




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 a (     nn.Module  ):
						"""simple docstring"""

						def __init__( self :     List[Any]				,							lowerCamelCase__ :     Tuple				)    ->						Union[str, Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[Any]				,							lowerCamelCase__ :     int				)    ->						Tuple:
													"""simple docstring"""


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







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     Dict				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Any				)    ->						Tuple:
													"""simple docstring"""


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

													__lowercase      =    transforms






						def __len__( self :     Tuple				)    ->						str:
													"""simple docstring"""


													return len(self.data				)






						def __getitem__( self :     Optional[Any]				,							lowerCamelCase__ :     Tuple				)    ->						Optional[int]:
													"""simple docstring"""


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

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

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

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






						def     UpperCAmelCase_      ( self :     str				)    ->						Any:
													"""simple docstring"""


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







def 		_A(       UpperCamelCase__							:			Optional[Any]				)    -> int:
							'''simple docstring'''


							__lowercase      =    [len(row['''sentence''']				) for row in batch]
							__lowercase     ,     __lowercase      =    len(UpperCamelCase__				), max(UpperCamelCase__				)

							__lowercase      =    torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long				)
							__lowercase      =    torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long				)

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

							__lowercase      =    torch.stack([row['''image'''] for row in batch]				)
							__lowercase      =    torch.stack([row['''label'''] for row in batch]				)
							__lowercase      =    torch.stack([row['''image_start_token'''] for row in batch]				)
							__lowercase      =    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(       )    -> List[Any]:
							'''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(       )    -> 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]				,       ),
							    ]				)












import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       int       	=		['image_processor', 'tokenizer']
						UpperCamelCase_	:       Dict       	=		'OwlViTImageProcessor'
						UpperCamelCase_	:       List[str]       	=		('CLIPTokenizer', 'CLIPTokenizerFast')






						def __init__( self :     str				,							lowerCamelCase__ :     Any=None				,							lowerCamelCase__ :     List[Any]=None				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    None
													if "feature_extractor" in kwargs:
																				warnings.warn(
																				    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																				    ''' instead.'''				,							lowerCamelCase__				,							)
																				__lowercase      =    kwargs.pop('''feature_extractor'''				)

													__lowercase      =    image_processor if image_processor is not None else feature_extractor
													if image_processor is None:
																				raise ValueError('''You need to specify an `image_processor`.'''				)
													if tokenizer is None:
																				raise ValueError('''You need to specify a `tokenizer`.'''				)

													super().__init__(lowerCamelCase__				,							lowerCamelCase__				)






						def __call__( self :     List[Any]				,							lowerCamelCase__ :     List[str]=None				,							lowerCamelCase__ :     int=None				,							lowerCamelCase__ :     Any=None				,							lowerCamelCase__ :     str="max_length"				,							lowerCamelCase__ :     Union[str, Any]="np"				,							**lowerCamelCase__ :     Dict				)    ->						Union[str, Any]:
													"""simple docstring"""


													if text is None and query_images is None and images is None:
																				raise ValueError(
																				    '''You have to specify at least one text or query image or image. All three cannot be none.'''				)

													if text is not None:
																				if isinstance(lowerCamelCase__				,							lowerCamelCase__				) or (isinstance(lowerCamelCase__				,							lowerCamelCase__				) and not isinstance(text[0]				,							lowerCamelCase__				)):
																											__lowercase      =    [self.tokenizer(lowerCamelCase__				,							padding=lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				)]

																				elif isinstance(lowerCamelCase__				,							lowerCamelCase__				) and isinstance(text[0]				,							lowerCamelCase__				):
																											__lowercase      =    []

																											# Maximum number of queries across batch
																											__lowercase      =    max([len(lowerCamelCase__				) for t in text]				)

																											# Pad all batch samples to max number of text queries
																											for t in text:
																																		if len(lowerCamelCase__				) != max_num_queries:
																																									__lowercase      =    t + [''' '''] * (max_num_queries - len(lowerCamelCase__				))

																																		__lowercase      =    self.tokenizer(lowerCamelCase__				,							padding=lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				)
																																		encodings.append(lowerCamelCase__				)
																				else:
																											raise TypeError('''Input text should be a string, a list of strings or a nested list of strings'''				)

																				if return_tensors == "np":
																											__lowercase      =    np.concatenate([encoding['''input_ids'''] for encoding in encodings]				,							axis=0				)
																											__lowercase      =    np.concatenate([encoding['''attention_mask'''] for encoding in encodings]				,							axis=0				)

																				elif return_tensors == "jax" and is_flax_available():
																											import jax.numpy as jnp

																											__lowercase      =    jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]				,							axis=0				)
																											__lowercase      =    jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]				,							axis=0				)

																				elif return_tensors == "pt" and is_torch_available():
																											import torch

																											__lowercase      =    torch.cat([encoding['''input_ids'''] for encoding in encodings]				,							dim=0				)
																											__lowercase      =    torch.cat([encoding['''attention_mask'''] for encoding in encodings]				,							dim=0				)

																				elif return_tensors == "tf" and is_tf_available():
																											import tensorflow as tf

																											__lowercase      =    tf.stack([encoding['''input_ids'''] for encoding in encodings]				,							axis=0				)
																											__lowercase      =    tf.stack([encoding['''attention_mask'''] for encoding in encodings]				,							axis=0				)

																				else:
																											raise ValueError('''Target return tensor type could not be returned'''				)

																				__lowercase      =    BatchEncoding()
																				__lowercase      =    input_ids
																				__lowercase      =    attention_mask

													if query_images is not None:
																				__lowercase      =    BatchEncoding()
																				__lowercase      =    self.image_processor(
																				    lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				).pixel_values
																				__lowercase      =    query_pixel_values

													if images is not None:
																				__lowercase      =    self.image_processor(lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				)

													if text is not None and images is not None:
																				__lowercase      =    image_features.pixel_values
																				return encoding
													elif query_images is not None and images is not None:
																				__lowercase      =    image_features.pixel_values
																				return encoding
													elif text is not None or query_images is not None:
																				return encoding
													else:
																				return BatchEncoding(data=dict(**lowerCamelCase__				)				,							tensor_type=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     int				,							*lowerCamelCase__ :     str				,							**lowerCamelCase__ :     Optional[int]				)    ->						Tuple:
													"""simple docstring"""


													return self.image_processor.post_process(*lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							*lowerCamelCase__ :     Tuple				,							**lowerCamelCase__ :     Tuple				)    ->						Union[str, Any]:
													"""simple docstring"""


													return self.image_processor.post_process_object_detection(*lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[str]				,							*lowerCamelCase__ :     List[str]				,							**lowerCamelCase__ :     Optional[Any]				)    ->						List[Any]:
													"""simple docstring"""


													return self.image_processor.post_process_image_guided_detection(*lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							*lowerCamelCase__ :     str				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													return self.tokenizer.batch_decode(*lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     str				,							*lowerCamelCase__ :     Optional[int]				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


													return self.tokenizer.decode(*lowerCamelCase__				,							**lowerCamelCase__				)






						@property
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													warnings.warn(
													    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,							lowerCamelCase__				,							)
													return self.image_processor_class






						@property
						def     UpperCAmelCase_      ( self :     str				)    ->						Any:
													"""simple docstring"""


													warnings.warn(
													    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,							lowerCamelCase__				,							)
													return self.image_processor






