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







						def 					lowerCamelCase__   (				self					:	List[Any]							,    UpperCAmelCase					:	List[str]			):
													__lowerCamelCase    :						int						      =   self.pool(self.model(UpperCAmelCase			)			)
													__lowerCamelCase    :						str						      =   torch.flatten(UpperCAmelCase							,    start_dim=2			)
													__lowerCamelCase    :						Union[str, Any]						      =   out.transpose(1							,    2			).contiguous()
													return out  # BxNx2048

class        _snake_case   (    a__						):






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

													__lowerCamelCase    :						Tuple						      =   transforms






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






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

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

													__lowerCamelCase    :						List[str]						      =   Image.open(os.path.join(self.data_dir							,    self.data[index]["img"]			)			).convert("RGB"			)
													__lowerCamelCase    :						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			):
													__lowerCamelCase    :						int						      =   Counter()
													for row in self.data:
																				label_freqs.update(row["label"]			)
													return label_freqs


def       lowercase_      (      _lowerCamelCase:		Union[str, Any]					)       ->					Any:



							'''simple docstring'''

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

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

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

							__lowerCamelCase    :						int						      =   torch.stack([row["image"] for row in batch]					)
							__lowerCamelCase    :						Optional[int]						      =   torch.stack([row["label"] for row in batch]					)
							__lowerCamelCase    :						int						      =   torch.stack([row["image_start_token"] for row in batch]					)
							__lowerCamelCase    :						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       lowercase_      (      )       ->					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       lowercase_      (      )       ->					Dict:



							'''simple docstring'''

							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]  ,      ),
							    ]					)



"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


UpperCAmelCase    :     Optional[Any]   						=		logging.get_logger(__name__)
def      lowerCamelCase       (     _UpperCamelCase		:      Any       ,			_UpperCamelCase		:      Optional[Any]							)		->       int:

							'''simple docstring'''



							__UpperCAmelCase   :						int      							=					b.T
							__UpperCAmelCase   :						Union[str, Any]      							=					np.sum(np.square(_UpperCamelCase							)       ,			axis=1							)
							__UpperCAmelCase   :						List[Any]      							=					np.sum(np.square(_UpperCamelCase							)       ,			axis=0							)
							__UpperCAmelCase   :						int      							=					np.matmul(_UpperCamelCase       ,			_UpperCamelCase							)
							__UpperCAmelCase   :						Tuple      							=					aa[:, None] - 2 * ab + ba[None, :]
							return d
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple       ,			_UpperCamelCase		:      Optional[Any]							)		->       Optional[int]:

							'''simple docstring'''



							__UpperCAmelCase   :						Tuple      							=					x.reshape(-1       ,			3							)
							__UpperCAmelCase   :						Optional[Any]      							=					squared_euclidean_distance(_UpperCamelCase       ,			_UpperCamelCase							)
							return np.argmin(_UpperCamelCase       ,			axis=1							)





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							["""pixel_values"""]






					def __init__(       self    : List[str] ,       UpperCamelCase    : Optional[Union[List[List[int]], np.ndarray]] = None ,       UpperCamelCase    : bool = True ,       UpperCamelCase    : Dict[str, int] = None ,       UpperCamelCase    : PILImageResampling = PILImageResampling.BILINEAR ,       UpperCamelCase    : bool = True ,       UpperCamelCase    : bool = True ,       **UpperCamelCase    : int ,       ):
												'''simple docstring'''






												super().__init__(**UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					size if size is not None else {"""height""": 256, """width""": 256}
												__UpperCAmelCase   :						Tuple      							=					get_size_dict(UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					np.array(UpperCamelCase  ) if clusters is not None else None
												__UpperCAmelCase   :						int      							=					do_resize
												__UpperCAmelCase   :						List[str]      							=					size
												__UpperCAmelCase   :						Dict      							=					resample
												__UpperCAmelCase   :						int      							=					do_normalize
												__UpperCAmelCase   :						List[Any]      							=					do_color_quantize






