


"""simple docstring"""






import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


UpperCAmelCase    :     Optional[int]   						=		get_logger(__name__)





class      lowerCamelCase__   (     enum.Enum				):







					"""simple docstring"""


					__a	       =							"""all_checks"""
					__a	       =							"""basic_checks"""
					__a	       =							"""no_checks"""






class      lowerCamelCase__   (     A				):







					"""simple docstring"""








class      lowerCamelCase__   (     A				):







					"""simple docstring"""








class      lowerCamelCase__   (     A				):







					"""simple docstring"""








class      lowerCamelCase__   (     A				):







					"""simple docstring"""








def      lowerCamelCase       (     _UpperCamelCase		:      Optional[dict]       ,			_UpperCamelCase		:      dict       ,			_UpperCamelCase		:      str=None							)		->       Tuple:

							'''simple docstring'''



							if expected_checksums is None:
														logger.info("""Unable to verify checksums."""							)
														return
							if len(set(_UpperCamelCase							) - set(_UpperCamelCase							)							) > 0:
														raise ExpectedMoreDownloadedFiles(str(set(_UpperCamelCase							) - set(_UpperCamelCase							)							)							)
							if len(set(_UpperCamelCase							) - set(_UpperCamelCase							)							) > 0:
														raise UnexpectedDownloadedFile(str(set(_UpperCamelCase							) - set(_UpperCamelCase							)							)							)
							__UpperCAmelCase   :						Dict      							=					[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
							__UpperCAmelCase   :						str      							=					""" for """ + verification_name if verification_name is not None else """"""
							if len(_UpperCamelCase							) > 0:
														raise NonMatchingChecksumError(
														    f'''Checksums didn\'t match{for_verification_name}:\n'''
														    f'''{bad_urls}\n'''
														    """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""							)
							logger.info("""All the checksums matched successfully""" + for_verification_name							)





class      lowerCamelCase__   (     A				):







					"""simple docstring"""








class      lowerCamelCase__   (     A				):







					"""simple docstring"""








class      lowerCamelCase__   (     A				):







					"""simple docstring"""








class      lowerCamelCase__   (     A				):







					"""simple docstring"""








def      lowerCamelCase       (     _UpperCamelCase		:      Optional[dict]       ,			_UpperCamelCase		:      dict							)		->       Dict:

							'''simple docstring'''



							if expected_splits is None:
														logger.info("""Unable to verify splits sizes."""							)
														return
							if len(set(_UpperCamelCase							) - set(_UpperCamelCase							)							) > 0:
														raise ExpectedMoreSplits(str(set(_UpperCamelCase							) - set(_UpperCamelCase							)							)							)
							if len(set(_UpperCamelCase							) - set(_UpperCamelCase							)							) > 0:
														raise UnexpectedSplits(str(set(_UpperCamelCase							) - set(_UpperCamelCase							)							)							)
							__UpperCAmelCase   :						List[Any]      							=					[
							    {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
							    for name in expected_splits
							    if expected_splits[name].num_examples != recorded_splits[name].num_examples
							]
							if len(_UpperCamelCase							) > 0:
														raise NonMatchingSplitsSizesError(str(_UpperCamelCase							)							)
							logger.info("""All the splits matched successfully."""							)
def      lowerCamelCase       (     _UpperCamelCase		:      str       ,			_UpperCamelCase		:      bool = True							)		->       dict:

							'''simple docstring'''



							if record_checksum:
														__UpperCAmelCase   :						Any      							=					shaaaa()
														with open(_UpperCamelCase       ,			"""rb"""							) as f:
																					for chunk in iter(lambda: f.read(1 << 2_0							)       ,			b""""""							):
																												m.update(_UpperCamelCase							)
																					__UpperCAmelCase   :						List[str]      							=					m.hexdigest()
							else:
														__UpperCAmelCase   :						Tuple      							=					None
							return {"num_bytes": os.path.getsize(_UpperCamelCase							), "checksum": checksum}
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]							)		->       Dict:

							'''simple docstring'''



							if dataset_size and config.IN_MEMORY_MAX_SIZE:
														return dataset_size < config.IN_MEMORY_MAX_SIZE
							else:
														return False




"""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    :     str   						=		{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      lowerCamelCase__   (     nn.Module				):







					"""simple docstring"""


					def __init__(       self    : Union[str, Any] ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												super().__init__()
												__UpperCAmelCase   :						Union[str, Any]      							=					torchvision.models.resnetaaa(pretrained=UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					list(model.children()  )[:-2]
												__UpperCAmelCase   :						Tuple      							=					nn.Sequential(*UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






					def lowerCamelCase__							(       self    : List[Any] ,       UpperCamelCase    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					self.pool(self.model(UpperCamelCase  )  )
												__UpperCAmelCase   :						str      							=					torch.flatten(UpperCamelCase ,       start_dim=2  )
												__UpperCAmelCase   :						Union[str, Any]      							=					out.transpose(1 ,       2  ).contiguous()
												return out  # BxNx2048






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : str ,       UpperCamelCase    : str ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : Any ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					[json.loads(UpperCamelCase  ) for l in open(UpperCamelCase  )]
												__UpperCAmelCase   :						Optional[Any]      							=					os.path.dirname(UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					tokenizer
												__UpperCAmelCase   :						List[str]      							=					labels
												__UpperCAmelCase   :						Optional[int]      							=					len(UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					max_seq_length

												__UpperCAmelCase   :						Tuple      							=					transforms






					def __len__(       self    : Tuple  ):
												'''simple docstring'''






												return len(self.data  )






					def __getitem__(       self    : int ,       UpperCamelCase    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,       add_special_tokens=UpperCamelCase  )  )
												__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Dict      							=					sentence[0], sentence[1:-1], sentence[-1]
												__UpperCAmelCase   :						Union[str, Any]      							=					sentence[: self.max_seq_length]

												__UpperCAmelCase   :						Tuple      							=					torch.zeros(self.n_classes  )
												__UpperCAmelCase   :						str      							=					1

												__UpperCAmelCase   :						List[str]      							=					Image.open(os.path.join(self.data_dir ,       self.data[index]["""img"""]  )  ).convert("""RGB"""  )
												__UpperCAmelCase   :						Any      							=					self.transforms(UpperCamelCase  )

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






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






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






def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       Any:

							'''simple docstring'''



							__UpperCAmelCase   :						Union[str, Any]      							=					[len(row["""sentence"""]							) for row in batch]
							__UpperCAmelCase							,__UpperCAmelCase   :						Union[str, Any]      							=					len(_UpperCamelCase							), max(_UpperCamelCase							)

							__UpperCAmelCase   :						int      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)
							__UpperCAmelCase   :						Any      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)

							for i_batch, (input_row, length) in enumerate(zip(_UpperCamelCase       ,			_UpperCamelCase							)							):
														__UpperCAmelCase   :						List[str]      							=					input_row["""sentence"""]
														__UpperCAmelCase   :						Optional[Any]      							=					1

							__UpperCAmelCase   :						int      							=					torch.stack([row["""image"""] for row in batch]							)
							__UpperCAmelCase   :						Optional[int]      							=					torch.stack([row["""label"""] for row in batch]							)
							__UpperCAmelCase   :						int      							=					torch.stack([row["""image_start_token"""] for row in batch]							)
							__UpperCAmelCase   :						int      							=					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       (     )		->       List[str]:

							'''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       (     )		->       Dict:

							'''simple docstring'''



							return transforms.Compose(
							    [
							        transforms.Resize(2_5_6							),
							        transforms.CenterCrop(2_2_4							),
							        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]       ,			),
							    ]							)
