
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						:      int									= {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 				lowerCamelCase__							(       nn.Module):




	'''simple docstring'''




	def __init__(      self	:Union[str, Any]  ,     a	:List[str]		)	->				Union[str, Any]:
								super().__init__()
								__UpperCamelCase			:     Tuple         =      torchvision.models.resnetaaa(pretrained=A__		)
								__UpperCamelCase			:     List[Any]         =      list(model.children()		)[:-2]
								__UpperCamelCase			:     str         =      nn.Sequential(*A__		)
								__UpperCamelCase			:     Dict         =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]		)







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




class 				lowerCamelCase__							(       __a):




	'''simple docstring'''




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

								__UpperCamelCase			:     Any         =      transforms







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







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

								__UpperCamelCase			:     str         =      torch.zeros(self.n_classes		)
								__UpperCamelCase			:     Union[str, Any]         =      1

								__UpperCamelCase			:     Union[str, Any]         =      Image.open(os.path.join(self.data_dir  ,     self.data[index]["img"]		)		).convert("RGB"		)
								__UpperCamelCase			:     List[Any]         =      self.transforms(A__		)

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







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				str:
								__UpperCamelCase			:     Optional[int]         =      Counter()
								for row in self.data:
															label_freqs.update(row["label"]		)
								return label_freqs




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any])      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[Any]         =      [len(row["sentence"]) for row in batch]
							__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      len(_lowerCamelCase), max(_lowerCamelCase)

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

							for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase		,						_lowerCamelCase)):
														__UpperCamelCase			:     Optional[Any]         =      input_row["sentence"]
														__UpperCamelCase			:     Dict         =      1

							__UpperCamelCase			:     Optional[Any]         =      torch.stack([row["image"] for row in batch])
							__UpperCamelCase			:     Tuple         =      torch.stack([row["label"] for row in batch])
							__UpperCamelCase			:     Optional[int]         =      torch.stack([row["image_start_token"] for row in batch])
							__UpperCamelCase			:     str         =      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 			_SCREAMING_SNAKE_CASE      (	)      ->					str:






							'''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 			_SCREAMING_SNAKE_CASE      (	)      ->					Any:






							'''simple docstring'''





							return transforms.Compose(
							    [
							        transforms.Resize(256),
							        transforms.CenterCrop(224),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]		,						std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]		,						),
							    ])



from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					None:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      analyze_text(_lowerCamelCase)
							__UpperCamelCase			:     List[str]         =      list(" " + ascii_lowercase)
							# what is our total sum of probabilities.
							__UpperCamelCase			:     Any         =      sum(single_char_strings.values())

							# one length string
							__UpperCamelCase			:     Optional[Any]         =      0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					__UpperCamelCase			:     List[Any]         =      single_char_strings[ch]
																					__UpperCamelCase			:     List[str]         =      my_str / all_sum
																					my_fir_sum += prob * math.loga(_lowerCamelCase)  # entropy formula.

    # print entropy
							print(F'{round(-1 * my_fir_sum):.1f}')

							# two len string
							__UpperCamelCase			:     Optional[Any]         =      sum(two_char_strings.values())
							__UpperCamelCase			:     Tuple         =      0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					__UpperCamelCase			:     List[str]         =      cha + cha
																					if sequence in two_char_strings:
																												__UpperCamelCase			:     Optional[Any]         =      two_char_strings[sequence]
																												__UpperCamelCase			:     Any         =      int(_lowerCamelCase) / all_sum
																												my_sec_sum += prob * math.loga(_lowerCamelCase)

    # print second entropy
							print(F'{round(-1 * my_sec_sum):.1f}')

							# print the difference between them
							print(F'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)):.1f}')




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					tuple[dict, dict]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      Counter()  # type: ignore
							__UpperCamelCase			:     Any         =      Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0		,						len(_lowerCamelCase) - 1):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings




def 			_SCREAMING_SNAKE_CASE      (	)      ->					List[str]:






							'''simple docstring'''





							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
		main()