


"""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 __snake_case		(       nn.Module   ):







   """simple docstring"""




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




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]					,	UpperCamelCase__				:List[str]							):
          # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
          _a							 =					self.pool(self.model(lowerCamelCase__							)							)
          _a							 =					torch.flatten(lowerCamelCase__					,	start_dim=2							)
          _a							 =					out.transpose(1					,	2							).contiguous()
          return out  # BxNx2048






class __snake_case		(       _a   ):







   """simple docstring"""




   def __init__(		self				:List[str]					,	UpperCamelCase__				:Any					,	UpperCamelCase__				:Any					,	UpperCamelCase__				:Optional[int]					,	UpperCamelCase__				:Dict					,	UpperCamelCase__				:Any							):
          _a							 =					[json.loads(lowerCamelCase__							) for l in open(lowerCamelCase__							)]
          _a							 =					os.path.dirname(lowerCamelCase__							)
          _a							 =					tokenizer
          _a							 =					labels
          _a							 =					len(lowerCamelCase__							)
          _a							 =					max_seq_length

          _a							 =					transforms




   def __len__(		self				:List[str]							):
          return len(self.data							)




   def __getitem__(		self				:Optional[Any]					,	UpperCamelCase__				:Dict							):
          _a							 =					torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]					,	add_special_tokens=lowerCamelCase__							)							)
          _a							 =					sentence[0], sentence[1:-1], sentence[-1]
          _a							 =					sentence[: self.max_seq_length]

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

          _a							 =					Image.open(os.path.join(self.data_dir					,	self.data[index]["img"]							)							).convert("RGB"							)
          _a							 =					self.transforms(lowerCamelCase__							)

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




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]							):
          _a							 =					Counter()
          for row in self.data:
                 label_freqs.update(row["label"]							)
          return label_freqs






def     __a   (	a       ):





       """simple docstring"""







       _a							 =					[len(row["sentence"]       ) for row in batch]
       _a							 =					len(a       ), max(a       )

       _a							 =					torch.zeros(a,		a,		dtype=torch.long       )
       _a							 =					torch.zeros(a,		a,		dtype=torch.long       )

       for i_batch, (input_row, length) in enumerate(zip(a,		a       )       ):
              _a							 =					input_row["sentence"]
              _a							 =					1

       _a							 =					torch.stack([row["image"] for row in batch]       )
       _a							 =					torch.stack([row["label"] for row in batch]       )
       _a							 =					torch.stack([row["image_start_token"] for row in batch]       )
       _a							 =					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   (	):





       """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     __a   (	):





       """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  :      Union[str, Any]			 ={
    """configuration_clap""": [
        """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ClapAudioConfig""",
        """ClapConfig""",
        """ClapTextConfig""",
    ],
    """processing_clap""": ["""ClapProcessor"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Union[str, Any]			 =[
					    """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ClapModel""",
					    """ClapPreTrainedModel""",
					    """ClapTextModel""",
					    """ClapTextModelWithProjection""",
					    """ClapAudioModel""",
					    """ClapAudioModelWithProjection""",
					]
					_lowerCAmelCase  :      List[str]			 =["""ClapFeatureExtractor"""]

if TYPE_CHECKING:
					from .configuration_clap import (
					    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
					    ClapAudioConfig,
					    ClapConfig,
					    ClapTextConfig,
					)
					from .processing_clap import ClapProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clap import ClapFeatureExtractor
										from .modeling_clap import (
										    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ClapAudioModel,
										    ClapAudioModelWithProjection,
										    ClapModel,
										    ClapPreTrainedModel,
										    ClapTextModel,
										    ClapTextModelWithProjection,
										)


else:
					import sys

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