=							1
							lowercase__  :				Dict									=							-1
							lowercase__  :				Any									=							-2
							lowercase__  :				Tuple									=							-3
							lowercase__  :				Optional[Any]									=							-6
							lowercase__  :				Tuple									=							-9
							lowercase__  :				List[Any]									=							-12
							lowercase__  :				Any									=							-15
							lowercase__  :				List[Any]									=							-18
							lowercase__  :				int									=							-21
							lowercase__  :				Optional[Any]									=							-24



class   lowercase			(   a   ):
							lowercase__  :				Union[str, Any]									=							8
							lowercase__  :				List[Any]									=							7
							lowercase__  :				List[str]									=							6
							lowercase__  :				Dict									=							5
							lowercase__  :				List[Any]									=							4
							lowercase__  :				Union[str, Any]									=							3
							lowercase__  :				Union[str, Any]									=							2
							lowercase__  :				Optional[Any]									=							1





def 		__lowerCamelCase							(UpperCAmelCase__   :							float					,       UpperCAmelCase__   :							str | SIUnit					,       UpperCAmelCase__   :							str | SIUnit					,       ):
					if isinstance(UpperCAmelCase__					,       UpperCAmelCase__     ):
										SCREAMING_SNAKE_CASE				=				SIUnit[known_prefix.lower()]
					if isinstance(UpperCAmelCase__					,       UpperCAmelCase__     ):
										SCREAMING_SNAKE_CASE				=				SIUnit[unknown_prefix.lower()]
					SCREAMING_SNAKE_CASE				=				known_amount * (
					    1_0 ** (known_prefix.value - unknown_prefix.value)
					)
					return unknown_amount





def 		__lowerCamelCase							(UpperCAmelCase__   :							float					,       UpperCAmelCase__   :							str | BinaryUnit					,       UpperCAmelCase__   :							str | BinaryUnit					,       ):
					if isinstance(UpperCAmelCase__					,       UpperCAmelCase__     ):
										SCREAMING_SNAKE_CASE				=				BinaryUnit[known_prefix.lower()]
					if isinstance(UpperCAmelCase__					,       UpperCAmelCase__     ):
										SCREAMING_SNAKE_CASE				=				BinaryUnit[unknown_prefix.lower()]
					SCREAMING_SNAKE_CASE				=				known_amount * (
					    2 ** ((known_prefix.value - unknown_prefix.value) * 1_0)
					)
					return unknown_amount


if __name__ == "__main__":
							import doctest

							doctest.testmod()




