




'''simple docstring'''



import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''data2vec-audio'''



   def __init__(   self :     str  ,				__lowerCamelCase :     Any=3_2  ,				__lowerCamelCase :     Union[str, Any]=7_6_8  ,				__lowerCamelCase :     str=1_2  ,				__lowerCamelCase :     Optional[Any]=1_2  ,				__lowerCamelCase :     Union[str, Any]=3_0_7_2  ,				__lowerCamelCase :     Optional[int]="gelu"  ,				__lowerCamelCase :     List[str]=0.1  ,				__lowerCamelCase :     List[Any]=0.1  ,				__lowerCamelCase :     Optional[Any]=0.1  ,				__lowerCamelCase :     Dict=0.0  ,				__lowerCamelCase :     Optional[Any]=0.1  ,				__lowerCamelCase :     List[str]=0.1  ,				__lowerCamelCase :     Optional[int]=0.0_2  ,				__lowerCamelCase :     Any=1e-5  ,				__lowerCamelCase :     str="gelu"  ,				__lowerCamelCase :     List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)  ,				__lowerCamelCase :     int=(5, 2, 2, 2, 2, 2, 2)  ,				__lowerCamelCase :     List[Any]=(1_0, 3, 3, 3, 3, 2, 2)  ,				__lowerCamelCase :     str=False  ,				__lowerCamelCase :     str=1_6  ,				__lowerCamelCase :     Union[str, Any]=1_9  ,				__lowerCamelCase :     List[Any]=5  ,				__lowerCamelCase :     Optional[int]=0.0_5  ,				__lowerCamelCase :     List[str]=1_0  ,				__lowerCamelCase :     Dict=2  ,				__lowerCamelCase :     List[Any]=0.0  ,				__lowerCamelCase :     int=1_0  ,				__lowerCamelCase :     int=0  ,				__lowerCamelCase :     Tuple="sum"  ,				__lowerCamelCase :     Union[str, Any]=False  ,				__lowerCamelCase :     str=False  ,				__lowerCamelCase :     Optional[Any]=2_5_6  ,				__lowerCamelCase :     Optional[int]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)  ,				__lowerCamelCase :     Optional[int]=(5, 3, 3, 1, 1)  ,				__lowerCamelCase :     int=(1, 2, 3, 1, 1)  ,				__lowerCamelCase :     Dict=5_1_2  ,				__lowerCamelCase :     Union[str, Any]=0  ,				__lowerCamelCase :     Union[str, Any]=1  ,				__lowerCamelCase :     Optional[Any]=2  ,				__lowerCamelCase :     Optional[Any]=False  ,				__lowerCamelCase :     Any=3  ,				__lowerCamelCase :     Dict=2  ,				__lowerCamelCase :     List[str]=3  ,				__lowerCamelCase :     Union[str, Any]=None  ,				**__lowerCamelCase :     Dict  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase  ,				pad_token_id=__lowerCamelCase  ,				bos_token_id=__lowerCamelCase  ,				eos_token_id=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				feat_extract_activation
      _SCREAMING_SNAKE_CASE	=				list(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				list(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				list(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				conv_bias
      _SCREAMING_SNAKE_CASE	=				num_conv_pos_embeddings
      _SCREAMING_SNAKE_CASE	=				num_conv_pos_embedding_groups
      _SCREAMING_SNAKE_CASE	=				conv_pos_kernel_size
      _SCREAMING_SNAKE_CASE	=				len(self.conv_dim   )
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				hidden_dropout
      _SCREAMING_SNAKE_CASE	=				attention_dropout
      _SCREAMING_SNAKE_CASE	=				activation_dropout
      _SCREAMING_SNAKE_CASE	=				feat_proj_dropout
      _SCREAMING_SNAKE_CASE	=				final_dropout
      _SCREAMING_SNAKE_CASE	=				layerdrop
      _SCREAMING_SNAKE_CASE	=				layer_norm_eps
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				use_weighted_layer_sum

      if (
          (len(self.conv_stride   ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
          or (len(self.conv_dim   ) != self.num_feat_extract_layers)
      ):
         raise ValueError(
             "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
             " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
             F""" {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,"""
             F""" `len(config.conv_kernel) = {len(self.conv_kernel   )}`."""   )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      _SCREAMING_SNAKE_CASE	=				mask_time_prob
      _SCREAMING_SNAKE_CASE	=				mask_time_length
      _SCREAMING_SNAKE_CASE	=				mask_time_min_masks
      _SCREAMING_SNAKE_CASE	=				mask_feature_prob
      _SCREAMING_SNAKE_CASE	=				mask_feature_length
      _SCREAMING_SNAKE_CASE	=				mask_feature_min_masks

      # ctc loss
      _SCREAMING_SNAKE_CASE	=				ctc_loss_reduction
      _SCREAMING_SNAKE_CASE	=				ctc_zero_infinity

      # adapter
      _SCREAMING_SNAKE_CASE	=				add_adapter
      _SCREAMING_SNAKE_CASE	=				adapter_kernel_size
      _SCREAMING_SNAKE_CASE	=				adapter_stride
      _SCREAMING_SNAKE_CASE	=				num_adapter_layers
      _SCREAMING_SNAKE_CASE	=				output_hidden_size or hidden_size

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      _SCREAMING_SNAKE_CASE	=				classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      _SCREAMING_SNAKE_CASE	=				list(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				list(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				list(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				xvector_output_dim



   @property
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      return math.prod(self.conv_stride   )







'''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


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 		lowercase_   (			nn.Module       ):


   """simple docstring"""

   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     int   ):

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



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      _SCREAMING_SNAKE_CASE	=				self.pool(self.model(__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.flatten(__lowerCamelCase  ,				start_dim=2   )
      _SCREAMING_SNAKE_CASE	=				out.transpose(1  ,				2   ).contiguous()
      return out  # BxNx2048







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[json.loads(__lowerCamelCase   ) for l in open(__lowerCamelCase   )]
      _SCREAMING_SNAKE_CASE	=				os.path.dirname(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				tokenizer
      _SCREAMING_SNAKE_CASE	=				labels
      _SCREAMING_SNAKE_CASE	=				len(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				max_seq_length

      _SCREAMING_SNAKE_CASE	=				transforms



   def __len__(   self :     int   ):

      """simple docstring"""
      return len(self.data   )



   def __getitem__(   self :     str  ,				__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,				add_special_tokens=__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				sentence[0], sentence[1:-1], sentence[-1]
      _SCREAMING_SNAKE_CASE	=				sentence[: self.max_seq_length]

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

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

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



   def 							lowerCAmelCase_      (   self :     Dict   ):

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


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				[len(row["sentence"]  ) for row in batch]
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				len(__A  ), max(__A  )

   _SCREAMING_SNAKE_CASE	=				torch.zeros(__A	,					__A	,					dtype=torch.long  )
   _SCREAMING_SNAKE_CASE	=				torch.zeros(__A	,					__A	,					dtype=torch.long  )

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

   _SCREAMING_SNAKE_CASE	=				torch.stack([row["image"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				torch.stack([row["label"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				torch.stack([row["image_start_token"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				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 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[int]:
   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 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[int]:
   return transforms.Compose(
       [
           transforms.Resize(2_56  ),
           transforms.CenterCrop(2_24  ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]	,					std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]	,					),
       ]  )

