





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCAmelCase_     :   Dict      							=     datasets.utils.logging.get_logger(__name__)
@dataclass
class 			UpperCamelCase_		(  datasets.BuilderConfig					):
					lowerCAmelCase_							=	None
					lowerCAmelCase_							=	'''utf-8'''
					lowerCAmelCase_							=	None
					lowerCAmelCase_							=	None
					lowerCAmelCase_							=	True  # deprecated
					lowerCAmelCase_							=	None  # deprecated
					lowerCAmelCase_							=	10 << 20  # 10MB
					lowerCAmelCase_							=	None





class 			UpperCamelCase_		(  datasets.ArrowBasedBuilder					):
					lowerCAmelCase_							=	JsonConfig




					def 	lowerCAmelCase			(		self  )		->							Any:
										if self.config.block_size is not None:
															logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'  )
															_snake_case									=				self.config.block_size
										if self.config.use_threads is not True:
															logger.warning(
															    'The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'  )
										if self.config.newlines_in_values is not None:
															raise ValueError('The JSON loader parameter `newlines_in_values` is no longer supported'  )
										return datasets.DatasetInfo(features=self.config.features  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Optional[int]:
										if not self.config.data_files:
															raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''  )
										_snake_case									=				dl_manager.download_and_extract(self.config.data_files  )
										if isinstance(lowerCAmelCase_				,						(str, list, tuple)  ):
															_snake_case									=				data_files
															if isinstance(lowerCAmelCase_				,						lowerCAmelCase_  ):
																				_snake_case									=				[files]
															_snake_case									=				[dl_manager.iter_files(lowerCAmelCase_  ) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,						gen_kwargs={'files': files}  )]
										_snake_case									=				[]
										for split_name, files in data_files.items():
															if isinstance(lowerCAmelCase_				,						lowerCAmelCase_  ):
																				_snake_case									=				[files]
															_snake_case									=				[dl_manager.iter_files(lowerCAmelCase_  ) for file in files]
															splits.append(datasets.SplitGenerator(name=lowerCAmelCase_				,						gen_kwargs={'files': files}  )  )
										return splits




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							pa.Table:
										if self.config.features is not None:
															# adding missing columns
															for column_name in set(self.config.features  ) - set(pa_table.column_names  ):
																				_snake_case									=				self.config.features.arrow_schema.field(lowerCAmelCase_  ).type
																				_snake_case									=				pa_table.append_column(lowerCAmelCase_				,						pa.array([None] * len(lowerCAmelCase_  )				,						type=lowerCAmelCase_  )  )
															# more expensive cast to support nested structures with keys in a different order
															# allows str <-> int/float or str to Audio for example
															_snake_case									=				table_cast(lowerCAmelCase_				,						self.config.features.arrow_schema  )
										return pa_table






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Dict:
										for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase_  )  ):
															# If the file is one json object and if we need to look at the list of items in one specific field
															if self.config.field is not None:
																				with open(lowerCAmelCase_				,						encoding=self.config.encoding				,						errors=self.config.encoding_errors  ) as f:
																									_snake_case									=				json.load(lowerCAmelCase_  )

																				# We keep only the field we are interested in
																				_snake_case									=				dataset[self.config.field]

																				# We accept two format: a list of dicts or a dict of lists
																				if isinstance(lowerCAmelCase_				,						(list, tuple)  ):
																									_snake_case									=				set().union(*[row.keys() for row in dataset]  )
																									_snake_case									=				{col: [row.get(lowerCAmelCase_  ) for row in dataset] for col in keys}
																				else:
																									_snake_case									=				dataset
																				_snake_case									=				pa.Table.from_pydict(lowerCAmelCase_  )
																				yield file_idx, self._cast_table(lowerCAmelCase_  )

															# If the file has one json object per line
															else:
																				with open(lowerCAmelCase_				,						'rb'  ) as f:
																									_snake_case									=				0
																									# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																									# Set a default minimum value of 16kB if the chunk size is really small
																									_snake_case									=				max(self.config.chunksize // 32				,						16 << 10  )
																									_snake_case									=				(
																									    self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
																									)
																									while True:
																														_snake_case									=				f.read(self.config.chunksize  )
																														if not batch:
																																			break
																														# Finish current line
																														try:
																																			batch += f.readline()
																														except (AttributeError, io.UnsupportedOperation):
																																			batch += readline(lowerCAmelCase_  )
																														# PyArrow only accepts utf-8 encoded bytes
																														if self.config.encoding != "utf-8":
																																			_snake_case									=				batch.decode(self.config.encoding				,						errors=lowerCAmelCase_  ).encode('utf-8'  )
																														try:
																																			while True:
																																								try:
																																													_snake_case									=				paj.read_json(
																																													    io.BytesIO(lowerCAmelCase_  )				,						read_options=paj.ReadOptions(block_size=lowerCAmelCase_  )  )
																																													break
																																								except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																													if (
																																													    isinstance(lowerCAmelCase_				,						pa.ArrowInvalid  )
																																													    and "straddling" not in str(lowerCAmelCase_  )
																																													    or block_size > len(lowerCAmelCase_  )
																																													):
																																																		raise
																																													else:
																																																		# Increase the block size in case it was too small.
																																																		# The block size will be reset for the next file.
																																																		logger.debug(
																																																		    F'''Batch of {len(lowerCAmelCase_  )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''  )
																																																		block_size *= 2
																														except pa.ArrowInvalid as e:
																																			try:
																																								with open(
																																								    lowerCAmelCase_				,						encoding=self.config.encoding				,						errors=self.config.encoding_errors  ) as f:
																																													_snake_case									=				json.load(lowerCAmelCase_  )
																																			except json.JSONDecodeError:
																																								logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCAmelCase_  )}: {e}'''  )
																																								raise e
																																			# If possible, parse the file as a list of json objects and exit the loop
																																			if isinstance(lowerCAmelCase_				,						lowerCAmelCase_  ):  # list is the only sequence type supported in JSON
																																								try:
																																													_snake_case									=				set().union(*[row.keys() for row in dataset]  )
																																													_snake_case									=				{col: [row.get(lowerCAmelCase_  ) for row in dataset] for col in keys}
																																													_snake_case									=				pa.Table.from_pydict(lowerCAmelCase_  )
																																								except (pa.ArrowInvalid, AttributeError) as e:
																																													logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCAmelCase_  )}: {e}'''  )
																																													raise ValueError(F'''Not able to read records in the JSON file at {file}.'''  ) from None
																																								yield file_idx, self._cast_table(lowerCAmelCase_  )
																																								break
																																			else:
																																								logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCAmelCase_  )}: {e}'''  )
																																								raise ValueError(
																																								    F'''Not able to read records in the JSON file at {file}. '''
																																								    F'''You should probably indicate the field of the JSON file containing your records. '''
																																								    F'''This JSON file contain the following fields: {str(list(dataset.keys()  )  )}. '''
																																								    F'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''  ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																														yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase_  )
																														batch_idx += 1










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]       ,    ),
					    ]						)



