




'''simple docstring'''







from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

lowercase      :   Optional[Any]      =       """\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"""



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase=8				):
     snake_case			:      Dict        =			height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     snake_case			:      Dict        =			width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor







class 					_a (__A	):





     '''simple docstring'''



     def __init__(				self							,__a							,__a							,__a							,)      ->				Optional[Any]:
          super().__init__()

          self.register_modules(
              unet=__A							,scheduler=__A							,movq=__A							,)
          snake_case			:      Union[str, Any]        =			2 ** (len(self.movq.config.block_out_channels							) - 1)







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a							,__a							,__a							)      ->				Dict:
          if latents is None:
               snake_case			:      Union[str, Any]        =			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}'''							)
               snake_case			:      Any        =			latents.to(__A							)

          snake_case			:      str        =			latents * scheduler.init_noise_sigma
          return latents







     def 			snake_case_      (				self							,__a=0							)      ->				str:
          if is_accelerate_available():
               from accelerate import cpu_offload
          else:
               raise ImportError("""Please install accelerate via `pip install accelerate`"""							)

          snake_case			:      Optional[int]        =			torch.device(F'''cuda:{gpu_id}'''							)

          snake_case			:      Tuple        =			[
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                    cpu_offload(__A							,__A							)







     def 			snake_case_      (				self							,__a=0							)      ->				Any:
          if is_accelerate_available() and is_accelerate_version(""">="""							,"""0.17.0.dev0"""							):
               from accelerate import cpu_offload_with_hook
          else:
               raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""							)

          snake_case			:      Optional[Any]        =			torch.device(F'''cuda:{gpu_id}'''							)

          if self.device.type != "cpu":
               self.to("""cpu"""							,silence_dtype_warnings=__A							)
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

          snake_case			:      str        =			None
          for cpu_offloaded_model in [self.unet, self.movq]:
               snake_case ,		snake_case			:      str        =			cpu_offload_with_hook(__A							,__A							,prev_module_hook=__A							)

          # We'll offload the last model manually.
          snake_case			:      str        =			hook







     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 			snake_case_      (				self							)      ->				Any:
          if not hasattr(self.unet							,"""_hf_hook"""							):
               return self.device
          for module in self.unet.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







     @torch.no_grad()
     @replace_example_docstring(__A							)
     def __call__(				self							,__a							,__a							,__a = 512							,__a = 512							,__a = 100							,__a = 4.0							,__a = 1							,__a = None							,__a = None							,__a = "pil"							,__a = True							,)      ->				Tuple:
          snake_case			:      Any        =			self._execution_device

          snake_case			:      Tuple        =			guidance_scale > 1.0

          if isinstance(__A							,__A							):
               snake_case			:      List[str]        =			torch.cat(__A							,dim=0							)
          snake_case			:      str        =			image_embeds.shape[0] * num_images_per_prompt
          if isinstance(__A							,__A							):
               snake_case			:      Tuple        =			torch.cat(__A							,dim=0							)

          if do_classifier_free_guidance:
               snake_case			:      int        =			image_embeds.repeat_interleave(__A							,dim=0							)
               snake_case			:      Optional[Any]        =			negative_image_embeds.repeat_interleave(__A							,dim=0							)

          snake_case			:      List[Any]        =			torch.cat([negative_image_embeds, image_embeds]							,dim=0							).to(dtype=self.unet.dtype							,device=__A							)

          self.scheduler.set_timesteps(__A							,device=__A							)
          snake_case			:      Union[str, Any]        =			self.scheduler.timesteps

          snake_case			:      List[str]        =			self.unet.config.in_channels

          snake_case ,		snake_case			:      Any        =			downscale_height_and_width(__A							,__A							,self.movq_scale_factor							)

          # create initial latent
          snake_case			:      Union[str, Any]        =			self.prepare_latents(
              (batch_size, num_channels_latents, height, width)							,image_embeds.dtype							,__A							,__A							,__A							,self.scheduler							,)

