





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


UpperCAmelCase_     :   List[str]      							=     logging.get_logger(__name__)
class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						**lowerCAmelCase_  )		->							List[Any]:
										_snake_case									=				feature_size
										_snake_case									=				sampling_rate
										_snake_case									=				padding_value

										_snake_case									=				kwargs.pop('padding_side'				,						'right'  )
										_snake_case									=				kwargs.pop('return_attention_mask'				,						__UpperCamelCase  )

										super().__init__(**__UpperCamelCase  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						)		->							List[str]:
										# If we have a list of dicts, let's convert it in a dict of lists
										# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
										if isinstance(__UpperCamelCase				,						(list, tuple)  ) and isinstance(processed_features[0]				,						(dict, BatchFeature)  ):
															_snake_case									=				{
															    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
															}

										# The model's main input name, usually `input_values`, has be passed for padding
										if self.model_input_names[0] not in processed_features:
															raise ValueError(
															    'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
															    F''' to this method that includes {self.model_input_names[0]}, but you provided'''
															    F''' {list(processed_features.keys()  )}'''  )

										_snake_case									=				processed_features[self.model_input_names[0]]
										_snake_case									=				(
										    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
										)

										if len(__UpperCamelCase  ) == 0:
															if return_attention_mask:
																				_snake_case									=				[]
															return processed_features

										# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
										# and rebuild them afterwards if no return_tensors is specified
										# Note that we lose the specific device the tensor may be on for PyTorch

										_snake_case									=				required_input[0]
										if isinstance(__UpperCamelCase				,						(list, tuple)  ):
															# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
															_snake_case									=				0
															while len(required_input[index]  ) == 0:
																				index += 1
															if index < len(__UpperCamelCase  ):
																				_snake_case									=				required_input[index][0]

										if return_tensors is None:
															if is_tf_tensor(__UpperCamelCase  ):
																				_snake_case									=				'tf'
															elif is_torch_tensor(__UpperCamelCase  ):
																				_snake_case									=				'pt'
															elif isinstance(__UpperCamelCase				,						(int, float, list, tuple, np.ndarray)  ):
																				_snake_case									=				'np'
															else:
																				raise ValueError(
																				    F'''type of {first_element} unknown: {type(__UpperCamelCase  )}. '''
																				    'Should be one of a python, numpy, pytorch or tensorflow object.'  )

										for key, value in processed_features.items():
															if isinstance(value[0]				,						(int, float)  ):
																				_snake_case									=				to_numpy(__UpperCamelCase  )
															else:
																				_snake_case									=				[to_numpy(__UpperCamelCase  ) for v in value]

        # Convert padding_strategy in PaddingStrategy
										_snake_case									=				self._get_padding_strategies(padding=__UpperCamelCase				,						max_length=__UpperCamelCase  )

										_snake_case									=				processed_features[self.model_input_names[0]]

										_snake_case									=				len(__UpperCamelCase  )
										if not all(len(__UpperCamelCase  ) == batch_size for v in processed_features.values()  ):
															raise ValueError('Some items in the output dictionary have a different batch size than others.'  )

										_snake_case									=				[]
										for i in range(__UpperCamelCase  ):
															_snake_case									=				{k: v[i] for k, v in processed_features.items()}
															# truncation
															_snake_case									=				self._truncate(
															    __UpperCamelCase				,						max_length=__UpperCamelCase				,						pad_to_multiple_of=__UpperCamelCase				,						truncation=__UpperCamelCase				,						)
															truncated_inputs.append(__UpperCamelCase  )

										if padding_strategy == PaddingStrategy.LONGEST:
															# make sure that `max_length` cannot be longer than the longest truncated length
															_snake_case									=				max(len(input_slice[self.model_input_names[0]]  ) for input_slice in truncated_inputs  )
															_snake_case									=				PaddingStrategy.MAX_LENGTH

										_snake_case									=				{}
										for i in range(__UpperCamelCase  ):
															# padding
															_snake_case									=				self._pad(
															    truncated_inputs[i]				,						max_length=__UpperCamelCase				,						padding_strategy=__UpperCamelCase				,						pad_to_multiple_of=__UpperCamelCase				,						return_attention_mask=__UpperCamelCase				,						)

															for key, value in outputs.items():
																				if key not in batch_outputs:
																									_snake_case									=				[]
																				if value.dtype is np.dtype(np.floataa  ):
																									_snake_case									=				value.astype(np.floataa  )
																				batch_outputs[key].append(__UpperCamelCase  )

										return BatchFeature(__UpperCamelCase				,						tensor_type=__UpperCamelCase  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = PaddingStrategy.DO_NOT_PAD				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						)		->							List[str]:
										_snake_case									=				processed_features[self.model_input_names[0]]

										if padding_strategy == PaddingStrategy.LONGEST:
															_snake_case									=				len(__UpperCamelCase  )

