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


_lowerCAmelCase 				=     {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      ,       a__       ):
         super().__init__()
         A_     :				List[Any]			      =	torchvision.models.resnetaaa(pretrained=a__       )
         A_     :				Dict			      =	list(model.children()       )[:-2]
         A_     :				Optional[Any]			      =	nn.Sequential(*a__       )
         A_     :				Any			      =	nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]       )






      def 		_lowerCamelCase      (		self      ,       a__       ):
         # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
         A_     :				List[Any]			      =	self.pool(self.model(a__       )       )
         A_     :				Optional[Any]			      =	torch.flatten(a__      ,       start_dim=2       )
         A_     :				int			      =	out.transpose(1      ,       2       ).contiguous()
         return out  # BxNx2048






class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __init__(		self      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__       ):
         A_     :				Optional[int]			      =	[json.loads(a__       ) for l in open(a__       )]
         A_     :				List[str]			      =	os.path.dirname(a__       )
         A_     :				str			      =	tokenizer
         A_     :				Dict			      =	labels
         A_     :				Dict			      =	len(a__       )
         A_     :				List[str]			      =	max_seq_length

         A_     :				Any			      =	transforms
      def __len__(		self       ):
         return len(self.data       )
      def __getitem__(		self      ,       a__       ):
         A_     :				List[Any]			      =	torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]      ,       add_special_tokens=a__       )       )
         A_     :				Optional[Any]			      =	sentence[0], sentence[1:-1], sentence[-1]
         A_     :				int			      =	sentence[: self.max_seq_length]

         A_     :				Dict			      =	torch.zeros(self.n_classes       )
         A_     :				Union[str, Any]			      =	1

         A_     :				str			      =	Image.open(os.path.join(self.data_dir      ,       self.data[index]["""img"""]       )       ).convert("""RGB"""       )
         A_     :				Dict			      =	self.transforms(a__       )

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






      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	Counter()
         for row in self.data:
            label_freqs.update(row["""label"""]       )
         return label_freqs






def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Tuple			      =	[len(row["""sentence"""]     ) for row in batch]
   A_     :				Dict			      =	len(_lowerCAmelCase     ), max(_lowerCAmelCase     )

   A_     :				Any			      =	torch.zeros(_lowerCAmelCase  ,_lowerCAmelCase  ,dtype=torch.long     )
   A_     :				Optional[Any]			      =	torch.zeros(_lowerCAmelCase  ,_lowerCAmelCase  ,dtype=torch.long     )

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

   A_     :				str			      =	torch.stack([row["""image"""] for row in batch]     )
   A_     :				List[str]			      =	torch.stack([row["""label"""] for row in batch]     )
   A_     :				Optional[Any]			      =	torch.stack([row["""image_start_token"""] for row in batch]     )
   A_     :				Union[str, Any]			      =	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(2_5_6     ),
           transforms.CenterCrop(2_2_4     ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]  ,std=[0.1222_1994, 0.1214_5835, 0.1438_0469]  ,),
       ]     )













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase 				=     {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NllbMoeForConditionalGeneration""",
          """NllbMoeModel""",
          """NllbMoePreTrainedModel""",
          """NllbMoeTop2Router""",
          """NllbMoeSparseMLP""",
      ]


if TYPE_CHECKING:
      from .configuration_nllb_moe import (
          NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
          NllbMoeConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nllb_moe import (
                NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
                NllbMoeForConditionalGeneration,
                NllbMoeModel,
                NllbMoePreTrainedModel,
                NllbMoeSparseMLP,
                NllbMoeTopaRouter,
            )


else:
      import sys

      _lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






