



"""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


lowercase__    :  Dict							    =	{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        _UpperCAmelCase       (			nn.Module):

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






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





class        _UpperCAmelCase       (			lowerCAmelCase__):

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

     snake_case_ :     Dict				=	transforms

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

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

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

     snake_case_ :     Optional[int]				=	Image.open(os.path.join(self.data_dir  ,     self.data[index]['''img''']				)				).convert('''RGB'''				)
     snake_case_ :     str				=	self.transforms(lowercase_				)

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






 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_ :     List[str]				=	Counter()
     for row in self.data:
         label_freqs.update(row['''label''']				)
     return label_freqs





def __lowercase			(							_a    ):
    snake_case_ :     Optional[Any]				=	[len(row['''sentence''']    ) for row in batch]
    snake_case_,	snake_case_ :     List[Any]				=	len(_a    ), max(_a    )

    snake_case_ :     int				=	torch.zeros(_a		,			_a		,			dtype=torch.long    )
    snake_case_ :     int				=	torch.zeros(_a		,			_a		,			dtype=torch.long    )

    for i_batch, (input_row, length) in enumerate(zip(_a		,			_a    )    ):
        snake_case_ :     Optional[Any]				=	input_row['''sentence''']
        snake_case_ :     Optional[int]				=	1

    snake_case_ :     Optional[int]				=	torch.stack([row['''image'''] for row in batch]    )
    snake_case_ :     Union[str, Any]				=	torch.stack([row['''label'''] for row in batch]    )
    snake_case_ :     Tuple				=	torch.stack([row['''image_start_token'''] for row in batch]    )
    snake_case_ :     Union[str, 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 __lowercase			(							):
    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 __lowercase			(							):
    return transforms.Compose(
        [
            transforms.Resize(256    ),
            transforms.CenterCrop(224    ),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]		,			std=[0.1222_1994, 0.1214_5835, 0.1438_0469]		,			),
        ]    )










"""simple docstring"""

import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt




class        _UpperCAmelCase       :

 def __init__(     self		:       List[Any]				):
     snake_case_ :     List[str]				=	''''''
     snake_case_ :     Tuple				=	''''''
     snake_case_ :     int				=	[]
     snake_case_ :     Optional[int]				=	0
     snake_case_ :     Optional[Any]				=	256
     snake_case_ :     Tuple				=	0
     snake_case_ :     Tuple				=	0
     snake_case_ :     Optional[Any]				=	0
     snake_case_ :     Any				=	0

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       List[Any]				):
     snake_case_ :     List[Any]				=	cva.imread(lowercase_  ,     0				)
     snake_case_ :     Tuple				=	copy.deepcopy(self.img				)
     snake_case_,	snake_case_,	snake_case_ :     List[Any]				=	plt.hist(self.img.ravel()  ,     256  ,     [0, 256]  ,     label='''x'''				)
     snake_case_ :     str				=	np.sum(lowercase_				)
     for i in range(len(lowercase_				)				):
         snake_case_ :     Optional[Any]				=	x[i] / self.k
         self.sk += prk
         snake_case_ :     Any				=	(self.L - 1) * self.sk
         if self.rem != 0:
             snake_case_ :     Dict				=	int(last % last				)
         snake_case_ :     Union[str, Any]				=	int(last + 1 if self.rem >= 0.5 else last				)
         self.last_list.append(lowercase_				)
         snake_case_ :     int				=	int(np.ma.count(self.img				) / self.img[1].size				)
         snake_case_ :     Tuple				=	self.img[1].size
     for i in range(self.number_of_cols				):
         for j in range(self.number_of_rows				):
             snake_case_ :     Union[str, Any]				=	self.img[j][i]
             if num != self.last_list[num]:
                 snake_case_ :     List[str]				=	self.last_list[num]
     cva.imwrite('''output_data/output.jpg'''  ,     self.img				)

 def 						_snake_case					(     self		:       Tuple				):
     plt.hist(self.img.ravel()  ,     256  ,     [0, 256]				)






 def 						_snake_case					(     self		:       int				):
     cva.imshow('''Output-Image'''  ,     self.img				)
     cva.imshow('''Input-Image'''  ,     self.original_image				)
     cva.waitKey(5000				)
     cva.destroyAllWindows()


if __name__ == "__main__":
     lowercase__    :  Any							    =	os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
     lowercase__    :  Any							    =	ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()





