'''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	: Tuple    		=  {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       lowercase__		(     nn.Module):


						def __init__( self			:       Tuple					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												super().__init__()
												SCREAMING_SNAKE_CASE							:					Dict           =							torchvision.models.resnetaaa(pretrained=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							list(model.children()		)[:-2]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							nn.Sequential(*UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]		)



						def  __A ( self			:       str					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.pool(self.model(UpperCamelCase__		)		)
												SCREAMING_SNAKE_CASE							:					Dict           =							torch.flatten(UpperCamelCase__					,	start_dim=2		)
												SCREAMING_SNAKE_CASE							:					str           =							out.transpose(1					,	2		).contiguous()
												return out  # BxNx2048






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Tuple					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							[json.loads(UpperCamelCase__		) for l in open(UpperCamelCase__		)]
												SCREAMING_SNAKE_CASE							:					Dict           =							os.path.dirname(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							tokenizer
												SCREAMING_SNAKE_CASE							:					Tuple           =							labels
												SCREAMING_SNAKE_CASE							:					Tuple           =							len(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							max_seq_length

												SCREAMING_SNAKE_CASE							:					int           =							transforms


						def __len__( self			:       Optional[int]		):





												'''simple docstring'''





												return len(self.data		)


						def __getitem__( self			:       str					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']					,	add_special_tokens=UpperCamelCase__		)		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							sentence[0], sentence[1:-1], sentence[-1]
												SCREAMING_SNAKE_CASE							:					List[str]           =							sentence[: self.max_seq_length]

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.zeros(self.n_classes		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							1

												SCREAMING_SNAKE_CASE							:					Dict           =							Image.open(os.path.join(self.data_dir					,	self.data[index]['''img''']		)		).convert('''RGB'''		)
												SCREAMING_SNAKE_CASE							:					Any           =							self.transforms(UpperCamelCase__		)

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



						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							Counter()
												for row in self.data:
																		label_freqs.update(row['''label''']		)
												return label_freqs






def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[len(row['''sentence''']		) for row in batch]
						SCREAMING_SNAKE_CASE							:					List[str]           =							len(_lowercase		), max(_lowercase		)

						SCREAMING_SNAKE_CASE							:					List[str]           =							torch.zeros(_lowercase	,						_lowercase	,						dtype=torch.long		)
						SCREAMING_SNAKE_CASE							:					int           =							torch.zeros(_lowercase	,						_lowercase	,						dtype=torch.long		)

						for i_batch, (input_row, length) in enumerate(zip(_lowercase	,						_lowercase		)		):
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_row['''sentence''']
												SCREAMING_SNAKE_CASE							:					str           =							1

						SCREAMING_SNAKE_CASE							:					Dict           =							torch.stack([row['''image'''] for row in batch]		)
						SCREAMING_SNAKE_CASE							:					int           =							torch.stack([row['''label'''] for row in batch]		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.stack([row['''image_start_token'''] for row in batch]		)
						SCREAMING_SNAKE_CASE							:					Dict           =							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(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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase	: Tuple    		=  {
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Dict    		=  [
					    'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CTRLForSequenceClassification',
					    'CTRLLMHeadModel',
					    'CTRLModel',
					    'CTRLPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Any    		=  [
					    'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCTRLForSequenceClassification',
					    'TFCTRLLMHeadModel',
					    'TFCTRLModel',
					    'TFCTRLPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
					from .tokenization_ctrl import CTRLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ctrl import (
										    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CTRLForSequenceClassification,
										    CTRLLMHeadModel,
										    CTRLModel,
										    CTRLPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ctrl import (
										    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCTRLForSequenceClassification,
										    TFCTRLLMHeadModel,
										    TFCTRLModel,
										    TFCTRLPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase	: Optional[Any]    		=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





