

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  :      Union[str, Any]			 ={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		):






					'''simple docstring'''



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





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

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



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

						UpperCAmelCase__:	List[Any]									= transforms





					def __len__(				self						):
						return len(self.data						)





					def __getitem__(				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Any									= torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]				,      add_special_tokens=lowerCamelCase__						)						)
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	int									= sentence[0], sentence[1:-1], sentence[-1]
						UpperCAmelCase__:	int									= sentence[: self.max_seq_length]

						UpperCAmelCase__:	Any									= torch.zeros(self.n_classes						)
						UpperCAmelCase__:	Tuple									= 1

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

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Union[str, Any]									= Counter()
						for row in self.data:
							label_freqs.update(row["label"]						)
						return label_freqs



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= [len(row["sentence"]      ) for row in batch]
	UpperCAmelCase__							,						UpperCAmelCase__:	Any									= len(SCREAMING_SNAKE_CASE      ), max(SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	Union[str, Any]									= torch.zeros(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,dtype=torch.long      )
	UpperCAmelCase__:	Any									= 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      )      ):
		UpperCAmelCase__:	Union[str, Any]									= input_row["sentence"]
		UpperCAmelCase__:	List[str]									= 1

	UpperCAmelCase__:	List[str]									= torch.stack([row["image"] for row in batch]      )
	UpperCAmelCase__:	str									= torch.stack([row["label"] for row in batch]      )
	UpperCAmelCase__:	Union[str, Any]									= torch.stack([row["image_start_token"] for row in batch]      )
	UpperCAmelCase__:	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 			_A	(      ):
	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 			_A	(      ):
	return transforms.Compose(
	    [
	        transforms.Resize(2_5_6      ),
	        transforms.CenterCrop(2_2_4      ),
	        transforms.ToTensor(),
	        transforms.Normalize(
	            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]	,std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]	,),
	    ]      )


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	# A local function to see if a dot lands in the circle.
	def is_in_circle(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ) -> bool:
		UpperCAmelCase__:	Any									= sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	UpperCAmelCase__:	Optional[int]									= mean(
	    int(is_in_circle(uniform(-1.0	,1.0      )	,uniform(-1.0	,1.0      )      )      )
	    for _ in range(SCREAMING_SNAKE_CASE      )      )
	# The ratio of the area for circle to square is pi/4.
	UpperCAmelCase__:	Optional[int]									= proportion * 4
	print(f"The estimated value of pi is {pi_estimate}"      )
	print(f"The numpy value of pi is {pi}"      )
	print(f"The total error is {abs(pi - pi_estimate      )}"      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0	,):
	return mean(
	    function_to_integrate(uniform(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )      ) for _ in range(SCREAMING_SNAKE_CASE      )      ) * (max_value - min_value)



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0      ):

	def identity_function(SCREAMING_SNAKE_CASE      ) -> float:
		return x

	UpperCAmelCase__:	Tuple									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[Any]									= (max_value * max_value - min_value * min_value) / 2

	print("******************"      )
	print(f"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {expected_value}"      )
	print(f"Total error is {abs(estimated_value - expected_value      )}"      )
	print("******************"      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	def function_to_integrate(SCREAMING_SNAKE_CASE      ) -> float:
		return sqrt(4.0 - x * x      )

	UpperCAmelCase__:	Union[str, Any]									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,0.0	,2.0      )

	print("******************"      )
	print("Estimating pi using area_under_curve_estimator"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {pi}"      )
	print(f"Total error is {abs(estimated_value - pi      )}"      )
	print("******************"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()