




'''simple docstring'''



import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version




def   lowercase__(			*__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Optional[Union[Dict, Any]] = None      ,__UpperCamelCase:					Dict=True      ,__UpperCamelCase:					List[Any]=2    ):

      """simple docstring"""






      from .. import __version__

      SCREAMING_SNAKE_CASE   :      int    					=						take_from
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						()
      if not isinstance(args[0]      ,__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      List[str]    					=						(args,)

      for attribute, version_name, message in args:
            if version.parse(version.parse(__UpperCamelCase    ).base_version    ) >= version.parse(__UpperCamelCase    ):
                  raise ValueError(
                      f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
                      f" version {__version__} is >= {version_name}"    )

            SCREAMING_SNAKE_CASE   :      Tuple    					=						None
            if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) and attribute in deprecated_kwargs:
                  values += (deprecated_kwargs.pop(__UpperCamelCase    ),)
                  SCREAMING_SNAKE_CASE   :      Dict    					=						f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
            elif hasattr(__UpperCamelCase      ,__UpperCamelCase    ):
                  values += (getattr(__UpperCamelCase      ,__UpperCamelCase    ),)
                  SCREAMING_SNAKE_CASE   :      Optional[int]    					=						f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
            elif deprecated_kwargs is None:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						f"`{attribute}` is deprecated and will be removed in version {version_name}."

            if warning is not None:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						warning + ' ' if standard_warn else ''
                  warnings.warn(warning + message      ,__UpperCamelCase      ,stacklevel=__UpperCamelCase    )

      if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) and len(__UpperCamelCase    ) > 0:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						inspect.getouterframes(inspect.currentframe()    )[1]
            SCREAMING_SNAKE_CASE   :      Any    					=						call_frame.filename
            SCREAMING_SNAKE_CASE   :      Tuple    					=						call_frame.lineno
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						call_frame.function
            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Tuple    					=						next(iter(deprecated_kwargs.items()    )    )
            raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"    )

      if len(__UpperCamelCase    ) == 0:
            return
      elif len(__UpperCamelCase    ) == 1:
            return values[0]
      return values









'''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      UpperCamelCase__					(					nn.Module			):




					"""simple docstring"""


					def __init__(						self				:			Dict      ,						lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



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



					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			self.pool(self.model(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				int		=			torch.flatten(lowerCamelCase_      ,						start_dim=2						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			out.transpose(1      ,						2						).contiguous()
							return out  # BxNx2048







class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE :				List[Any]		=			transforms


					def __len__(						self				:			Any						):

							'''simple docstring'''



							return len(self.data						)


					def __getitem__(						self				:			Optional[int]      ,						lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]      ,						add_special_tokens=lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Tuple		=			sentence[0], sentence[1:-1], sentence[-1]
							SCREAMING_SNAKE_CASE :				int		=			sentence[: self.max_seq_length]

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

							SCREAMING_SNAKE_CASE :				int		=			Image.open(os.path.join(self.data_dir      ,						self.data[index]["""img"""]						)						).convert("""RGB"""						)
							SCREAMING_SNAKE_CASE :				str		=			self.transforms(lowerCamelCase_						)

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



					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



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







def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Tuple		=			[len(row["""sentence"""]	) for row in batch]
		SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Any		=			len(lowerCamelCase_	), max(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				int		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)
		SCREAMING_SNAKE_CASE :				Any		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)

		for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase_			,			lowerCamelCase_	)	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			input_row["""sentence"""]
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			1

		SCREAMING_SNAKE_CASE :				List[str]		=			torch.stack([row["""image"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				int		=			torch.stack([row["""label"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				str		=			torch.stack([row["""image_start_token"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				Any		=			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		(  ):


		"""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 		__A		(  ):


		"""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]			,			),
		    ]	)



