


"""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


a__    :							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 			UpperCamelCase_     (			nn.Module):

	"""simple docstring"""





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




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




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





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

						__SCREAMING_SNAKE_CASE						       =    transforms




	def __len__(			self      :    List[Any]       )	->     Dict:
						return len(self.data       )




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

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

						__SCREAMING_SNAKE_CASE						       =    Image.open(os.path.join(self.data_dir      ,      self.data[index]["img"]       )       ).convert("RGB"       )
						__SCREAMING_SNAKE_CASE						       =    self.transforms(UpperCAmelCase__       )

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




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    Counter()
						for row in self.data:
											label_freqs.update(row["label"]       )
						return label_freqs




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    [len(row["sentence"]       ) for row in batch]
					__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    len(lowerCAmelCase_       ), max(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    torch.zeros(lowerCAmelCase_  ,						lowerCAmelCase_  ,						dtype=torch.long       )
					__SCREAMING_SNAKE_CASE						       =    torch.zeros(lowerCAmelCase_  ,						lowerCAmelCase_  ,						dtype=torch.long       )

					for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase_  ,						lowerCAmelCase_       )       ):
										__SCREAMING_SNAKE_CASE						       =    input_row["sentence"]
										__SCREAMING_SNAKE_CASE						       =    1

					__SCREAMING_SNAKE_CASE						       =    torch.stack([row["image"] for row in batch]       )
					__SCREAMING_SNAKE_CASE						       =    torch.stack([row["label"] for row in batch]       )
					__SCREAMING_SNAKE_CASE						       =    torch.stack([row["image_start_token"] for row in batch]       )
					__SCREAMING_SNAKE_CASE						       =    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]  ,						),
					    ]       )




"""simple docstring"""







from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__    :							Tuple            =					logging.get_logger(__name__)

a__    :							List[Any]            =					{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Any    =      "efficientformer"




	def __init__(			self      :    Any      ,      UpperCAmelCase__      :    List[int] = [3, 2, 6, 4]      ,      UpperCAmelCase__      :    List[int] = [4_8, 9_6, 2_2_4, 4_4_8]      ,      UpperCAmelCase__      :    List[bool] = [True, True, True, True]      ,      UpperCAmelCase__      :    int = 4_4_8      ,      UpperCAmelCase__      :    int = 3_2      ,      UpperCAmelCase__      :    int = 4      ,      UpperCAmelCase__      :    int = 7      ,      UpperCAmelCase__      :    int = 5      ,      UpperCAmelCase__      :    int = 8      ,      UpperCAmelCase__      :    int = 4      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 1_6      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 2      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    float = 1E-5      ,      UpperCAmelCase__      :    str = "gelu"      ,      UpperCAmelCase__      :    float = 0.02      ,      UpperCAmelCase__      :    float = 1E-12      ,      UpperCAmelCase__      :    int = 2_2_4      ,      UpperCAmelCase__      :    float = 1E-05      ,      **UpperCAmelCase__      :    Tuple      ,      )	->     None:
						super().__init__(**UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    hidden_act
						__SCREAMING_SNAKE_CASE						       =    hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    hidden_sizes
						__SCREAMING_SNAKE_CASE						       =    num_hidden_layers
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    layer_norm_eps
						__SCREAMING_SNAKE_CASE						       =    patch_size
						__SCREAMING_SNAKE_CASE						       =    num_channels
						__SCREAMING_SNAKE_CASE						       =    depths
						__SCREAMING_SNAKE_CASE						       =    mlp_expansion_ratio
						__SCREAMING_SNAKE_CASE						       =    downsamples
						__SCREAMING_SNAKE_CASE						       =    dim
						__SCREAMING_SNAKE_CASE						       =    key_dim
						__SCREAMING_SNAKE_CASE						       =    attention_ratio
						__SCREAMING_SNAKE_CASE						       =    resolution
						__SCREAMING_SNAKE_CASE						       =    pool_size
						__SCREAMING_SNAKE_CASE						       =    downsample_patch_size
						__SCREAMING_SNAKE_CASE						       =    downsample_stride
						__SCREAMING_SNAKE_CASE						       =    downsample_pad
						__SCREAMING_SNAKE_CASE						       =    drop_path_rate
						__SCREAMING_SNAKE_CASE						       =    num_metaad_blocks
						__SCREAMING_SNAKE_CASE						       =    distillation
						__SCREAMING_SNAKE_CASE						       =    use_layer_scale
						__SCREAMING_SNAKE_CASE						       =    layer_scale_init_value
						__SCREAMING_SNAKE_CASE						       =    image_size
						__SCREAMING_SNAKE_CASE						       =    batch_norm_eps
