



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


UpperCAmelCase      :							List[str]							       ={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   ,       snake_case__			):

									'''simple docstring'''


									super().__init__()
									UpperCamelCase_						     =      torchvision.models.resnetaaa(pretrained=_UpperCAmelCase			)
									UpperCamelCase_						     =      list(model.children()			)[:-2]
									UpperCamelCase_						     =      nn.Sequential(*_UpperCAmelCase			)
									UpperCamelCase_						     =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]			)

				def 					_lowerCamelCase    (       self   ,       snake_case__			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.pool(self.model(_UpperCAmelCase			)			)
									UpperCamelCase_						     =      torch.flatten(_UpperCAmelCase   ,       start_dim=2			)
									UpperCamelCase_						     =      out.transpose(1   ,       2			).contiguous()
									return out  # BxNx2048




class 					_lowercase      (_UpperCAmelCase					):
				'''simple docstring'''







				def __init__(       self   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__			):

									'''simple docstring'''


									UpperCamelCase_						     =      [json.loads(_UpperCAmelCase			) for l in open(_UpperCAmelCase			)]
									UpperCamelCase_						     =      os.path.dirname(_UpperCAmelCase			)
									UpperCamelCase_						     =      tokenizer
									UpperCamelCase_						     =      labels
									UpperCamelCase_						     =      len(_UpperCAmelCase			)
									UpperCamelCase_						     =      max_seq_length

									UpperCamelCase_						     =      transforms

				def __len__(       self			):

									'''simple docstring'''


									return len(self.data			)

				def __getitem__(       self   ,       snake_case__			):

									'''simple docstring'''


									UpperCamelCase_						     =      torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]   ,       add_special_tokens=_UpperCAmelCase			)			)
									UpperCamelCase_						     =      sentence[0], sentence[1:-1], sentence[-1]
									UpperCamelCase_						     =      sentence[: self.max_seq_length]

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

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

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

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


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




def       _lowerCAmelCase			(_lowerCAmelCase):
					UpperCamelCase_						     =      [len(row["sentence"]) for row in batch]
					UpperCamelCase_						     =      len(__UpperCAmelCase), max(__UpperCAmelCase)

					UpperCamelCase_						     =      torch.zeros(__UpperCAmelCase      ,		__UpperCAmelCase      ,		dtype=torch.long)
					UpperCamelCase_						     =      torch.zeros(__UpperCAmelCase      ,		__UpperCAmelCase      ,		dtype=torch.long)

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

					UpperCamelCase_						     =      torch.stack([row["image"] for row in batch])
					UpperCamelCase_						     =      torch.stack([row["label"] for row in batch])
					UpperCamelCase_						     =      torch.stack([row["image_start_token"] for row in batch])
					UpperCamelCase_						     =      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			():
					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			():
					return transforms.Compose(
					    [
					        transforms.Resize(2_56),
					        transforms.CenterCrop(2_24),
					        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"""






from __future__ import annotations

from math import pi, sqrt



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  tuple:

   if inductance <= 0:
      raise ValueError('''Inductance cannot be 0 or negative''' )

   elif capacitance <= 0:
      raise ValueError('''Capacitance cannot be 0 or negative''' )

   else:
      return (
          "Resonant frequency",
          float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
