






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


__snake_case      :      Optional[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       __SCREAMING_SNAKE_CASE   (				nn.Module):






						def __init__(		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												super().__init__()
												lowerCAmelCase__        =  torchvision.models.resnetaaa(pretrained=_UpperCamelCase				)
												lowerCAmelCase__        =  list(model.children()				)[:-2]
												lowerCAmelCase__        =  nn.Sequential(*_UpperCamelCase				)
												lowerCAmelCase__        =  nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
												lowerCAmelCase__        =  self.pool(self.model(_UpperCamelCase				)				)
												lowerCAmelCase__        =  torch.flatten(_UpperCamelCase       ,  start_dim=2				)
												lowerCAmelCase__        =  out.transpose(1       ,  2				).contiguous()
												return out  # BxNx2048




class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  [json.loads(_UpperCamelCase				) for l in open(_UpperCamelCase				)]
												lowerCAmelCase__        =  os.path.dirname(_UpperCamelCase				)
												lowerCAmelCase__        =  tokenizer
												lowerCAmelCase__        =  labels
												lowerCAmelCase__        =  len(_UpperCamelCase				)
												lowerCAmelCase__        =  max_seq_length

												lowerCAmelCase__        =  transforms






						def __len__(		self				):






												"""simple docstring"""
												return len(self.data				)






						def __getitem__(		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,  add_special_tokens=_UpperCamelCase				)				)
												lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__        =  sentence[0], sentence[1:-1], sentence[-1]
												lowerCAmelCase__        =  sentence[: self.max_seq_length]

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

												lowerCAmelCase__        =  Image.open(os.path.join(self.data_dir       ,  self.data[index]['img']				)				).convert('RGB'				)
												lowerCAmelCase__        =  self.transforms(_UpperCamelCase				)

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







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  Counter()
												for row in self.data:
																		label_freqs.update(row['label']				)
												return label_freqs







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]						)					->				Tuple:

						"""simple docstring"""



						lowerCAmelCase__        =  [len(row['sentence']						) for row in batch]
						lowerCAmelCase__  ,    lowerCAmelCase__        =  len(UpperCamelCase_						), max(UpperCamelCase_						)

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

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

						lowerCAmelCase__        =  torch.stack([row['image'] for row in batch]						)
						lowerCAmelCase__        =  torch.stack([row['label'] for row in batch]						)
						lowerCAmelCase__        =  torch.stack([row['image_start_token'] for row in batch]						)
						lowerCAmelCase__        =  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				(      )					->				Optional[int]:

						"""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				(      )					->				Optional[int]:

						"""simple docstring"""



						return transforms.Compose(
						    [
						        transforms.Resize(256						),
						        transforms.CenterCrop(224						),
						        transforms.ToTensor(),
						        transforms.Normalize(
						            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]      ,    std=[0.1222_1994, 0.1214_5835, 0.1438_0469]      ,    ),
						    ]						)













from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case      :      Dict             =       {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      str             =       [
					    """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FocalNetForImageClassification""",
					    """FocalNetForMaskedImageModeling""",
					    """FocalNetBackbone""",
					    """FocalNetModel""",
					    """FocalNetPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_focalnet import (
										    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FocalNetBackbone,
										    FocalNetForImageClassification,
										    FocalNetForMaskedImageModeling,
										    FocalNetModel,
										    FocalNetPreTrainedModel,
										)

else:
					import sys

					__snake_case      :      List[Any]             =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





