
"""simple docstring"""



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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Features						):






							"""simple docstring"""



							_lowerCAmelCase  	=			np.inf

							def set_batch_size(SCREAMING_SNAKE_CASE:					FeatureType						) -> None:
														nonlocal batch_size
														if isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			min(SCREAMING_SNAKE_CASE     ,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS						)
														elif isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			min(SCREAMING_SNAKE_CASE     ,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS						)
														elif isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						) and feature.dtype == "binary":
																					_lowerCAmelCase  	=			min(SCREAMING_SNAKE_CASE     ,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS						)

							_visit(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

							return None if batch_size is np.inf else batch_size
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



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







											"""simple docstring"""




											super().__init__(
											    UpperCAmelCase_				,     split=UpperCAmelCase_				,     features=UpperCAmelCase_				,     cache_dir=UpperCAmelCase_				,     keep_in_memory=UpperCAmelCase_				,     streaming=UpperCAmelCase_				,     num_proc=UpperCAmelCase_				,     **UpperCAmelCase_				,     )
											_lowerCAmelCase  	=			path_or_paths if isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ) else {self.split: path_or_paths}
											_lowerCAmelCase  	=			_PACKAGED_DATASETS_MODULES['parquet'][1]
											_lowerCAmelCase  	=			Parquet(
											    cache_dir=UpperCAmelCase_				,     data_files=UpperCAmelCase_				,     features=UpperCAmelCase_				,     hash=UpperCAmelCase_				,     **UpperCAmelCase_				,     )







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







											"""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=UpperCAmelCase_				,     download_mode=UpperCAmelCase_				,     verification_mode=UpperCAmelCase_				,     base_path=UpperCAmelCase_				,     num_proc=self.num_proc				,     )
																		_lowerCAmelCase  	=			self.builder.as_dataset(
																		    split=self.split				,     verification_mode=UpperCAmelCase_				,     in_memory=self.keep_in_memory  )
											return dataset






class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



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







											"""simple docstring"""




											_lowerCAmelCase  	=			dataset
											_lowerCAmelCase  	=			path_or_buf
											_lowerCAmelCase  	=			batch_size or get_writer_batch_size(dataset.features  )
											_lowerCAmelCase  	=			parquet_writer_kwargs


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			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:
																									_lowerCAmelCase  	=			self._write(file_obj=UpperCAmelCase_				,     batch_size=UpperCAmelCase_				,     **self.parquet_writer_kwargs  )
											else:
																		_lowerCAmelCase  	=			self._write(file_obj=self.path_or_buf				,     batch_size=UpperCAmelCase_				,     **self.parquet_writer_kwargs  )
											return written







				def 				__lowerCamelCase	(					self	:			Tuple				,     UpperCAmelCase_	:			BinaryIO				,     UpperCAmelCase_	:			int				,     **UpperCAmelCase_	:			int  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			0
											_lowerCAmelCase  	=			parquet_writer_kwargs.pop('path_or_buf'				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.dataset.features.arrow_schema

											_lowerCAmelCase  	=			pq.ParquetWriter(UpperCAmelCase_				,     schema=UpperCAmelCase_				,     **UpperCAmelCase_  )

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








"""simple docstring"""



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_snake_case          =						argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
_snake_case          =						parser.parse_args()


_snake_case          =						'''cpu'''
_snake_case          =						'''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

_snake_case          =						'''path-to-your-trained-model'''
_snake_case          =						StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
	_snake_case          =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_snake_case          =						pipe.to(device)

# to channels last
_snake_case          =						pipe.unet.to(memory_format=torch.channels_last)
_snake_case          =						pipe.vae.to(memory_format=torch.channels_last)
_snake_case          =						pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
	_snake_case          =						pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_snake_case          =						torch.randn(2, 4, 6_4, 6_4)
_snake_case          =						torch.rand(1) * 9_9_9
_snake_case          =						torch.randn(2, 7_7, 7_6_8)
_snake_case          =						(sample, timestep, encoder_hidden_status)
try:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
	_snake_case          =						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_snake_case          =						6_6_6
_snake_case          =						torch.Generator(device).manual_seed(seed)
_snake_case          =						{'''generator''': generator}
if args.steps is not None:
	_snake_case          =						args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
	_snake_case          =						pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('''generated.png''')






