

"""simple docstring"""





import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available




def   _A  (  *UpperCamelCase_     :       List[Any])	->						List[str]:
   '''simple docstring'''


   if not isinstance(UpperCamelCase_,			UpperCamelCase_):
      __lowercase								=						list(UpperCamelCase_)
   for i in range(len(UpperCamelCase_)):
      __lowercase								=						None
   gc.collect()
   if is_xpu_available():
      torch.xpu.empty_cache()
   elif is_npu_available():
      torch.npu.empty_cache()
   else:
      torch.cuda.empty_cache()
   return objects




def   _A  (  UpperCamelCase_     :       Exception)	->						bool:
   '''simple docstring'''


   __lowercase								=						[
       "CUDA out of memory.",  # CUDA OOM
       "cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.",  # CUDNN SNAFU
       "DefaultCPUAllocator: can't allocate memory",  # CPU OOM
   ]
   if isinstance(UpperCamelCase_,			UpperCamelCase_) and len(exception.args) == 1:
      return any(err in exception.args[0] for err in _statements)
   return False




def   _A  (  UpperCamelCase_     :       callable = None,			UpperCamelCase_     :       int = 128)	->						int:
   '''simple docstring'''


   if function is None:
      return functools.partial(UpperCamelCase_,			starting_batch_size=UpperCamelCase_)

   __lowercase								=						starting_batch_size

   def decorator(*UpperCamelCase_     :       List[str],			**UpperCamelCase_     :       Optional[Any]):
      nonlocal batch_size
      gc.collect()
      if is_xpu_available():
         torch.xpu.empty_cache()
      elif is_npu_available():
         torch.npu.empty_cache()
      else:
         torch.cuda.empty_cache()
      __lowercase								=						list(inspect.signature(UpperCamelCase_).parameters.keys())
      # Guard against user error
      if len(UpperCamelCase_) < (len(UpperCamelCase_) + 1):
         __lowercase								=						", ".join([F"""{arg}={value}""" for arg, value in zip(params[1:],			args[1:])])
         raise TypeError(
             F"""Batch size was passed into `{function.__name__}` as the first argument when called."""
             F"""Remove this as the decorator already does so: `{function.__name__}({arg_str})`""")
      while True:
         if batch_size == 0:
            raise RuntimeError("No executable batch size found, reached zero.")
         try:
            return function(UpperCamelCase_,			*UpperCamelCase_,			**UpperCamelCase_)
         except Exception as e:
            if should_reduce_batch_size(UpperCamelCase_):
               gc.collect()
               if is_xpu_available():
                  torch.xpu.empty_cache()
               elif is_npu_available():
                  torch.npu.empty_cache()
               else:
                  torch.cuda.empty_cache()
               batch_size //= 2
            else:
               raise

   return decorator








"""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    :     str   						=		{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    : Union[str, Any] ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												super().__init__()
												__UpperCAmelCase   :						Union[str, Any]      							=					torchvision.models.resnetaaa(pretrained=UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					list(model.children()  )[:-2]
												__UpperCAmelCase   :						Tuple      							=					nn.Sequential(*UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






					def lowerCamelCase__							(       self    : List[Any] ,       UpperCamelCase    : List[str]  ):
												'''simple docstring'''






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






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : str ,       UpperCamelCase    : str ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : Any ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






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

												__UpperCAmelCase   :						Tuple      							=					transforms






					def __len__(       self    : Tuple  ):
												'''simple docstring'''






												return len(self.data  )






					def __getitem__(       self    : int ,       UpperCamelCase    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,       add_special_tokens=UpperCamelCase  )  )
												__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Dict      							=					sentence[0], sentence[1:-1], sentence[-1]
												__UpperCAmelCase   :						Union[str, Any]      							=					sentence[: self.max_seq_length]

												__UpperCAmelCase   :						Tuple      							=					torch.zeros(self.n_classes  )
												__UpperCAmelCase   :						str      							=					1

												__UpperCAmelCase   :						List[str]      							=					Image.open(os.path.join(self.data_dir ,       self.data[index]["""img"""]  )  ).convert("""RGB"""  )
												__UpperCAmelCase   :						Any      							=					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'''






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






def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       Any:

							'''simple docstring'''



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

							__UpperCAmelCase   :						int      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)
							__UpperCAmelCase   :						Any      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)

							for i_batch, (input_row, length) in enumerate(zip(_UpperCamelCase       ,			_UpperCamelCase							)							):
														__UpperCAmelCase   :						List[str]      							=					input_row["""sentence"""]
														__UpperCAmelCase   :						Optional[Any]      							=					1

							__UpperCAmelCase   :						int      							=					torch.stack([row["""image"""] for row in batch]							)
							__UpperCAmelCase   :						Optional[int]      							=					torch.stack([row["""label"""] for row in batch]							)
							__UpperCAmelCase   :						int      							=					torch.stack([row["""image_start_token"""] for row in batch]							)
							__UpperCAmelCase   :						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       (     )		->       List[str]:

							'''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       (     )		->       Dict:

							'''simple docstring'''



							return transforms.Compose(
							    [
							        transforms.Resize(2_5_6							),
							        transforms.CenterCrop(2_2_4							),
							        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]       ,			),
							    ]							)
