"""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  UpperCamelCase	(    nn.Module						):
   def __init__(    self  ,__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


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







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Tuple									=			self.pool(self.model(_a    )    )
        lowercase_			:       Any									=			torch.flatten(_a  ,start_dim=2    )
        lowercase_			:       str									=			out.transpose(1  ,2    ).contiguous()
        return out  # BxNx2048







class  UpperCamelCase	(    __SCREAMING_SNAKE_CASE						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


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

        lowercase_			:       str									=			transforms
   def __len__(    self    )			->   Union[str, Any]:


        '''simple docstring'''


        return len(self.data    )
   def __getitem__(    self  ,__UpperCamelCase    )			->   List[str]:


        '''simple docstring'''


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

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

        lowercase_			:       Optional[Any]									=			Image.open(os.path.join(self.data_dir  ,self.data[index]['img']    )    ).convert('RGB'    )
        lowercase_			:       Union[str, Any]									=			self.transforms(_a    )

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







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


        '''simple docstring'''


        lowercase_			:       str									=			Counter()
        for row in self.data:
             label_freqs.update(row['label']    )
        return label_freqs






def     lowercase__( __SCREAMING_SNAKE_CASE							:  str   ):
     lowercase_			:       List[Any]									=			[len(row['sentence']   ) for row in batch]
     lowercase_			:       Dict									=			len(__SCREAMING_SNAKE_CASE   ), max(__SCREAMING_SNAKE_CASE   )

     lowercase_			:       Optional[int]									=			torch.zeros(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    dtype=torch.long   )
     lowercase_			:       Tuple									=			torch.zeros(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    dtype=torch.long   )

     for i_batch, (input_row, length) in enumerate(zip(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )   ):
          lowercase_			:       str									=			input_row["""sentence"""]
          lowercase_			:       Tuple									=			1

     lowercase_			:       int									=			torch.stack([row['image'] for row in batch]   )
     lowercase_			:       str									=			torch.stack([row['label'] for row in batch]   )
     lowercase_			:       List[Any]									=			torch.stack([row['image_start_token'] for row in batch]   )
     lowercase_			:       Tuple									=			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     lowercase__( ):
     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     lowercase__( ):
     return transforms.Compose(
         [
             transforms.Resize(2_56   ),
             transforms.CenterCrop(2_24   ),
             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 argparse import ArgumentParser

from .env import EnvironmentCommand



def     lowercase__( ):
     lowercase_			:       List[Any]									=			ArgumentParser('Diffusers CLI tool'  ,    usage='diffusers-cli <command> [<args>]'   )
     lowercase_			:       int									=			parser.add_subparsers(help='diffusers-cli command helpers'   )

     # Register commands
     EnvironmentCommand.register_subcommand(__SCREAMING_SNAKE_CASE   )

     # Let's go
     lowercase_			:       str									=			parser.parse_args()

     if not hasattr(__SCREAMING_SNAKE_CASE  ,    'func'   ):
          parser.print_help()
          exit(1   )

     # Run
     lowercase_			:       Optional[Any]									=			args.func(__SCREAMING_SNAKE_CASE   )
     service.run()


if __name__ == "__main__":
       main()