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

               snake_case			:      Any        =			{"""image_embeds""": image_embeds}
               snake_case			:      Optional[Any]        =			self.unet(
                   sample=__A							,timestep=__A							,encoder_hidden_states=__A							,added_cond_kwargs=__A							,return_dict=__A							,)[0]

               if do_classifier_free_guidance:
                    snake_case ,		snake_case			:      Optional[int]        =			noise_pred.split(latents.shape[1]							,dim=1							)
                    snake_case ,		snake_case			:      Union[str, Any]        =			noise_pred.chunk(2							)
                    snake_case ,		snake_case			:      Tuple        =			variance_pred.chunk(2							)
                    snake_case			:      Any        =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                    snake_case			:      Optional[Any]        =			torch.cat([noise_pred, variance_pred_text]							,dim=1							)

               if not (
                   hasattr(self.scheduler.config							,"""variance_type"""							)
                   and self.scheduler.config.variance_type in ["learned", "learned_range"]
               ):
                    snake_case ,		snake_case			:      Optional[Any]        =			noise_pred.split(latents.shape[1]							,dim=1							)

               # compute the previous noisy sample x_t -> x_t-1
               snake_case			:      List[str]        =			self.scheduler.step(
                   __A							,__A							,__A							,generator=__A							,)[0]
          # post-processing
          snake_case			:      Any        =			self.movq.decode(__A							,force_not_quantize=__A							)["""sample"""]

          if output_type not in ["pt", "np", "pil"]:
               raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''							)

          if output_type in ["np", "pil"]:
               snake_case			:      int        =			image * 0.5 + 0.5
               snake_case			:      List[Any]        =			image.clamp(0							,1							)
               snake_case			:      Optional[int]        =			image.cpu().permute(0							,2							,3							,1							).float().numpy()

          if output_type == "pil":
               snake_case			:      Optional[Any]        =			self.numpy_to_pil(__A							)

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=__A							)







import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


SCREAMING_SNAKE_CASE							    =							{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class     __UpperCAmelCase							(       nn.Module							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__()
        __a					=  torchvision.models.resnetaaa(pretrained=__A    )
        __a					=  list(model.children()    )[:-2]
        __a					=  nn.Sequential(*__A    )
        __a					=  nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )


     def 	snake_case_  (	self  ,       __A    ):
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        __a					=  self.pool(self.model(__A    )    )
        __a					=  torch.flatten(__A  ,       start_dim=2    )
        __a					=  out.transpose(1  ,       2    ).contiguous()
        return out  # BxNx2048







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        __a					=  [json.loads(__A    ) for l in open(__A    )]
        __a					=  os.path.dirname(__A    )
        __a					=  tokenizer
        __a					=  labels
        __a					=  len(__A    )
        __a					=  max_seq_length

        __a					=  transforms


     def __len__(	self    ):
        return len(self.data    )


     def __getitem__(	self  ,       __A    ):
        __a					=  torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]  ,       add_special_tokens=__A    )    )
        __a	,    __a	,    __a					=  sentence[0], sentence[1:-1], sentence[-1]
        __a					=  sentence[: self.max_seq_length]

        __a					=  torch.zeros(self.n_classes    )
        __a					=  1

        __a					=  Image.open(os.path.join(self.data_dir  ,       self.data[index]["""img"""]    )    ).convert("""RGB"""    )
        __a					=  self.transforms(__A    )

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }


     def 	snake_case_  (	self    ):
        __a					=  Counter()
        for row in self.data:
           label_freqs.update(row["""label"""]    )
        return label_freqs







def  a   (lowerCAmelCase__   ):
   __a					=  [len(row["""sentence"""]   ) for row in batch]
   __a	,    __a					=  len(lowerCAmelCase__   ), max(lowerCAmelCase__   )

   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )
   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )

   for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase__		,				lowerCAmelCase__   )   ):
      __a					=  input_row["""sentence"""]
      __a					=  1

   __a					=  torch.stack([row["""image"""] for row in batch]   )
   __a					=  torch.stack([row["""label"""] for row in batch]   )
   __a					=  torch.stack([row["""image_start_token"""] for row in batch]   )
   __a					=  torch.stack([row["""image_end_token"""] for row in batch]   )

   return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def  a   ():
   return [
       "Crime",
       "Drama",
       "Thriller",
       "Action",
       "Comedy",
       "Romance",
       "Documentary",
       "Short",
       "Mystery",
       "History",
       "Family",
       "Adventure",
       "Fantasy",
       "Sci-Fi",
       "Western",
       "Horror",
       "Sport",
       "War",
       "Music",
       "Musical",
       "Animation",
       "Biography",
       "Film-Noir",
   ]




def  a   ():
   return transforms.Compose(
       [
           transforms.Resize(256   ),
           transforms.CenterCrop(224   ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]		,				std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]		,				),
       ]   )



