






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


__magic_name__:	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 			snake_case__   (				nn.Module ):
	def __init__(							self     ,  lowerCAmelCase__					)				-> Any:
							super().__init__()
							__magic_name__     :  Union[str, Any]  		=   torchvision.models.resnetaaa(pretrained=UpperCAmelCase__					)
							__magic_name__     :  Dict  		=   list(model.children()					)[:-2]
							__magic_name__     :  Optional[Any]  		=   nn.Sequential(*UpperCAmelCase__					)
							__magic_name__     :  Tuple  		=   nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)







	def      __magic_name__							(							self     ,  lowerCAmelCase__					)				-> int:
							__magic_name__     :  Any  		=   self.pool(self.model(UpperCAmelCase__					)					)
							__magic_name__     :  Optional[int]  		=   torch.flatten(UpperCAmelCase__     ,  start_dim=2					)
							__magic_name__     :  Union[str, Any]  		=   out.transpose(1     ,  2					).contiguous()
							return out  # BxNx2048





class 			snake_case__   (				UpperCAmelCase__ ):
	def __init__(							self     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__					)				-> Tuple:
							__magic_name__     :  Tuple  		=   [json.loads(UpperCAmelCase__					) for l in open(UpperCAmelCase__					)]
							__magic_name__     :  Optional[int]  		=   os.path.dirname(UpperCAmelCase__					)
							__magic_name__     :  Tuple  		=   tokenizer
							__magic_name__     :  int  		=   labels
							__magic_name__     :  List[Any]  		=   len(UpperCAmelCase__					)
							__magic_name__     :  Tuple  		=   max_seq_length

							__magic_name__     :  Optional[Any]  		=   transforms
	def __len__(							self					)				-> Any:
							return len(self.data					)
	def __getitem__(							self     ,  lowerCAmelCase__					)				-> str:
							__magic_name__     :  Union[str, Any]  		=   torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]     ,  add_special_tokens=UpperCAmelCase__					)					)
							__magic_name__		,__magic_name__		,__magic_name__     :  Dict  		=   sentence[0], sentence[1:-1], sentence[-1]
							__magic_name__     :  Optional[Any]  		=   sentence[: self.max_seq_length]

							__magic_name__     :  Dict  		=   torch.zeros(self.n_classes					)
							__magic_name__     :  List[Any]  		=   1

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

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







	def      __magic_name__							(							self					)				-> List[str]:
							__magic_name__     :  Tuple  		=   Counter()
							for row in self.data:
													label_freqs.update(row["""label"""]					)
							return label_freqs



def        UpperCamelCase		(			_A					):






						"""simple docstring"""







						__magic_name__     :  List[str]  		=   [len(row["""sentence"""]					) for row in batch]
						__magic_name__		,__magic_name__     :  List[Any]  		=   len(lowercase_					), max(lowercase_					)

						__magic_name__     :  int  		=   torch.zeros(lowercase_,       lowercase_,       dtype=torch.long					)
						__magic_name__     :  Dict  		=   torch.zeros(lowercase_,       lowercase_,       dtype=torch.long					)

						for i_batch, (input_row, length) in enumerate(zip(lowercase_,       lowercase_					)					):
												__magic_name__     :  Optional[int]  		=   input_row["""sentence"""]
												__magic_name__     :  List[Any]  		=   1

						__magic_name__     :  Union[str, Any]  		=   torch.stack([row["""image"""] for row in batch]					)
						__magic_name__     :  List[str]  		=   torch.stack([row["""label"""] for row in batch]					)
						__magic_name__     :  List[str]  		=   torch.stack([row["""image_start_token"""] for row in batch]					)
						__magic_name__     :  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        UpperCamelCase		(			):






						"""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        UpperCamelCase		(			):






						"""simple docstring"""







						return transforms.Compose(
						    [
						        transforms.Resize(256					),
						        transforms.CenterCrop(224					),
						        transforms.ToTensor(),
						        transforms.Normalize(
						            mean=[0.46777044, 0.44531429, 0.40661017],       std=[0.12221994, 0.12145835, 0.14380469],       ),
						    ]					)






from math import atan, cos, radians, sin, tan

from .haversine_distance import haversine_distance

_lowerCamelCase      :						Dict							=  6_378_137.0
_lowerCamelCase      :						Union[str, Any]							=  6_356_752.314_245
_lowerCamelCase      :						List[Any]							=  6378137







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			float:



       """simple docstring"""

       A__ 					=       (AXIS_A - AXIS_B) / AXIS_A
       # Parametric latitudes
       # https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
       A__ 					=       atan((1 - flattening) * tan(radians(lowercase_		)		)		)
       A__ 					=       atan((1 - flattening) * tan(radians(lowercase_		)		)		)

       # Compute central angle between two points
       # using haversine theta. sigma =  haversine_distance / equatorial radius
       A__ 					=       haversine_distance(lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		) / EQUATORIAL_RADIUS

       # Intermediate P and Q values
       A__ 					=       (b_lata + b_lata) / 2
       A__ 					=       (b_lata - b_lata) / 2

       # Intermediate X value
       # X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
       A__ 					=       (sin(lowercase_		) ** 2) * (cos(lowercase_		) ** 2)
       A__ 					=       cos(sigma / 2		) ** 2
       A__ 					=       (sigma - sin(lowercase_		)) * (x_numerator / x_demonimator)

       # Intermediate Y value
       # Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
       A__ 					=       (cos(lowercase_		) ** 2) * (sin(lowercase_		) ** 2)
       A__ 					=       sin(sigma / 2		) ** 2
       A__ 					=       (sigma + sin(lowercase_		)) * (y_numerator / y_denominator)

       return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))


if __name__ == "__main__":
  import doctest

  doctest.testmod()