					def lowerCamelCase__							(       self    : List[str] ,       UpperCamelCase    : np.ndarray ,       UpperCamelCase    : Dict[str, int] ,       UpperCamelCase    : PILImageResampling = PILImageResampling.BILINEAR ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = None ,       **UpperCamelCase    : str ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					get_size_dict(UpperCamelCase  )
												if "height" not in size or "width" not in size:
																			raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''  )
												return resize(
												    UpperCamelCase ,       size=(size["""height"""], size["""width"""]) ,       resample=UpperCamelCase ,       data_format=UpperCamelCase ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : np.ndarray ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = None ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					rescale(image=UpperCamelCase ,       scale=1 / 127.5 ,       data_format=UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					image - 1
												return image






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : ImageInput ,       UpperCamelCase    : bool = None ,       UpperCamelCase    : Dict[str, int] = None ,       UpperCamelCase    : PILImageResampling = None ,       UpperCamelCase    : bool = None ,       UpperCamelCase    : Optional[bool] = None ,       UpperCamelCase    : Optional[Union[List[List[int]], np.ndarray]] = None ,       UpperCamelCase    : Optional[Union[str, TensorType]] = None ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST ,       **UpperCamelCase    : Optional[int] ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					do_resize if do_resize is not None else self.do_resize
												__UpperCAmelCase   :						Any      							=					size if size is not None else self.size
												__UpperCAmelCase   :						Any      							=					get_size_dict(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					resample if resample is not None else self.resample
												__UpperCAmelCase   :						Any      							=					do_normalize if do_normalize is not None else self.do_normalize
												__UpperCAmelCase   :						Any      							=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
												__UpperCAmelCase   :						Tuple      							=					clusters if clusters is not None else self.clusters
												__UpperCAmelCase   :						Tuple      							=					np.array(UpperCamelCase  )

												__UpperCAmelCase   :						int      							=					make_list_of_images(UpperCamelCase  )

												if not valid_images(UpperCamelCase  ):
																			raise ValueError(
																			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																			    """torch.Tensor, tf.Tensor or jax.ndarray."""  )

												if do_resize and size is None or resample is None:
																			raise ValueError("""Size and resample must be specified if do_resize is True."""  )

												if do_color_quantize and clusters is None:
																			raise ValueError("""Clusters must be specified if do_color_quantize is True."""  )

												# All transformations expect numpy arrays.
												__UpperCAmelCase   :						Tuple      							=					[to_numpy_array(UpperCamelCase  ) for image in images]

												if do_resize:
																			__UpperCAmelCase   :						Dict      							=					[self.resize(image=UpperCamelCase ,       size=UpperCamelCase ,       resample=UpperCamelCase  ) for image in images]

												if do_normalize:
																			__UpperCAmelCase   :						Any      							=					[self.normalize(image=UpperCamelCase  ) for image in images]

												if do_color_quantize:
																			__UpperCAmelCase   :						str      							=					[to_channel_dimension_format(UpperCamelCase ,       ChannelDimension.LAST  ) for image in images]
																			# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																			__UpperCAmelCase   :						Optional[int]      							=					np.array(UpperCamelCase  )
																			__UpperCAmelCase   :						Any      							=					color_quantize(UpperCamelCase ,       UpperCamelCase  ).reshape(images.shape[:-1]  )

																			# flatten to (batch_size, height*width)
																			__UpperCAmelCase   :						Dict      							=					images.shape[0]
																			__UpperCAmelCase   :						Optional[Any]      							=					images.reshape(UpperCamelCase ,       -1  )

																			# We need to convert back to a list of images to keep consistent behaviour across processors.
																			__UpperCAmelCase   :						int      							=					list(UpperCamelCase  )
												else:
																			__UpperCAmelCase   :						List[Any]      							=					[to_channel_dimension_format(UpperCamelCase ,       UpperCamelCase  ) for image in images]

												__UpperCAmelCase   :						List[str]      							=					{"""input_ids""": images}
												return BatchFeature(data=UpperCamelCase ,       tensor_type=UpperCamelCase  )
