


import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





class    A					(     A_				):


							def __init__(self   ,       lowerCAmelCase   ,       lowerCAmelCase = None   ,       lowerCAmelCase = None   ,       lowerCAmelCase = None   ,       lowerCAmelCase = False   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       **lowerCAmelCase   ,       ):
													super().__init__(
													    lowerCAmelCase   ,       split=lowerCAmelCase   ,       features=lowerCAmelCase   ,       cache_dir=lowerCAmelCase   ,       keep_in_memory=lowerCAmelCase   ,       streaming=lowerCAmelCase   ,       num_proc=lowerCAmelCase   ,       **lowerCAmelCase   ,       )
													__lowercase=    path_or_paths if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else {self.split: path_or_paths}
													__lowercase=    Csv(
													    cache_dir=lowerCAmelCase   ,       data_files=lowerCAmelCase   ,       features=lowerCAmelCase   ,       **lowerCAmelCase   ,       )







							def     _A (self      ):
													# Build iterable dataset
													if self.streaming:
																			__lowercase=    self.builder.as_streaming_dataset(split=self.split      )
													# Build regular (map-style) dataset
													else:
																			__lowercase=    None
																			__lowercase=    None
																			__lowercase=    None
																			__lowercase=    None

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





class    A					:


							def __init__(self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase = None   ,       lowerCAmelCase = None   ,       **lowerCAmelCase   ,       ):
													if num_proc is not None and num_proc <= 0:
																			raise ValueError(f'num_proc {num_proc} must be an integer > 0.'      )

													__lowercase=    dataset
													__lowercase=    path_or_buf
													__lowercase=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
													__lowercase=    num_proc
													__lowercase=    'utf-8'
													__lowercase=    to_csv_kwargs


							def     _A (self      ):
													__lowercase=    self.to_csv_kwargs.pop('path_or_buf'   ,       lowerCAmelCase      )
													__lowercase=    self.to_csv_kwargs.pop('header'   ,       lowerCAmelCase      )
													__lowercase=    self.to_csv_kwargs.pop('index'   ,       lowerCAmelCase      )

													if isinstance(self.path_or_buf   ,       (str, bytes, os.PathLike)      ):
																			with open(self.path_or_buf   ,       'wb+'      ) as buffer:
																									__lowercase=    self._write(file_obj=lowerCAmelCase   ,       header=lowerCAmelCase   ,       index=lowerCAmelCase   ,       **self.to_csv_kwargs      )
													else:
																			__lowercase=    self._write(file_obj=self.path_or_buf   ,       header=lowerCAmelCase   ,       index=lowerCAmelCase   ,       **self.to_csv_kwargs      )
													return written


							def     _A (self   ,       lowerCAmelCase      ):
													__lowercase,							__lowercase,							__lowercase,							__lowercase=    args

													__lowercase=    query_table(
													    table=self.dataset.data   ,       key=slice(lowerCAmelCase   ,       offset + self.batch_size      )   ,       indices=self.dataset._indices   ,       )
													__lowercase=    batch.to_pandas().to_csv(
													    path_or_buf=lowerCAmelCase   ,       header=header if (offset == 0) else False   ,       index=lowerCAmelCase   ,       **lowerCAmelCase      )
													return csv_str.encode(self.encoding      )







							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       **lowerCAmelCase      ):
													__lowercase=    0

													if self.num_proc is None or self.num_proc == 1:
																			for offset in logging.tqdm(
																			    range(0   ,       len(self.dataset      )   ,       self.batch_size      )   ,       unit='ba'   ,       disable=not logging.is_progress_bar_enabled()   ,       desc='Creating CSV from Arrow format'   ,       ):
																									__lowercase=    self._batch_csv((offset, header, index, to_csv_kwargs)      )
																									written += file_obj.write(lowerCAmelCase      )

													else:
																			__lowercase,							__lowercase=    len(self.dataset      ), self.batch_size
																			with multiprocessing.Pool(self.num_proc      ) as pool:
																									for csv_str in logging.tqdm(
																									    pool.imap(
																									        self._batch_csv   ,       [(offset, header, index, to_csv_kwargs) for offset in range(0   ,       lowerCAmelCase   ,       lowerCAmelCase      )]   ,       )   ,       total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   ,       unit='ba'   ,       disable=not logging.is_progress_bar_enabled()   ,       desc='Creating CSV from Arrow format'   ,       ):
																															written += file_obj.write(lowerCAmelCase      )

													return written










from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    A					(     A_				):
							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
							UpperCamelCase_     :							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
	from .pipeline_alt_diffusion import AltDiffusionPipeline
	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline






