






'''simple docstring'''
import functools
import operator

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


a_ 		=       logging.get_logger(__name__)

a_ 		=       {
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}






class    __SCREAMING_SNAKE_CASE					(	__snake_case ):
 snake_case_     							=							"""wav2vec2"""




 def __init__(					self		:							Dict    ,					__lowercase		:							List[str]=32    ,					__lowercase		:							Optional[Any]=7_68    ,					__lowercase		:							Tuple=12    ,					__lowercase		:							Optional[Any]=12    ,					__lowercase		:							str=30_72    ,					__lowercase		:							List[str]="gelu"    ,					__lowercase		:							Optional[Any]=0.1    ,					__lowercase		:							str=0.1    ,					__lowercase		:							Optional[Any]=0.1    ,					__lowercase		:							List[str]=0.0    ,					__lowercase		:							Optional[int]=0.0    ,					__lowercase		:							int=0.1    ,					__lowercase		:							List[str]=0.1    ,					__lowercase		:							Union[str, Any]=0.02    ,					__lowercase		:							int=1e-5    ,					__lowercase		:							List[Any]="group"    ,					__lowercase		:							Any="gelu"    ,					__lowercase		:							List[str]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)    ,					__lowercase		:							Dict=(5, 2, 2, 2, 2, 2, 2)    ,					__lowercase		:							Optional[int]=(10, 3, 3, 3, 3, 2, 2)    ,					__lowercase		:							Tuple=False    ,					__lowercase		:							Optional[int]=1_28    ,					__lowercase		:							List[Any]=16    ,					__lowercase		:							Dict=False    ,					__lowercase		:							List[Any]=True    ,					__lowercase		:							Optional[Any]=0.05    ,					__lowercase		:							Any=10    ,					__lowercase		:							Dict=2    ,					__lowercase		:							Optional[int]=0.0    ,					__lowercase		:							Dict=10    ,					__lowercase		:							str=0    ,					__lowercase		:							Tuple=3_20    ,					__lowercase		:							List[Any]=2    ,					__lowercase		:							str=0.1    ,					__lowercase		:							str=1_00    ,					__lowercase		:							List[str]=2_56    ,					__lowercase		:							Union[str, Any]=2_56    ,					__lowercase		:							int=0.1    ,					__lowercase		:							List[str]="sum"    ,					__lowercase		:							List[str]=False    ,					__lowercase		:							Union[str, Any]=False    ,					__lowercase		:							Any=2_56    ,					__lowercase		:							int=(5_12, 5_12, 5_12, 5_12, 15_00)    ,					__lowercase		:							str=(5, 3, 3, 1, 1)    ,					__lowercase		:							Union[str, Any]=(1, 2, 3, 1, 1)    ,					__lowercase		:							List[str]=5_12    ,					__lowercase		:							Union[str, Any]=0    ,					__lowercase		:							Any=1    ,					__lowercase		:							str=2    ,					__lowercase		:							Union[str, Any]=False    ,					__lowercase		:							str=3    ,					__lowercase		:							Dict=2    ,					__lowercase		:							List[str]=3    ,					__lowercase		:							Tuple=None    ,					__lowercase		:							Any=None    ,					**__lowercase		:							Dict    ,					)		->  List[str]:
       super().__init__(**__SCREAMING_SNAKE_CASE    ,					pad_token_id=__SCREAMING_SNAKE_CASE    ,					bos_token_id=__SCREAMING_SNAKE_CASE    ,					eos_token_id=__SCREAMING_SNAKE_CASE					)
       SCREAMING_SNAKE_CASE__   : Tuple													=hidden_size
       SCREAMING_SNAKE_CASE__   : int													=feat_extract_norm
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=feat_extract_activation
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=list(__SCREAMING_SNAKE_CASE					)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=list(__SCREAMING_SNAKE_CASE					)
       SCREAMING_SNAKE_CASE__   : List[str]													=list(__SCREAMING_SNAKE_CASE					)
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=conv_bias
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=num_conv_pos_embeddings
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=num_conv_pos_embedding_groups
       SCREAMING_SNAKE_CASE__   : List[Any]													=len(self.conv_dim					)
       SCREAMING_SNAKE_CASE__   : str													=num_hidden_layers
       SCREAMING_SNAKE_CASE__   : Dict													=intermediate_size
       SCREAMING_SNAKE_CASE__   : List[Any]													=hidden_act
       SCREAMING_SNAKE_CASE__   : int													=num_attention_heads
       SCREAMING_SNAKE_CASE__   : Dict													=hidden_dropout
       SCREAMING_SNAKE_CASE__   : int													=attention_dropout
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=activation_dropout
       SCREAMING_SNAKE_CASE__   : Dict													=feat_proj_dropout
       SCREAMING_SNAKE_CASE__   : List[str]													=final_dropout
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=layerdrop
       SCREAMING_SNAKE_CASE__   : str													=layer_norm_eps
       SCREAMING_SNAKE_CASE__   : Tuple													=initializer_range
       SCREAMING_SNAKE_CASE__   : Tuple													=vocab_size
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=do_stable_layer_norm
       SCREAMING_SNAKE_CASE__   : List[Any]													=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__   : List[str]													=apply_spec_augment
       SCREAMING_SNAKE_CASE__   : Tuple													=mask_time_prob
       SCREAMING_SNAKE_CASE__   : Dict													=mask_time_length
       SCREAMING_SNAKE_CASE__   : List[Any]													=mask_time_min_masks
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=mask_feature_prob
       SCREAMING_SNAKE_CASE__   : str													=mask_feature_length
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=mask_feature_min_masks

