





import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


_lowerCamelCase					:				str  							=					logging.get_logger(__name__)


class 						lowerCamelCase   (__lowerCAmelCase			):







							"""simple docstring"""




							def __init__(					self	:     str, *_UpperCAmelCase	:     Any, **_UpperCAmelCase	:     Tuple						)      -> str:





														"""simple docstring"""
														warnings.warn(
														    "The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
														    " Please use MaskFormerImageProcessor instead.", __SCREAMING_SNAKE_CASE, )
														super().__init__(*__SCREAMING_SNAKE_CASE, **__SCREAMING_SNAKE_CASE						)













"""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


a__    :   Optional[int]    						= logging.get_logger(__name__)  # pylint: disable=invalid-name





def 							a__	(     self		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     List[str]:

 '''simple docstring'''






 snake_case__										= image.to(device=_UpperCAmelCase		)
 snake_case__										= image  # take image as image_embeddings
 snake_case__										= image_embeddings.unsqueeze(1		)

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

 if do_classifier_free_guidance:
  snake_case__										= 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
  snake_case__										= torch.cat([uncond_embeddings, image_embeddings]		)

 return image_embeddings



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        super().__init__()

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

        snake_case__										= decoder_pipe_kwargs.get("torch_dtype"      ,    __SCREAMING_SNAKE_CASE   ) or prior.dtype

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

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

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

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

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

         snake_case__										= self.text_encoder(text_input_ids.to(__SCREAMING_SNAKE_CASE   )   )

         snake_case__										= text_encoder_output.text_embeds
         snake_case__										= text_encoder_output.last_hidden_state

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

        snake_case__										= text_embeddings.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        snake_case__										= text_encoder_hidden_states.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        snake_case__										= text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )

        if do_classifier_free_guidance:
         snake_case__										= [""] * batch_size

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

         snake_case__										= uncond_embeddings_text_encoder_output.text_embeds
         snake_case__										= uncond_embeddings_text_encoder_output.last_hidden_state

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

         snake_case__										= uncond_embeddings.shape[1]
         snake_case__										= uncond_embeddings.repeat(1      ,    __SCREAMING_SNAKE_CASE   )
         snake_case__										= uncond_embeddings.view(batch_size * num_images_per_prompt      ,    __SCREAMING_SNAKE_CASE   )

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

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

        return text_embeddings, text_encoder_hidden_states, text_mask

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''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(__SCREAMING_SNAKE_CASE      ,    "_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      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        if latents is None:
         snake_case__										= randn_tensor(__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE      ,    dtype=__SCREAMING_SNAKE_CASE   )
        else:
         if latents.shape != shape:
          raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
         snake_case__										= latents.to(__SCREAMING_SNAKE_CASE   )

        snake_case__										= latents * scheduler.init_noise_sigma
        return latents

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE = None   ):






        '''simple docstring'''

        self.decoder_pipe.to(__SCREAMING_SNAKE_CASE   )
        super().to(__SCREAMING_SNAKE_CASE   )

       @torch.no_grad()
       def __call__(	self      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = 25      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = 4.0      ,    __SCREAMING_SNAKE_CASE = 8.0      ,    __SCREAMING_SNAKE_CASE = 50      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = 0.0      ,    __SCREAMING_SNAKE_CASE = "pil"      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

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

        snake_case__										= self._execution_device

        snake_case__										= batch_size * num_images_per_prompt

        snake_case__										= prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        snake_case__							,      snake_case__							,      snake_case__										= self._encode_prompt(
            __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

        # prior

        self.prior_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.prior_scheduler.timesteps

        snake_case__										= self.prior.config.embedding_dim

        snake_case__										= self.prepare_latents(
            (batch_size, embedding_dim)      ,    text_embeddings.dtype      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    self.prior_scheduler      ,    )

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

         snake_case__										= self.prior(
             __SCREAMING_SNAKE_CASE      ,    timestep=__SCREAMING_SNAKE_CASE      ,    proj_embedding=__SCREAMING_SNAKE_CASE      ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    ).predicted_image_embedding

         if do_classifier_free_guidance:
          snake_case__							,      snake_case__										= predicted_image_embedding.chunk(2   )
          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]:
          snake_case__										= None
         else:
          snake_case__										= prior_timesteps_tensor[i + 1]

         snake_case__										= self.prior_scheduler.step(
             __SCREAMING_SNAKE_CASE      ,    timestep=__SCREAMING_SNAKE_CASE      ,    sample=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    prev_timestep=__SCREAMING_SNAKE_CASE      ,    ).prev_sample

        snake_case__										= self.prior.post_process_latents(__SCREAMING_SNAKE_CASE   )

        snake_case__										= prior_latents

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

