
'''simple docstring'''

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       (		SCREAMING_SNAKE_CASE__      ):





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


				"""simple docstring"""

				super().__init__(
				    __magic_name__ ,							split=__magic_name__ ,							features=__magic_name__ ,							cache_dir=__magic_name__ ,							keep_in_memory=__magic_name__ ,							streaming=__magic_name__ ,							num_proc=__magic_name__ ,							**__magic_name__ ,							)
				lowerCAmelCase__									=							path_or_paths if isinstance(__magic_name__ ,							__magic_name__							) else {self.split: path_or_paths}
				lowerCAmelCase__									=							Csv(
				    cache_dir=__magic_name__ ,							data_files=__magic_name__ ,							features=__magic_name__ ,							**__magic_name__ ,							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

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

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






class 				A       :





	def __init__(   self     :    Dict ,							__magic_name__     :    Dataset ,							__magic_name__     :    Union[PathLike, BinaryIO] ,							__magic_name__     :    Optional[int] = None ,							__magic_name__     :    Optional[int] = None ,							**__magic_name__     :    Any ,							):


				"""simple docstring"""

				if num_proc is not None and num_proc <= 0:
							raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""							)

				lowerCAmelCase__									=							dataset
				lowerCAmelCase__									=							path_or_buf
				lowerCAmelCase__									=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
				lowerCAmelCase__									=							num_proc
				lowerCAmelCase__									=							"utf-8"
				lowerCAmelCase__									=							to_csv_kwargs





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.to_csv_kwargs.pop("path_or_buf" ,							__magic_name__							)
				lowerCAmelCase__									=							self.to_csv_kwargs.pop("header" ,							__magic_name__							)
				lowerCAmelCase__									=							self.to_csv_kwargs.pop("index" ,							__magic_name__							)

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any] ,							__magic_name__     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__									=							args

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    BinaryIO ,							__magic_name__     :    str ,							__magic_name__     :    Tuple ,							**__magic_name__     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							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" ,							):
										lowerCAmelCase__									=							self._batch_csv((offset, header, index, to_csv_kwargs)							)
										written += file_obj.write(__magic_name__							)

				else:
							lowerCAmelCase__		,lowerCAmelCase__									=							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 ,							__magic_name__ ,							__magic_name__							)] ,							) ,							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(__magic_name__							)

				return written







'''simple docstring'''

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCAmelCase__				:	Tuple					=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Dict				       =       ['safety_checker', 'feature_extractor']





	def __init__(   self     :    Optional[Any] ,							__magic_name__     :    AutoencoderKL ,							__magic_name__     :    CLIPTextModel ,							__magic_name__     :    CLIPTokenizer ,							__magic_name__     :    UNetaDConditionModel ,							__magic_name__     :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,							__magic_name__     :    StableDiffusionSafetyChecker ,							__magic_name__     :    CLIPImageProcessor ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				super().__init__()
				if hasattr(scheduler.config ,							"steps_offset"							) and scheduler.config.steps_offset != 1:
							lowerCAmelCase__									=							(
							    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
							    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
							    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
							    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
							    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
							    " file"
							)
							deprecate("steps_offset!=1" ,							"1.0.0" ,							__magic_name__ ,							standard_warn=__magic_name__							)
							lowerCAmelCase__									=							dict(scheduler.config							)
							lowerCAmelCase__									=							1
							lowerCAmelCase__									=							FrozenDict(__magic_name__							)

				self.register_modules(
				    vae=__magic_name__ ,							text_encoder=__magic_name__ ,							tokenizer=__magic_name__ ,							unet=__magic_name__ ,							scheduler=__magic_name__ ,							safety_checker=__magic_name__ ,							feature_extractor=__magic_name__ ,							)
				self.register_to_config(requires_safety_checker=__magic_name__							)





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				return {k: getattr(self ,							__magic_name__							) for k in self.config.keys() if not k.startswith("_"							)}





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    Optional[Union[str, int]] = "auto"							):


				"""simple docstring"""

				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							lowerCAmelCase__									=							self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				self.enable_attention_slicing(__magic_name__							)





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    float = 0.8 ,							__magic_name__     :    Optional[int] = 50 ,							__magic_name__     :    Optional[float] = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    Optional[float] = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							):


				"""simple docstring"""

				return StableDiffusionInpaintPipelineLegacy(**self.components							)(
				    prompt=__magic_name__ ,							image=__magic_name__ ,							mask_image=__magic_name__ ,							strength=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							)





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    float = 0.8 ,							__magic_name__     :    Optional[int] = 50 ,							__magic_name__     :    Optional[float] = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    Optional[float] = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							**__magic_name__     :    List[str] ,							):


				"""simple docstring"""

				return StableDiffusionImgaImgPipeline(**self.components							)(
				    prompt=__magic_name__ ,							image=__magic_name__ ,							strength=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							callback_steps=__magic_name__ ,							)







	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    int = 512 ,							__magic_name__     :    int = 512 ,							__magic_name__     :    int = 50 ,							__magic_name__     :    float = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    float = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[torch.FloatTensor] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							):


				"""simple docstring"""

				return StableDiffusionPipeline(**self.components							)(
				    prompt=__magic_name__ ,							height=__magic_name__ ,							width=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							latents=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							callback_steps=__magic_name__ ,							)





