






"""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


UpperCAmelCase_   : Union[str, Any]   			= {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     :  str						,	lowercase_     :  Optional[int]):




          '''simple docstring'''


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



      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	lowercase_     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.pool(self.model(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.flatten(lowercase_						,	start_dim=2)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		out.transpose(1						,	2).contiguous()
          return out  # BxNx2048




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def __init__(						self     :  Any						,	lowercase_     :  List[Any]						,	lowercase_     :  Any						,	lowercase_     :  Tuple						,	lowercase_     :  str						,	lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		[json.loads(lowercase_) for l in open(lowercase_)]
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		os.path.dirname(lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		tokenizer
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		labels
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		len(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		max_seq_length

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		transforms
      def __len__(						self     :  List[Any]):




          '''simple docstring'''


          return len(self.data)
      def __getitem__(						self     :  Optional[Any]						,	lowercase_     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']						,	add_special_tokens=lowercase_))
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		sentence[0], sentence[1:-1], sentence[-1]
          SCREAMING_SNAKE_CASE_      :  Any    						=		sentence[: self.max_seq_length]

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

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		Image.open(os.path.join(self.data_dir						,	self.data[index]['''img'''])).convert('''RGB''')
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.transforms(lowercase_)

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



      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		Counter()
          for row in self.data:
              label_freqs.update(row['''label'''])
          return label_freqs



def 					_A			(__a							) ->       Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[len(row['''sentence''']							) for row in batch]
    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		len(__a							), max(__a							)

    SCREAMING_SNAKE_CASE_      :  Tuple    						=		torch.zeros(__a    ,				__a    ,				dtype=torch.long							)
    SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.zeros(__a    ,				__a    ,				dtype=torch.long							)

    for i_batch, (input_row, length) in enumerate(zip(__a    ,				__a							)							):
        SCREAMING_SNAKE_CASE_      :  Any    						=		input_row['''sentence''']
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		1

    SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.stack([row['''image'''] for row in batch]							)
    SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.stack([row['''label'''] for row in batch]							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.stack([row['''image_start_token'''] for row in batch]							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		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			() ->       Tuple:
    """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 					_A			() ->       List[str]:
    """simple docstring"""






    return transforms.Compose(
        [
            transforms.Resize(2_56							),
            transforms.CenterCrop(2_24							),
            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]    ,				),
        ]							)










"""simple docstring"""






from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)


