



'''simple docstring'''





import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
SCREAMING_SNAKE_CASE__          =  logging.getLogger(__name__)




def 							lowerCamelCase	(					_snake_case	:    str							):





    '''simple docstring'''


    lowercase__									=    git.Repo(search_parent_directories=_snake_case							)
    lowercase__									=    {
        "repo_id": str(_snake_case							),
        "repo_sha": str(repo.head.object.hexsha							),
        "repo_branch": str(repo.active_branch							),
    }

    with open(os.path.join(_snake_case  ,"git_log.json"							)  ,"w"							) as f:
        json.dump(_snake_case  ,_snake_case  ,indent=4							)




def 							lowerCamelCase	(					_snake_case	:    Tuple							):





    '''simple docstring'''


    if params.n_gpu <= 0:
        lowercase__									=    0
        lowercase__									=    -1
        lowercase__									=    True
        lowercase__									=    False
        return

    assert torch.cuda.is_available()

    logger.info("Initializing GPUs"							)
    if params.n_gpu > 1:
        assert params.local_rank != -1

        lowercase__									=    int(os.environ["WORLD_SIZE"]							)
        lowercase__									=    int(os.environ["N_GPU_NODE"]							)
        lowercase__									=    int(os.environ["RANK"]							)

        # number of nodes / node ID
        lowercase__									=    params.world_size // params.n_gpu_per_node
        lowercase__									=    params.global_rank // params.n_gpu_per_node
        lowercase__									=    True

        assert params.n_nodes == int(os.environ["N_NODES"]							)
        assert params.node_id == int(os.environ["NODE_RANK"]							)

    # local job (single GPU)
    else:
        assert params.local_rank == -1

        lowercase__									=    1
        lowercase__									=    0
        lowercase__									=    0
        lowercase__									=    0
        lowercase__									=    1
        lowercase__									=    1
        lowercase__									=    False

    # sanity checks
    assert params.n_nodes >= 1
    assert 0 <= params.node_id < params.n_nodes
    assert 0 <= params.local_rank <= params.global_rank < params.world_size
    assert params.world_size == params.n_nodes * params.n_gpu_per_node

    # define whether this is the master process / if we are in multi-node distributed mode
    lowercase__									=    params.node_id == 0 and params.local_rank == 0
    lowercase__									=    params.n_nodes > 1

    # summary
    lowercase__									=    f'''--- Global rank: {params.global_rank} - '''
    logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes							)
    logger.info(PREFIX + "Node ID        : %i" % params.node_id							)
    logger.info(PREFIX + "Local rank     : %i" % params.local_rank							)
    logger.info(PREFIX + "World size     : %i" % params.world_size							)
    logger.info(PREFIX + "GPUs per node  : %i" % params.n_gpu_per_node							)
    logger.info(PREFIX + "Master         : %s" % str(params.is_master							)							)
    logger.info(PREFIX + "Multi-node     : %s" % str(params.multi_node							)							)
    logger.info(PREFIX + "Multi-GPU      : %s" % str(params.multi_gpu							)							)
    logger.info(PREFIX + "Hostname       : %s" % socket.gethostname()							)

    # set GPU device
    torch.cuda.set_device(params.local_rank							)

    # initialize multi-GPU
    if params.multi_gpu:
        logger.info("Initializing PyTorch distributed"							)
        torch.distributed.init_process_group(
            init_method="env://"  ,backend="nccl"  ,)




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]							):





    '''simple docstring'''


    np.random.seed(args.seed							)
    torch.manual_seed(args.seed							)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed							)









'''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


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 					snake_case			(nn.Module ):



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







       def 				_a   (					self  ,UpperCAmelCase_   )	->				str:
           # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
           lowercase__									=    self.pool(self.model(UpperCAmelCase_   )   )
           lowercase__									=    torch.flatten(UpperCAmelCase_  ,start_dim=2   )
           lowercase__									=    out.transpose(1  ,2   ).contiguous()
           return out  # BxNx2048





class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    [json.loads(UpperCAmelCase_   ) for l in open(UpperCAmelCase_   )]
           lowercase__									=    os.path.dirname(UpperCAmelCase_   )
           lowercase__									=    tokenizer
           lowercase__									=    labels
           lowercase__									=    len(UpperCAmelCase_   )
           lowercase__									=    max_seq_length

           lowercase__									=    transforms



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



       def __getitem__(					self  ,UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,add_special_tokens=UpperCAmelCase_   )   )
           lowercase__							,							lowercase__							,							lowercase__									=    sentence[0], sentence[1:-1], sentence[-1]
           lowercase__									=    sentence[: self.max_seq_length]

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

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

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







       def 				_a   (					self   )	->				str:
           lowercase__									=    Counter()
           for row in self.data:
               label_freqs.update(row["label"]   )
           return label_freqs





def 							lowerCamelCase	(					_snake_case	:    Optional[int]							):





    '''simple docstring'''


    lowercase__									=    [len(row["sentence"]							) for row in batch]
    lowercase__							,							lowercase__									=    len(_snake_case							), max(_snake_case							)

    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)
    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)

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

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





    '''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	(					):





    '''simple docstring'''


    return transforms.Compose(
        [
            transforms.Resize(256							),
            transforms.CenterCrop(224							),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]  ,std=[0.12_221_994, 0.12_145_835, 0.14_380_469]  ,),
        ]							)




