


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


lowerCAmelCase__      		= {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      lowerCAmelCase__       (				nn.Module):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase)			->				Optional[Any]:
            super().__init__()
            _A      :       Any				    = torchvision.models.resnetaaa(pretrained=__lowerCamelCase)
            _A      :       Union[str, Any]				    = list(model.children())[:-2]
            _A      :       str				    = nn.Sequential(*__lowerCamelCase)
            _A      :       Any				    = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
            _A      :       str				    = self.pool(self.model(__lowerCamelCase))
            _A      :       int				    = torch.flatten(__lowerCamelCase ,						start_dim=2)
            _A      :       List[Any]				    = out.transpose(1 ,						2).contiguous()
            return out  # BxNx2048







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[str]:
            _A      :       Dict				    = [json.loads(__lowerCamelCase) for l in open(__lowerCamelCase)]
            _A      :       Union[str, Any]				    = os.path.dirname(__lowerCamelCase)
            _A      :       Optional[Any]				    = tokenizer
            _A      :       Any				    = labels
            _A      :       str				    = len(__lowerCamelCase)
            _A      :       Optional[int]				    = max_seq_length

            _A      :       int				    = transforms


     def __len__(						self)			->				Optional[Any]:
            return len(self.data)


     def __getitem__(						self ,						__lowerCamelCase)			->				List[Any]:
            _A      :       str				    = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"] ,						add_special_tokens=__lowerCamelCase))
            _A    ,			_A    ,			_A      :       List[str]				    = sentence[0], sentence[1:-1], sentence[-1]
            _A      :       int				    = sentence[: self.max_seq_length]

            _A      :       Any				    = torch.zeros(self.n_classes)
            _A      :       Tuple				    = 1

            _A      :       List[Any]				    = Image.open(os.path.join(self.data_dir ,						self.data[index]["img"])).convert("RGB")
            _A      :       Optional[Any]				    = self.transforms(__lowerCamelCase)

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


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Any				    = Counter()
            for row in self.data:
                   label_freqs.update(row["label"])
            return label_freqs




def 							_UpperCAmelCase				(UpperCamelCase__     :  List[Any]						):
       _A      :       List[str]				    = [len(row["sentence"]						) for row in batch]
       _A    ,			_A      :       Union[str, Any]				    = len(UpperCamelCase__						), max(UpperCamelCase__						)

       _A      :       int				    = torch.zeros(UpperCamelCase__				,							UpperCamelCase__				,							dtype=torch.long						)
       _A      :       Tuple				    = torch.zeros(UpperCamelCase__				,							UpperCamelCase__				,							dtype=torch.long						)

       for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__				,							UpperCamelCase__						)						):
              _A      :       Tuple				    = input_row["sentence"]
              _A      :       List[Any]				    = 1

       _A      :       Optional[int]				    = torch.stack([row["image"] for row in batch]						)
       _A      :       Optional[Any]				    = torch.stack([row["label"] for row in batch]						)
       _A      :       List[Any]				    = torch.stack([row["image_start_token"] for row in batch]						)
       _A      :       Dict				    = 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 							_UpperCAmelCase				():
       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 							_UpperCAmelCase				():
       return transforms.Compose(
           [
               transforms.Resize(256						),
               transforms.CenterCrop(224						),
               transforms.ToTensor(),
               transforms.Normalize(
                   mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]				,							std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]				,							),
           ]						)




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       _A    ,			_A      :       Any				    = image.size
       _A    ,			_A      :       str				    = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
       _A      :       List[str]				    = image.resize((w, h)				,							resample=PIL_INTERPOLATION["lanczos"]						)
       _A      :       Any				    = np.array(UpperCamelCase__						).astype(np.floataa						) / 2_55.0
       _A      :       Optional[Any]				    = image[None].transpose(0				,							3				,							1				,							2						)
       _A      :       Union[str, Any]				    = torch.from_numpy(UpperCamelCase__						)
       return 2.0 * image - 1.0







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						)			->				Optional[int]:
            super().__init__()
            self.register_modules(vqvae=__lowerCamelCase ,						unet=__lowerCamelCase ,						scheduler=__lowerCamelCase)


     @torch.no_grad()
     def __call__(						self ,						__lowerCamelCase = None ,						__lowerCamelCase = 1 ,						__lowerCamelCase = 1_0_0 ,						__lowerCamelCase = 0.0 ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						)			->				Union[Tuple, ImagePipelineOutput]:
            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Tuple				    = 1
            elif isinstance(__lowerCamelCase ,						torch.Tensor):
                   _A      :       Union[str, Any]				    = image.shape[0]
            else:
                   raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase)}")

            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Union[str, Any]				    = preprocess(__lowerCamelCase)

            _A    ,			_A      :       Union[str, Any]				    = image.shape[-2:]

            # in_channels should be 6: 3 for latents, 3 for low resolution image
            _A      :       Optional[Any]				    = (batch_size, self.unet.config.in_channels // 2, height, width)
            _A      :       str				    = next(self.unet.parameters()).dtype

            _A      :       Union[str, Any]				    = randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=self.device ,						dtype=__lowerCamelCase)

            _A      :       List[Any]				    = image.to(device=self.device ,						dtype=__lowerCamelCase)

            # set timesteps and move to the correct device
            self.scheduler.set_timesteps(__lowerCamelCase ,						device=self.device)
            _A      :       Any				    = self.scheduler.timesteps

            # scale the initial noise by the standard deviation required by the scheduler
            _A      :       List[str]				    = latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            _A      :       str				    = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
            _A      :       Optional[int]				    = {}
            if accepts_eta:
                   _A      :       List[Any]				    = eta

            for t in self.progress_bar(__lowerCamelCase):
                   # concat latents and low resolution image in the channel dimension.
                   _A      :       List[Any]				    = torch.cat([latents, image] ,						dim=1)
                   _A      :       str				    = self.scheduler.scale_model_input(__lowerCamelCase ,						__lowerCamelCase)
                   # predict the noise residual
                   _A      :       Any				    = self.unet(__lowerCamelCase ,						__lowerCamelCase).sample
                   # compute the previous noisy sample x_t -> x_t-1
                   _A      :       Optional[int]				    = self.scheduler.step(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase).prev_sample

            # decode the image latents with the VQVAE
            _A      :       Union[str, Any]				    = self.vqvae.decode(__lowerCamelCase).sample
            _A      :       Dict				    = torch.clamp(__lowerCamelCase ,						-1.0 ,						1.0)
            _A      :       Tuple				    = image / 2 + 0.5
            _A      :       int				    = image.cpu().permute(0 ,						2 ,						3 ,						1).numpy()

            if output_type == "pil":
                   _A      :       Optional[int]				    = self.numpy_to_pil(__lowerCamelCase)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=__lowerCamelCase)
