


import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
     return EnvironmentCommand()

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]    ):
     return EnvironmentCommand(args.accelerate_config_file    )






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    @staticmethod
    def _a (      A_   )  ->	Dict:
         __UpperCamelCase   							=parser.add_parser('env'   )
         download_parser.set_defaults(func=A_   )
         download_parser.add_argument(
             '--accelerate-config_file' ,       default=A_ ,       help='The accelerate config file to use for the default values in the launching script.' ,       )
         download_parser.set_defaults(func=A_   )






    def __init__(      self ,       A_ ,       *A_   )  ->	None:
         __UpperCamelCase   							=accelerate_config_file






    def _a (      self   )  ->	Optional[Any]:
         __UpperCamelCase   							='not installed'
         if is_safetensors_available():
              import safetensors

              __UpperCamelCase   							=safetensors.__version__
         elif importlib.util.find_spec('safetensors'   ) is not None:
              import safetensors

              __UpperCamelCase   							=f'{safetensors.__version__} but is ignored because of PyTorch version too old.'

         __UpperCamelCase   							='not installed'
         __UpperCamelCase   							=__UpperCamelCase   							='not found'
         if is_accelerate_available():
              import accelerate
              from accelerate.commands.config import default_config_file, load_config_from_file

              __UpperCamelCase   							=accelerate.__version__
              # Get the default from the config file.
              if self._accelerate_config_file is not None or os.path.isfile(A_   ):
                   __UpperCamelCase   							=load_config_from_file(self._accelerate_config_file   ).to_dict()

              __UpperCamelCase   							=(
                  '\n'.join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]   )
                  if isinstance(A_ ,       A_   )
                  else f'\t{accelerate_config}'
              )

         __UpperCamelCase   							='not installed'
         __UpperCamelCase   							='NA'
         if is_torch_available():
              import torch

              __UpperCamelCase   							=torch.__version__
              __UpperCamelCase   							=torch.cuda.is_available()

         __UpperCamelCase   							='not installed'
         __UpperCamelCase   							='NA'
         if is_tf_available():
              import tensorflow as tf

              __UpperCamelCase   							=tf.__version__
              try:
                   # deprecated in v2.1
                   __UpperCamelCase   							=tf.test.is_gpu_available()
              except AttributeError:
                   # returns list of devices, convert to bool
                   __UpperCamelCase   							=bool(tf.config.list_physical_devices('GPU'   )   )

         __UpperCamelCase   							='not installed'
         __UpperCamelCase   							='not installed'
         __UpperCamelCase   							='not installed'
         __UpperCamelCase   							='NA'
         if is_flax_available():
              import flax
              import jax
              import jaxlib

              __UpperCamelCase   							=flax.__version__
              __UpperCamelCase   							=jax.__version__
              __UpperCamelCase   							=jaxlib.__version__
              __UpperCamelCase   							=jax.lib.xla_bridge.get_backend().platform

         __UpperCamelCase   							={
             '`transformers` version': version,
             'Platform': platform.platform(),
             'Python version': platform.python_version(),
             'Huggingface_hub version': huggingface_hub.__version__,
             'Safetensors version': f'{safetensors_version}',
             'Accelerate version': f'{accelerate_version}',
             'Accelerate config': f'{accelerate_config_str}',
             'PyTorch version (GPU?)': f'{pt_version} ({pt_cuda_available})',
             'Tensorflow version (GPU?)': f'{tf_version} ({tf_cuda_available})',
             'Flax version (CPU?/GPU?/TPU?)': f'{flax_version} ({jax_backend})',
             'Jax version': f'{jax_version}',
             'JaxLib version': f'{jaxlib_version}',
             'Using GPU in script?': '<fill in>',
             'Using distributed or parallel set-up in script?': '<fill in>',
         }

         print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'   )
         print(self.format_dict(A_   )   )

         return info






    @staticmethod
    def _a (      A_   )  ->	Union[str, Any]:
         return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]   ) + "\n"









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


_A			=						{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		):



    """simple docstring"""






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






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






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ ,       A_ ,       A_   )  ->	List[str]:
         __UpperCamelCase   							=[json.loads(A_   ) for l in open(A_   )]
         __UpperCamelCase   							=os.path.dirname(A_   )
         __UpperCamelCase   							=tokenizer
         __UpperCamelCase   							=labels
         __UpperCamelCase   							=len(A_   )
         __UpperCamelCase   							=max_seq_length

         __UpperCamelCase   							=transforms






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






    def __getitem__(      self ,       A_   )  ->	Union[str, Any]:
         __UpperCamelCase   							=torch.LongTensor(self.tokenizer.encode(self.data[index]['text'] ,       add_special_tokens=A_   )   )
         __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=sentence[0], sentence[1:-1], sentence[-1]
         __UpperCamelCase   							=sentence[: self.max_seq_length]

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

         __UpperCamelCase   							=Image.open(os.path.join(self.data_dir ,       self.data[index]['img']   )   ).convert('RGB'   )
         __UpperCamelCase   							=self.transforms(A_   )

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






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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     __UpperCamelCase   							=[len(row['sentence']    ) for row in batch]
     __UpperCamelCase							,				__UpperCamelCase   							=len(SCREAMING_SNAKE_CASE__    ), max(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=torch.zeros(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					dtype=torch.long    )
     __UpperCamelCase   							=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__    )    ):
          __UpperCamelCase   							=input_row['sentence']
          __UpperCamelCase   							=1

     __UpperCamelCase   							=torch.stack([row['image'] for row in batch]    )
     __UpperCamelCase   							=torch.stack([row['label'] for row in batch]    )
     __UpperCamelCase   							=torch.stack([row['image_start_token'] for row in batch]    )
     __UpperCamelCase   							=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(2_56    ),
             transforms.CenterCrop(2_24    ),
             transforms.ToTensor(),
             transforms.Normalize(
                 mean=[0.46777044, 0.44531429, 0.40661017]      ,					std=[0.12221994, 0.12145835, 0.14380469]      ,					),
         ]    )





