


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


lowercase_	   =     {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[str]						,				a:	Dict    ) ->    List[str]:
       super().__init__()
       __lowerCamelCase		:							Tuple		 =						torchvision.models.resnetaaa(pretrained=a    )
       __lowerCamelCase		:							str		 =						list(model.children()    )[:-2]
       __lowerCamelCase		:							Optional[int]		 =						nn.Sequential(*a    )
       __lowerCamelCase		:							List[str]		 =						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )




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



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


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

       __lowerCamelCase		:							Any		 =						transforms




      def __len__(    self:	Union[str, Any]    ) ->    Dict:
       return len(self.data    )




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

       __lowerCamelCase		:							Any		 =						torch.zeros(self.n_classes    )
       __lowerCamelCase		:							Dict		 =						1

       __lowerCamelCase		:							Any		 =						Image.open(os.path.join(self.data_dir						,				self.data[index]['img']    )    ).convert('RGB'    )
       __lowerCamelCase		:							int		 =						self.transforms(a    )

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




      def 					_snake_case     (    self:	Optional[Any]    ) ->    Dict:
       __lowerCamelCase		:							Dict		 =						Counter()
       for row in self.data:
        label_freqs.update(row['label']    )
       return label_freqs



def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int		 =						[len(row['sentence']						) for row in batch]
 __lowerCamelCase		:							List[Any]		 =						len(SCREAMING_SNAKE_CASE__						), max(SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Any		 =						torch.zeros(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	dtype=torch.long						)
 __lowerCamelCase		:							int		 =						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__						)						):
  __lowerCamelCase		:							Optional[Any]		 =						input_row['sentence']
  __lowerCamelCase		:							Union[str, Any]		 =						1

 __lowerCamelCase		:							List[Any]		 =						torch.stack([row['image'] for row in batch]						)
 __lowerCamelCase		:							List[Any]		 =						torch.stack([row['label'] for row in batch]						)
 __lowerCamelCase		:							Optional[Any]		 =						torch.stack([row['image_start_token'] for row in batch]						)
 __lowerCamelCase		:							Optional[int]		 =						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 				UpperCamelCase__					(  ):
 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 				UpperCamelCase__					(  ):
 return transforms.Compose(
     [
         transforms.Resize(256						),
         transforms.CenterCrop(224						),
         transforms.ToTensor(),
         transforms.Normalize(
             mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]      ,	std=[0.12_221_994, 0.12_145_835, 0.14_380_469]      ,	),
     ]						)





import warnings

from .generation import TFGenerationMixin


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      warnings.warn(
          """Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
          """be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" ,				__UpperCamelCase ,				)

