


"""simple docstring"""



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


__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 _lowerCAmelCase    (				nn.Module     ):







   def __init__(     self						,     UpperCamelCase__						)     ->		int:

       '''simple docstring'''




       super().__init__()
       snake_case   :				List[str]				 =   torchvision.models.resnetaaa(pretrained=UpperCamelCase__						)
       snake_case   :				Tuple				 =   list(model.children()						)[:-2]
       snake_case   :				str				 =   nn.Sequential(*UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Dict:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   self.pool(self.model(UpperCamelCase__						)						)
       snake_case   :				int				 =   torch.flatten(UpperCamelCase__						,     start_dim=2						)
       snake_case   :				Optional[Any]				 =   out.transpose(1						,     2						).contiguous()
       return out  # BxNx2048







class _lowerCAmelCase    (				snake_case_     ):







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       snake_case   :				int				 =   [json.loads(UpperCamelCase__						) for l in open(UpperCamelCase__						)]
       snake_case   :				Optional[int]				 =   os.path.dirname(UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   tokenizer
       snake_case   :				int				 =   labels
       snake_case   :				Dict				 =   len(UpperCamelCase__						)
       snake_case   :				List[str]				 =   max_seq_length

       snake_case   :				Optional[int]				 =   transforms







   def __len__(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       return len(self.data						)







   def __getitem__(     self						,     UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Dict				 =   torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]						,     add_special_tokens=UpperCamelCase__						)						)
       snake_case ,snake_case ,snake_case   :				Dict				 =   sentence[0], sentence[1:-1], sentence[-1]
       snake_case   :				int				 =   sentence[: self.max_seq_length]

       snake_case   :				Tuple				 =   torch.zeros(self.n_classes						)
       snake_case   :				List[Any]				 =   1

       snake_case   :				Dict				 =   Image.open(os.path.join(self.data_dir						,     self.data[index]["img"]						)						).convert("RGB"						)
       snake_case   :				List[str]				 =   self.transforms(UpperCamelCase__						)

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







   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				Tuple				 =   Counter()
       for row in self.data:
           label_freqs.update(row["label"]						)
       return label_freqs







def   __lowerCAmelCase						(						lowercase  :  int			)     ->						Optional[Any]:


    """simple docstring"""





    snake_case   :				Union[str, Any]				 =   [len(row["sentence"]			) for row in batch]
    snake_case ,snake_case   :				Any				 =   len(lowercase			), max(lowercase			)

    snake_case   :				Optional[Any]				 =   torch.zeros(lowercase						,							lowercase						,							dtype=torch.long			)
    snake_case   :				Optional[int]				 =   torch.zeros(lowercase						,							lowercase						,							dtype=torch.long			)

    for i_batch, (input_row, length) in enumerate(zip(lowercase						,							lowercase			)			):
        snake_case   :				Tuple				 =   input_row["sentence"]
        snake_case   :				int				 =   1

    snake_case   :				Optional[int]				 =   torch.stack([row["image"] for row in batch]			)
    snake_case   :				Optional[int]				 =   torch.stack([row["label"] for row in batch]			)
    snake_case   :				List[Any]				 =   torch.stack([row["image_start_token"] for row in batch]			)
    snake_case   :				Optional[int]				 =   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   __lowerCAmelCase						(						)     ->						Dict:


    """simple docstring"""





    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   __lowerCAmelCase						(						)     ->						List[Any]:


    """simple docstring"""





    return transforms.Compose(
        [
            transforms.Resize(256			),
            transforms.CenterCrop(224			),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]						,							std=[0.1222_1994, 0.1214_5835, 0.1438_0469]						,							),
        ]			)









"""simple docstring"""



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)







@dataclass
class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Union[List[PIL.Image.Image], np.ndarray]
   __UpperCAmelCase			:	Optional[List[bool]]


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .pipeline_cycle_diffusion import CycleDiffusionPipeline
  from .pipeline_stable_diffusion import StableDiffusionPipeline
  from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
  from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
  from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
  from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
  from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
  from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
  from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
  from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
  from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
  from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
  from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
  from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
  from .pipeline_stable_unclip import StableUnCLIPPipeline
  from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
  from .safety_checker import StableDiffusionSafetyChecker
  from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
  from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      StableDiffusionDepthaImgPipeline,
      StableDiffusionDiffEditPipeline,
      StableDiffusionPixaPixZeroPipeline,
  )
else:
  from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
  from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
  from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
  if not (
      is_torch_available()
      and is_transformers_available()
      and is_k_diffusion_available()
      and is_k_diffusion_version(""">=""", """0.0.12""")
  ):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
  from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
  if not (is_transformers_available() and is_onnx_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_onnx_objects import *  # noqa F403
else:
  from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
  from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
  from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
  from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
  from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
  import flax







  @flax.struct.dataclass
  class _lowerCAmelCase    (				snake_case_     ):
     __UpperCAmelCase			:	np.ndarray
     __UpperCAmelCase			:	List[bool]



  from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
  from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
  from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
  from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
  from .safety_checker_flax import FlaxStableDiffusionSafetyChecker





