


"""simple docstring"""






from datetime import datetime

import matplotlib.pyplot as plt
import torch
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       Union[str, Any]:

							'''simple docstring'''



							for param in module.parameters():
														__UpperCAmelCase   :						int      							=					False
def      lowerCamelCase       (     )		->       Union[str, Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						int      							=					"""cuda""" if torch.cuda.is_available() else """cpu"""
							if torch.backends.mps.is_available() and torch.backends.mps.is_built():
														__UpperCAmelCase   :						Any      							=					"""mps"""
							if device == "mps":
														print(
														    """WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
														    """ errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
														    """ with generations."""							)
							return device
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       List[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Tuple      							=					plt.imshow(_UpperCamelCase							)
							fig.axes.get_xaxis().set_visible(_UpperCamelCase							)
							fig.axes.get_yaxis().set_visible(_UpperCamelCase							)
							plt.show()
def      lowerCamelCase       (     )		->       List[str]:

							'''simple docstring'''



							__UpperCAmelCase   :						Any      							=					datetime.now()
							__UpperCAmelCase   :						int      							=					current_time.strftime("""%H:%M:%S"""							)
							return timestamp




"""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


UpperCAmelCase    :     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      lowerCamelCase__   (     nn.Module				):







					"""simple docstring"""


					def __init__(       self    : Union[str, Any] ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												super().__init__()
												__UpperCAmelCase   :						Union[str, Any]      							=					torchvision.models.resnetaaa(pretrained=UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					list(model.children()  )[:-2]
												__UpperCAmelCase   :						Tuple      							=					nn.Sequential(*UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






					def lowerCamelCase__							(       self    : List[Any] ,       UpperCamelCase    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					self.pool(self.model(UpperCamelCase  )  )
												__UpperCAmelCase   :						str      							=					torch.flatten(UpperCamelCase ,       start_dim=2  )
												__UpperCAmelCase   :						Union[str, Any]      							=					out.transpose(1 ,       2  ).contiguous()
												return out  # BxNx2048






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : str ,       UpperCamelCase    : str ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : Any ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					[json.loads(UpperCamelCase  ) for l in open(UpperCamelCase  )]
												__UpperCAmelCase   :						Optional[Any]      							=					os.path.dirname(UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					tokenizer
												__UpperCAmelCase   :						List[str]      							=					labels
												__UpperCAmelCase   :						Optional[int]      							=					len(UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					max_seq_length

												__UpperCAmelCase   :						Tuple      							=					transforms






					def __len__(       self    : Tuple  ):
												'''simple docstring'''






												return len(self.data  )






					def __getitem__(       self    : int ,       UpperCamelCase    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,       add_special_tokens=UpperCamelCase  )  )
												__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Dict      							=					sentence[0], sentence[1:-1], sentence[-1]
												__UpperCAmelCase   :						Union[str, Any]      							=					sentence[: self.max_seq_length]

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

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

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






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






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






def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       Any:

							'''simple docstring'''



							__UpperCAmelCase   :						Union[str, Any]      							=					[len(row["""sentence"""]							) for row in batch]
							__UpperCAmelCase							,__UpperCAmelCase   :						Union[str, Any]      							=					len(_UpperCamelCase							), max(_UpperCamelCase							)

							__UpperCAmelCase   :						int      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)
							__UpperCAmelCase   :						Any      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)

							for i_batch, (input_row, length) in enumerate(zip(_UpperCamelCase       ,			_UpperCamelCase							)							):
														__UpperCAmelCase   :						List[str]      							=					input_row["""sentence"""]
														__UpperCAmelCase   :						Optional[Any]      							=					1

							__UpperCAmelCase   :						int      							=					torch.stack([row["""image"""] for row in batch]							)
							__UpperCAmelCase   :						Optional[int]      							=					torch.stack([row["""label"""] for row in batch]							)
							__UpperCAmelCase   :						int      							=					torch.stack([row["""image_start_token"""] for row in batch]							)
							__UpperCAmelCase   :						int      							=					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       (     )		->       List[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      lowerCamelCase       (     )		->       Dict:

							'''simple docstring'''



							return transforms.Compose(
							    [
							        transforms.Resize(2_5_6							),
							        transforms.CenterCrop(2_2_4							),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]       ,			std=[0.12_221_994, 0.12_145_835, 0.14_380_469]       ,			),
							    ]							)
