



"""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        __snake_case       (	nn.Module):




  def __init__( self     :						Union[str, Any]						,					__lowerCAmelCase     :						Optional[Any]		):






         """simple docstring"""






         super().__init__()
         _lowerCamelCase						:				List[str]    			= torchvision.models.resnetaaa(pretrained=__lowerCAmelCase		)
         _lowerCamelCase						:				str    			= list(model.children()		)[:-2]
         _lowerCamelCase						:				List[Any]    			= nn.Sequential(*__lowerCAmelCase		)
         _lowerCamelCase						:				Any    			= nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]						,					__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= self.pool(self.model(__lowerCAmelCase		)		)
         _lowerCamelCase						:				Tuple    			= torch.flatten(__lowerCAmelCase						,					start_dim=2		)
         _lowerCamelCase						:				Tuple    			= out.transpose(1						,					2		).contiguous()
         return out  # BxNx2048






class        __snake_case       (	SCREAMING_SNAKE_CASE__):




  def __init__( self     :						Union[str, Any]						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						int						,					__lowerCAmelCase     :						str		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[Any]    			= [json.loads(__lowerCAmelCase		) for l in open(__lowerCAmelCase		)]
         _lowerCamelCase						:				Dict    			= os.path.dirname(__lowerCAmelCase		)
         _lowerCamelCase						:				int    			= tokenizer
         _lowerCamelCase						:				List[str]    			= labels
         _lowerCamelCase						:				str    			= len(__lowerCAmelCase		)
         _lowerCamelCase						:				str    			= max_seq_length

         _lowerCamelCase						:				Dict    			= transforms




  def __len__( self     :						int		):






         """simple docstring"""






         return len(self.data		)




  def __getitem__( self     :						List[Any]						,					__lowerCAmelCase     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']						,					add_special_tokens=__lowerCAmelCase		)		)
         _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase						:				List[Any]    			= sentence[0], sentence[1:-1], sentence[-1]
         _lowerCamelCase						:				Tuple    			= sentence[: self.max_seq_length]

         _lowerCamelCase						:				str    			= torch.zeros(self.n_classes		)
         _lowerCamelCase						:				Tuple    			= 1

         _lowerCamelCase						:				str    			= Image.open(os.path.join(self.data_dir						,					self.data[index]['''img''']		)		).convert('''RGB'''		)
         _lowerCamelCase						:				str    			= 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     :						Any		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= Counter()
         for row in self.data:
                label_freqs.update(row['''label''']		)
         return label_freqs






def        snake_case_  (      A_					:		Optional[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				List[str]    			= [len(row['''sentence''']						) for row in batch]
       _lowerCamelCase			,    _lowerCamelCase						:				Union[str, Any]    			= len(__UpperCamelCase						), max(__UpperCamelCase						)

       _lowerCamelCase						:				Any    			= torch.zeros(__UpperCamelCase,				__UpperCamelCase,				dtype=torch.long						)
       _lowerCamelCase						:				List[Any]    			= torch.zeros(__UpperCamelCase,				__UpperCamelCase,				dtype=torch.long						)

       for i_batch, (input_row, length) in enumerate(zip(__UpperCamelCase,				__UpperCamelCase						)						):
              _lowerCamelCase						:				Union[str, Any]    			= input_row['''sentence''']
              _lowerCamelCase						:				List[str]    			= 1

       _lowerCamelCase						:				Optional[Any]    			= torch.stack([row['''image'''] for row in batch]						)
       _lowerCamelCase						:				Optional[int]    			= torch.stack([row['''label'''] for row in batch]						)
       _lowerCamelCase						:				List[Any]    			= torch.stack([row['''image_start_token'''] for row in batch]						)
       _lowerCamelCase						:				Optional[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        snake_case_  (      ):


       '''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        snake_case_  (      ):


       '''simple docstring'''





       return transforms.Compose(
           [
               transforms.Resize(2_56						),
               transforms.CenterCrop(2_24						),
               transforms.ToTensor(),
               transforms.Normalize(
                   mean=[0.46777044, 0.44531429, 0.40661017],				std=[0.12221994, 0.12145835, 0.14380469],				),
           ]						)



from __future__ import annotations


class      lowerCamelCase    :
  """simple docstring"""







  def __init__(							self       :				Union[str, Any]     ,   __magic_name__       :				str     ,   __magic_name__       :				str      )     ->		Dict:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     text, pattern
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     len(__magic_name__      ), len(__magic_name__      )







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				str      )     ->		int:

        for i in range(self.patLen - 1     ,   -1     ,   -1      ):
              if char == self.pattern[i]:
                    return i
        return -1







  def       __A   (							self       :				Dict     ,   __magic_name__       :				int      )     ->		int:

        for i in range(self.patLen - 1     ,   -1     ,   -1      ):
              if self.pattern[i] != self.text[current_pos + i]:
                    return current_pos + i
        return -1







  def       __A   (							self       :				Tuple      )     ->		list[int]:
        # searches pattern in text and returns index positions
        SCREAMING_SNAKE_CASE_      =     []
        for i in range(self.textLen - self.patLen + 1      ):
              SCREAMING_SNAKE_CASE_      =     self.mismatch_in_text(__magic_name__      )
              if mismatch_index == -1:
                    positions.append(__magic_name__      )
              else:
                    SCREAMING_SNAKE_CASE_      =     self.match_in_pattern(self.text[mismatch_index]      )
                    SCREAMING_SNAKE_CASE_      =     (
                        mismatch_index - match_index
                    )  # shifting index lgtm [py/multiple-definition]
        return positions



A   :     Dict       =				"ABAABA"
A   :     Union[str, Any]       =				"AB"
A   :     str       =				BoyerMooreSearch(text, pattern)
A   :     Tuple       =				bms.bad_character_heuristic()

if len(positions) == 0:
      print("No match found")
else:
      print("Pattern found in following positions: ")
      print(positions)