										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
															_snake_case									=				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										_snake_case									=				padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__UpperCamelCase  ) < max_length

										if return_attention_mask and "attention_mask" not in processed_features:
															_snake_case									=				np.ones(len(__UpperCamelCase  )				,						dtype=np.intaa  )

										if needs_to_be_padded:
															_snake_case									=				max_length - len(__UpperCamelCase  )
															if self.padding_side == "right":
																				if return_attention_mask:
																									_snake_case									=				np.pad(
																									    processed_features['attention_mask']				,						(0, difference)  )
																				_snake_case									=				((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																				_snake_case									=				np.pad(
																				    __UpperCamelCase				,						__UpperCamelCase				,						'constant'				,						constant_values=self.padding_value  )
															elif self.padding_side == "left":
																				if return_attention_mask:
																									_snake_case									=				np.pad(
																									    processed_features['attention_mask']				,						(difference, 0)  )
																				_snake_case									=				((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																				_snake_case									=				np.pad(
																				    __UpperCamelCase				,						__UpperCamelCase				,						'constant'				,						constant_values=self.padding_value  )
															else:
																				raise ValueError('Invalid padding strategy:' + str(self.padding_side  )  )

										return processed_features




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						)		->							List[str]:
										if not truncation:
															return processed_features
										elif truncation and max_length is None:
															raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'  )

										_snake_case									=				processed_features[self.model_input_names[0]]

										# find `max_length` that fits `pad_to_multiple_of`
										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
															_snake_case									=				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										_snake_case									=				len(__UpperCamelCase  ) > max_length

										if needs_to_be_truncated:
															_snake_case									=				processed_features[self.model_input_names[0]][:max_length]
															if "attention_mask" in processed_features:
																				_snake_case									=				processed_features['attention_mask'][:max_length]

										return processed_features






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_=False				,						lowerCAmelCase_=None  )		->							List[Any]:

										# Get padding strategy
										if padding is not False:
															if padding is True:
																				_snake_case									=				PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
															elif not isinstance(__UpperCamelCase				,						__UpperCamelCase  ):
																				_snake_case									=				PaddingStrategy(__UpperCamelCase  )
															elif isinstance(__UpperCamelCase				,						__UpperCamelCase  ):
																				_snake_case									=				padding
										else:
															_snake_case									=				PaddingStrategy.DO_NOT_PAD

										# Set max length if needed
										if max_length is None:
															if padding_strategy == PaddingStrategy.MAX_LENGTH:
																				raise ValueError(
																				    F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''  )

        # Test if we have a padding value
										if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
															raise ValueError(
															    'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
															    ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'  )

										return padding_strategy










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					):




					def __init__(		self				,						lowerCAmelCase_  )		->							int:
										super().__init__()
										_snake_case									=				torchvision.models.resnetaaa(pretrained=lowerCAmelCase_  )
										_snake_case									=				list(model.children()  )[:-2]
										_snake_case									=				nn.Sequential(*lowerCAmelCase_  )
										_snake_case									=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							str:
										# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
										_snake_case									=				self.pool(self.model(lowerCAmelCase_  )  )
										_snake_case									=				torch.flatten(lowerCAmelCase_				,						start_dim=2  )
										_snake_case									=				out.transpose(1				,						2  ).contiguous()
										return out  # BxNx2048





class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[Any]:
										_snake_case									=				[json.loads(lowerCAmelCase_  ) for l in open(lowerCAmelCase_  )]
										_snake_case									=				os.path.dirname(lowerCAmelCase_  )
										_snake_case									=				tokenizer
										_snake_case									=				labels
										_snake_case									=				len(lowerCAmelCase_  )
										_snake_case									=				max_seq_length

										_snake_case									=				transforms




					def __len__(		self  )		->							Any:
										return len(self.data  )




					def __getitem__(		self				,						lowerCAmelCase_  )		->							Optional[int]:
										_snake_case									=				torch.LongTensor(self.tokenizer.encode(self.data[index]['text']				,						add_special_tokens=lowerCAmelCase_  )  )
										_snake_case							,     _snake_case							,     _snake_case									=				sentence[0], sentence[1:-1], sentence[-1]
										_snake_case									=				sentence[: self.max_seq_length]

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

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

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






					def 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				Counter()
										for row in self.data:
															label_freqs.update(row['label']  )
										return label_freqs





def 						lowerCamelCase__     (     UpperCamelCase__			:       str						)			-> Dict:



					'''simple docstring'''




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

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

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

					_snake_case									=				torch.stack([row['image'] for row in batch]						)
					_snake_case									=				torch.stack([row['label'] for row in batch]						)
					_snake_case									=				torch.stack([row['image_start_token'] for row in batch]						)
					_snake_case									=				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__     (     )			-> 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__     (     )			-> Tuple:



					'''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]       ,    ),
					    ]						)



