'''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


__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       _a    (		nn.Module      ):


  """simple docstring"""

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







    '''simple docstring'''


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





  def 		lowerCamelCase__  (		self      :       Optional[int]   ,				lowercase_      :       Optional[Any]      ):







    '''simple docstring'''


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






class       _a    (		__a      ):


  """simple docstring"""

  def __init__(		self      :       Any   ,				lowercase_      :       Dict   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       Any   ,				lowercase_      :       Tuple   ,				lowercase_      :       List[Any]      ):







    '''simple docstring'''


    lowercase_     =       [json.loads(lowercase_      ) for l in open(lowercase_      )]
    lowercase_     =       os.path.dirname(lowercase_      )
    lowercase_     =       tokenizer
    lowercase_     =       labels
    lowercase_     =       len(lowercase_      )
    lowercase_     =       max_seq_length

    lowercase_     =       transforms




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







    '''simple docstring'''


    return len(self.data      )




  def __getitem__(		self      :       Tuple   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]   ,				add_special_tokens=lowercase_      )      )
    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(lowercase_      )

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





  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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






def   A_      (       SCREAMING_SNAKE_CASE_						)			->str:
  lowercase_     =       [len(row["""sentence"""]						) for row in batch]
  lowercase_     ,	lowercase_     =       len(SCREAMING_SNAKE_CASE_						), max(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       torch.zeros(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	dtype=torch.long						)
  lowercase_     =       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_     =       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   A_      (       )			->Dict:
  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_      (       )			->Tuple:
  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 dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


__snake_case						  =		logging.get_logger(__name__)





@dataclass
class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						[
      '''no_inference''',
      '''no_cuda''',
      '''no_tpu''',
      '''no_speed''',
      '''no_memory''',
      '''no_env_print''',
      '''no_multi_process''',
  ]




  def __init__(		self      :       int   ,				**lowercase_      :       int      ):







    '''simple docstring'''


    for deprecated_arg in self.deprecated_args:
      if deprecated_arg in kwargs:
        lowercase_     =       deprecated_arg[3:]
        setattr(self   ,				lowercase_   ,				not kwargs.pop(lowercase_      )      )
        logger.warning(
            F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
            F""" {positive_arg}={kwargs[positive_arg]}"""      )

    lowercase_     =       kwargs.pop("""torchscript"""   ,				self.torchscript      )
    lowercase_     =       kwargs.pop("""torch_xla_tpu_print_metrics"""   ,				self.torch_xla_tpu_print_metrics      )
    lowercase_     =       kwargs.pop("""fp16_opt_level"""   ,				self.fpaa_opt_level      )
    super().__init__(**lowercase_      )

  A_    							=						field(default=__a ,				metadata={'''help''': '''Trace the models using torchscript'''}      )
  A_    							=						field(default=__a ,				metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}      )
  A_    							=						field(
      default='''O1''' ,				metadata={
          '''help''': (
              '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
              '''See details at https://nvidia.github.io/apex/amp.html'''
          )
      } ,				)




  @cached_property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    logger.info("""PyTorch: setting up devices"""      )
    if not self.cuda:
      lowercase_     =       torch.device("""cpu"""      )
      lowercase_     =       0
    elif is_torch_tpu_available():
      lowercase_     =       xm.xla_device()
      lowercase_     =       0
    else:
      lowercase_     =       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )
      lowercase_     =       torch.cuda.device_count()
    return device, n_gpu




  @property
  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    return is_torch_tpu_available() and self.tpu




  @property
  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    # TODO(PVP): currently only single GPU is supported
    return torch.cuda.current_device()




  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    return self._setup_devices[0]




  @property
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    return self._setup_devices[1]





  @property
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    return self.n_gpu > 0

