


'''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


_lowercase      :      Optional[Any]            =					{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	):




				def __init__(		self      :			Optional[Any]				,   lowerCAmelCase      :			Any				)->					Dict:

										"""simple docstring"""




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





				def 					a__(		self      :			Any				,   lowerCAmelCase      :			Union[str, Any]				)->					Union[str, Any]:

										"""simple docstring"""




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







class 	UpperCamelCase__(       lowerCAmelCase	):




				def __init__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Optional[Any]				)->					Optional[Any]:

										"""simple docstring"""




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

										UpperCAmelCase					    =     transforms




				def __len__(		self      :			str				)->					Union[str, Any]:

										"""simple docstring"""




										return len(self.data				)




				def __getitem__(		self      :			Any				,   lowerCAmelCase      :			Any				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				,   add_special_tokens=lowerCAmelCase				)				)
										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     sentence[0], sentence[1:-1], sentence[-1]
										UpperCAmelCase					    =     sentence[: self.max_seq_length]

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

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

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





				def 					a__(		self      :			Optional[int]				)->					Optional[Any]:

										"""simple docstring"""




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







def 				lowerCamelCase__				(	A       :      Optional[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     [len(row['''sentence''']					) for row in batch]
						UpperCAmelCase    ,    UpperCAmelCase					    =     len(A					), max(A					)

						UpperCAmelCase					    =     torch.zeros(A				,       A				,       dtype=torch.long					)
						UpperCAmelCase					    =     torch.zeros(A				,       A				,       dtype=torch.long					)

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

						UpperCAmelCase					    =     torch.stack([row['''image'''] for row in batch]					)
						UpperCAmelCase					    =     torch.stack([row['''label'''] for row in batch]					)
						UpperCAmelCase					    =     torch.stack([row['''image_start_token'''] for row in batch]					)
						UpperCAmelCase					    =     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__				(	):




						'''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__				(	):




						'''simple docstring'''






						return transforms.Compose(
						    [
						        transforms.Resize(2_56					),
						        transforms.CenterCrop(2_24					),
						        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]				,       ),
						    ]					)









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      List[str]            =					{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Dict            =					[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	_lowercase      :      Tuple            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





