


'''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	={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 		_snake_case					(     nn.Module	):




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




					def 				snake_case__						(      self							,							_lowerCamelCase):
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase__  :					List[Any]        =     self.pool(self.model(_lowerCamelCase))
								UpperCAmelCase__  :					Tuple        =     torch.flatten(_lowerCamelCase							,							start_dim=2)
								UpperCAmelCase__  :					Any        =     out.transpose(1							,							2).contiguous()
								return out  # BxNx2048


class 		_snake_case					(     a__	):




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

								UpperCAmelCase__  :					List[str]        =     transforms




					def __len__(      self):
								return len(self.data)




					def __getitem__(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]							,							add_special_tokens=_lowerCamelCase))
								UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__  :					Union[str, Any]        =     sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase__  :					Optional[Any]        =     sentence[: self.max_seq_length]

								UpperCAmelCase__  :					List[str]        =     torch.zeros(self.n_classes)
								UpperCAmelCase__  :					str        =     1

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

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




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[int]        =     Counter()
								for row in self.data:
											label_freqs.update(row["""label"""])
								return label_freqs







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     [len(row["""sentence"""]					) for row in batch]
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     len(UpperCamelCase__					), max(UpperCamelCase__					)

			UpperCAmelCase__  :					Dict        =     torch.zeros(UpperCamelCase__ ,      UpperCamelCase__ ,      dtype=torch.long					)
			UpperCAmelCase__  :					Tuple        =     torch.zeros(UpperCamelCase__ ,      UpperCamelCase__ ,      dtype=torch.long					)

			for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__ ,      UpperCamelCase__					)					):
						UpperCAmelCase__  :					Optional[int]        =     input_row["""sentence"""]
						UpperCAmelCase__  :					Any        =     1

			UpperCAmelCase__  :					str        =     torch.stack([row["""image"""] for row in batch]					)
			UpperCAmelCase__  :					str        =     torch.stack([row["""label"""] for row in batch]					)
			UpperCAmelCase__  :					int        =     torch.stack([row["""image_start_token"""] for row in batch]					)
			UpperCAmelCase__  :					List[str]        =     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      (		):
			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      (		):
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6					),
			        transforms.CenterCrop(2_2_4					),
			        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] ,      ),
			    ]					)



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A	={
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'EncodecModel',
		    'EncodecPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_encodec import (
		    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    EncodecConfig,
		)
		from .feature_extraction_encodec import EncodecFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_encodec import (
				    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EncodecModel,
				    EncodecPreTrainedModel,
				)

else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)