       # parameters for pretraining with codevector quantized representations
       SCREAMING_SNAKE_CASE__   : Any													=num_codevectors_per_group
       SCREAMING_SNAKE_CASE__   : List[Any]													=num_codevector_groups
       SCREAMING_SNAKE_CASE__   : Any													=contrastive_logits_temperature
       SCREAMING_SNAKE_CASE__   : Tuple													=feat_quantizer_dropout
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=num_negatives
       SCREAMING_SNAKE_CASE__   : int													=codevector_dim
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=proj_codevector_dim
       SCREAMING_SNAKE_CASE__   : List[Any]													=diversity_loss_weight

       # ctc loss
       SCREAMING_SNAKE_CASE__   : List[str]													=ctc_loss_reduction
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=ctc_zero_infinity

       # adapter
       SCREAMING_SNAKE_CASE__   : int													=add_adapter
       SCREAMING_SNAKE_CASE__   : Tuple													=adapter_kernel_size
       SCREAMING_SNAKE_CASE__   : List[Any]													=adapter_stride
       SCREAMING_SNAKE_CASE__   : int													=num_adapter_layers
       SCREAMING_SNAKE_CASE__   : Optional[int]													=output_hidden_size or hidden_size
       SCREAMING_SNAKE_CASE__   : List[Any]													=adapter_attn_dim

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       SCREAMING_SNAKE_CASE__   : Any													=classifier_proj_size

       # XVector-specific parameters. Feel free to ignore for other classes.
       SCREAMING_SNAKE_CASE__   : Tuple													=list(__SCREAMING_SNAKE_CASE					)
       SCREAMING_SNAKE_CASE__   : str													=list(__SCREAMING_SNAKE_CASE					)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=list(__SCREAMING_SNAKE_CASE					)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=xvector_output_dim




 @property
 def    __magic_name__		(					self		:							Optional[Any]					)		->  str:
       return functools.reduce(operator.mul    ,					self.conv_stride    ,					1					)







"""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					:				List[Any]					    =						{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		):






    def __init__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		List[Any]:
      super().__init__()
      lowerCamelCase_         =						torchvision.models.resnetaaa(pretrained=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						list(model.children()					)[:-2]
      lowerCamelCase_         =						nn.Sequential(*__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Any:
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      lowerCamelCase_         =						self.pool(self.model(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						torch.flatten(__SCREAMING_SNAKE_CASE       ,						start_dim=2					)
      lowerCamelCase_         =						out.transpose(1       ,						2					).contiguous()
      return out  # BxNx2048






class      a						(					__snake_case		):






    def __init__(       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							List[Any]					)     ->		Optional[Any]:
      lowerCamelCase_         =						[json.loads(__SCREAMING_SNAKE_CASE					) for l in open(__SCREAMING_SNAKE_CASE					)]
      lowerCamelCase_         =						os.path.dirname(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						tokenizer
      lowerCamelCase_         =						labels
      lowerCamelCase_         =						len(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						max_seq_length

      lowerCamelCase_         =						transforms






    def __len__(       self     :							Any					)     ->		Any:
      return len(self.data					)






    def __getitem__(       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		List[str]:
      lowerCamelCase_         =						torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						sentence[0], sentence[1:-1], sentence[-1]
      lowerCamelCase_         =						sentence[: self.max_seq_length]

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

      lowerCamelCase_         =						Image.open(os.path.join(self.data_dir       ,						self.data[index]['img']					)					).convert('RGB'					)
      lowerCamelCase_         =						self.transforms(__SCREAMING_SNAKE_CASE					)

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






    def    UpperCamelCase       (       self     :							Dict					)     ->		Dict:
      lowerCamelCase_         =						Counter()
      for row in self.data:
        label_freqs.update(row['label']					)
      return label_freqs







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any] )      ->				str:
  lowerCamelCase_         =						[len(row['sentence'] ) for row in batch]
  lowerCamelCase_     ,	lowerCamelCase_         =						len(_lowerCamelCase ), max(_lowerCamelCase )

  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )
  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )

  for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase		,				_lowerCamelCase ) ):
    lowerCamelCase_         =						input_row['sentence']
    lowerCamelCase_         =						1

  lowerCamelCase_         =						torch.stack([row['image'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['label'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['image_start_token'] for row in batch] )
  lowerCamelCase_         =						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]:
  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__       (							)      ->				Union[str, Any]:
  return transforms.Compose(
      [
          transforms.Resize(256 ),
          transforms.CenterCrop(224 ),
          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]		,				),
      ] )




