




'''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__					    =    {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     a__					(      nn.Module   ):






						def __init__(     self	,    _A    ):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase            = torchvision.models.resnetaaa(pretrained=__A    )
											__lowerCAmelCase            = list(model.children()    )[:-2]
											__lowerCAmelCase            = nn.Sequential(*__A    )
											__lowerCAmelCase            = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.pool(self.model(__A    )    )
											__lowerCAmelCase            = torch.flatten(__A	,    start_dim=2    )
											__lowerCAmelCase            = out.transpose(1	,    2    ).contiguous()
											return out  # BxNx2048






class     a__					(      __lowercase   ):






						def __init__(     self	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = [json.loads(__A    ) for l in open(__A    )]
											__lowerCAmelCase            = os.path.dirname(__A    )
											__lowerCAmelCase            = tokenizer
											__lowerCAmelCase            = labels
											__lowerCAmelCase            = len(__A    )
											__lowerCAmelCase            = max_seq_length

											__lowerCAmelCase            = transforms






						def __len__(     self    ):




											"""simple docstring"""



											return len(self.data    )






						def __getitem__(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]	,    add_special_tokens=__A    )    )
											__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(__A    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Dict  ):
					__lowerCAmelCase            = [len(row["sentence"]  ) for row in batch]
					__lowerCAmelCase     ,							__lowerCAmelCase            = len(a__  ), max(a__  )

					__lowerCAmelCase            = torch.zeros(a__   ,   a__   ,   dtype=torch.long  )
					__lowerCAmelCase            = torch.zeros(a__   ,   a__   ,   dtype=torch.long  )

					for i_batch, (input_row, length) in enumerate(zip(a__   ,   a__  )  ):
										__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 			_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_56  ),
					        transforms.CenterCrop(2_24  ),
					        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]   ,   ),
					    ]  )










import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     a__					(      snake_case__   ):
						_a							:					Optional[Any]      =					"""M-CLIP"""






						def __init__(     self	,    _A=1_0_2_4	,    _A=7_6_8	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = transformerDimSize
											__lowerCAmelCase            = imageDimSize
											super().__init__(**_A    )






class     a__					(      snake_case__   ):
						_a							:					Tuple      =					MCLIPConfig






						def __init__(     self	,    _A	,    *_A	,    **_A    ):




											"""simple docstring"""



											super().__init__(_A	,    *_A	,    **_A    )
											__lowerCAmelCase            = XLMRobertaModel(_A    )
											__lowerCAmelCase            = torch.nn.Linear(
											    in_features=config.transformerDimensions	,    out_features=config.numDims    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.transformer(input_ids=_A	,    attention_mask=_A    )[0]
											__lowerCAmelCase            = (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
											return self.LinearTransformation(_A    ), embs




