




import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





def 				A__ (snake_case   :		Features				)     ->						Optional[int]:
					__UpperCamelCase					:	Any             =      np.inf

					def set_batch_size(snake_case   :		FeatureType				) -> None:
										nonlocal batch_size
										if isinstance(snake_case		,	snake_case				):
															__UpperCamelCase					:	Optional[Any]             =      min(snake_case		,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS				)
										elif isinstance(snake_case		,	snake_case				):
															__UpperCamelCase					:	Optional[Any]             =      min(snake_case		,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS				)
										elif isinstance(snake_case		,	snake_case				) and feature.dtype == "binary":
															__UpperCamelCase					:	Optional[int]             =      min(snake_case		,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS				)

					_visit(snake_case		,	snake_case				)

					return None if batch_size is np.inf else batch_size


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						NestedDataStructureLike[PathLike]					,     lowerCAmelCase		:						Optional[NamedSplit] = None					,     lowerCAmelCase		:						Optional[Features] = None					,     lowerCAmelCase		:						str = None					,     lowerCAmelCase		:						bool = False					,     lowerCAmelCase		:						bool = False					,     lowerCAmelCase		:						Optional[int] = None					,     **lowerCAmelCase		:						str					,     )	->       int:







						"""simple docstring"""







						super().__init__(
						    lowerCAmelCase					,     split=lowerCAmelCase					,     features=lowerCAmelCase					,     cache_dir=lowerCAmelCase					,     keep_in_memory=lowerCAmelCase					,     streaming=lowerCAmelCase					,     num_proc=lowerCAmelCase					,     **lowerCAmelCase					,     )
						__UpperCamelCase					:	Tuple             =      path_or_paths if isinstance(lowerCAmelCase					,     lowerCAmelCase	) else {self.split: path_or_paths}
						__UpperCamelCase					:	Any             =      _PACKAGED_DATASETS_MODULES["""parquet"""][1]
						__UpperCamelCase					:	Optional[Any]             =      Parquet(
						    cache_dir=lowerCAmelCase					,     data_files=lowerCAmelCase					,     features=lowerCAmelCase					,     hash=lowerCAmelCase					,     **lowerCAmelCase					,     )






	def 					lowerCamelCase__     (					self		:						Any	)	->       Dict:







						"""simple docstring"""







						if self.streaming:
											__UpperCamelCase					:	List[str]             =      self.builder.as_streaming_dataset(split=self.split	)
						# Build regular (map-style) dataset
						else:
											__UpperCamelCase					:	List[Any]             =      None
											__UpperCamelCase					:	Optional[Any]             =      None
											__UpperCamelCase					:	List[str]             =      None
											__UpperCamelCase					:	Optional[int]             =      None

											self.builder.download_and_prepare(
											    download_config=lowerCAmelCase					,     download_mode=lowerCAmelCase					,     verification_mode=lowerCAmelCase					,     base_path=lowerCAmelCase					,     num_proc=self.num_proc					,     )
											__UpperCamelCase					:	Tuple             =      self.builder.as_dataset(
											    split=self.split					,     verification_mode=lowerCAmelCase					,     in_memory=self.keep_in_memory	)
						return dataset






class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __init__(					self		:						str					,     lowerCAmelCase		:						Dataset					,     lowerCAmelCase		:						Union[PathLike, BinaryIO]					,     lowerCAmelCase		:						Optional[int] = None					,     **lowerCAmelCase		:						Tuple					,     )	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      dataset
						__UpperCamelCase					:	str             =      path_or_buf
						__UpperCamelCase					:	Union[str, Any]             =      batch_size or get_writer_batch_size(dataset.features	)
						__UpperCamelCase					:	List[Any]             =      parquet_writer_kwargs






	def 					lowerCamelCase__     (					self		:						Tuple	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

						if isinstance(self.path_or_buf					,     (str, bytes, os.PathLike)	):
											with open(self.path_or_buf					,     """wb+"""	) as buffer:
																__UpperCamelCase					:	str             =      self._write(file_obj=lowerCAmelCase					,     batch_size=lowerCAmelCase					,     **self.parquet_writer_kwargs	)
						else:
											__UpperCamelCase					:	Union[str, Any]             =      self._write(file_obj=self.path_or_buf					,     batch_size=lowerCAmelCase					,     **self.parquet_writer_kwargs	)
						return written






	def 					lowerCamelCase__     (					self		:						int					,     lowerCAmelCase		:						BinaryIO					,     lowerCAmelCase		:						int					,     **lowerCAmelCase		:						Optional[int]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      0
						__UpperCamelCase					:	Any             =      parquet_writer_kwargs.pop("""path_or_buf"""					,     lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      self.dataset.features.arrow_schema

						__UpperCamelCase					:	str             =      pq.ParquetWriter(lowerCAmelCase					,     schema=lowerCAmelCase					,     **lowerCAmelCase	)

						for offset in logging.tqdm(
						    range(0					,     len(self.dataset	)					,     lowerCAmelCase	)					,     unit="""ba"""					,     disable=not logging.is_progress_bar_enabled()					,     desc="""Creating parquet from Arrow format"""					,     ):
											__UpperCamelCase					:	Dict             =      query_table(
											    table=self.dataset._data					,     key=slice(lowerCAmelCase					,     offset + batch_size	)					,     indices=self.dataset._indices if self.dataset._indices is not None else None					,     )
											writer.write_table(lowerCAmelCase	)
											written += batch.nbytes
						writer.close()
						return written












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    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						List[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.pool(self.model(lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      torch.flatten(lowerCAmelCase					,     start_dim=2	)
						__UpperCamelCase					:	Optional[int]             =      out.transpose(1					,     2	).contiguous()
						return out  # BxNx2048






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      [json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
						__UpperCamelCase					:	Optional[Any]             =      os.path.dirname(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      tokenizer
						__UpperCamelCase					:	Dict             =      labels
						__UpperCamelCase					:	int             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      max_seq_length

						__UpperCamelCase					:	int             =      transforms






	def __len__(					self		:						Union[str, Any]	)	->       List[Any]:







						"""simple docstring"""







						return len(self.data	)






	def __getitem__(					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]	)	->       int:







						"""simple docstring"""







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

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

						__UpperCamelCase					:	List[Any]             =      Image.open(os.path.join(self.data_dir					,     self.data[index]["""img"""]	)	).convert("""RGB"""	)
						__UpperCamelCase					:	Dict             =      self.transforms(lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







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






def 				A__ (snake_case   :		int				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      [len(row["""sentence"""]				) for row in batch]
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      len(snake_case				), max(snake_case				)

					__UpperCamelCase					:	List[str]             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)
					__UpperCamelCase					:	Tuple             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)

					for i_batch, (input_row, length) in enumerate(zip(snake_case		,	snake_case				)				):
										__UpperCamelCase					:	Optional[Any]             =      input_row["""sentence"""]
										__UpperCamelCase					:	Any             =      1

					__UpperCamelCase					:	List[Any]             =      torch.stack([row["""image"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""label"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""image_start_token"""] for row in batch]				)
					__UpperCamelCase					:	List[str]             =      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__ ()     ->						Optional[Any]:
					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__ ()     ->						Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(2_56				),
					        transforms.CenterCrop(2_24				),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017]		,	std=[0.12221994, 0.12145835, 0.14380469]		,	),
					    ]				)






