


'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a_						:       Tuple      			=				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""")
a_						:       int      			=				parser.parse_args()


a_						:       Dict      			=				"""cpu"""
a_						:       int      			=				"""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

a_						:       Tuple      			=				"""path-to-your-trained-model"""
a_						:       List[Any]      			=				StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
     a_						:       Optional[int]      			=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
a_						:       Tuple      			=				pipe.to(device)

# to channels last
a_						:       Tuple      			=				pipe.unet.to(memory_format=torch.channels_last)
a_						:       Dict      			=				pipe.vae.to(memory_format=torch.channels_last)
a_						:       str      			=				pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
     a_						:       Optional[Any]      			=				pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
a_						:       str      			=				torch.randn(2, 4, 64, 64)
a_						:       Optional[int]      			=				torch.rand(1) * 999
a_						:       Optional[Any]      			=				torch.randn(2, 77, 768)
a_						:       int      			=				(sample, timestep, encoder_hidden_status)
try:
     a_						:       List[str]      			=				ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
     a_						:       List[str]      			=				ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
a_						:       Union[str, Any]      			=				ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
a_						:       List[Any]      			=				ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
     a_						:       List[str]      			=				ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
a_						:       List[str]      			=				666
a_						:       List[Any]      			=				torch.Generator(device).manual_seed(seed)
a_						:       Optional[int]      			=				{"""generator""": generator}
if args.steps is not None:
     a_						:       Union[str, Any]      			=				args.steps

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

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







'''simple docstring'''

import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy





def __snake_case      (   UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Dict	):
 assert isinstance(UpperCAmelCase_      ,       UpperCAmelCase_	)
 assert dataset.num_rows == 4
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_1", "col_2", "col_3"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype





@require_sqlalchemy
@pytest.mark.parametrize("keep_in_memory"      ,       [False, True]	)
def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Union[str, Any]	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  lowerCamelCase_  				=						SqlDatasetReader(
      "dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_      ,       keep_in_memory=UpperCAmelCase_	).read()
 _check_sql_dataset(UpperCAmelCase_      ,       UpperCAmelCase_	)





@require_sqlalchemy
@pytest.mark.parametrize(
    "features"      ,       [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]      ,       )
def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		str	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCamelCase_  				=						features.copy() if features else default_expected_features
 lowerCamelCase_  				=						(
     Features({feature: Value(UpperCAmelCase_	) for feature, dtype in features.items()}	) if features is not None else None
 )
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       features=UpperCAmelCase_      ,       cache_dir=UpperCAmelCase_	).read()
 _check_sql_dataset(UpperCAmelCase_      ,       UpperCAmelCase_	)





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]	):
 with contextlib.closing(sqlitea.connect(UpperCAmelCase_	)	) as con:
  lowerCamelCase_  				=						con.cursor()
  cur.execute("SELECT * FROM dataset"	)
  for row in cur:
   yield row





@require_sqlalchemy
def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		List[str]	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "tmp.sql"	)
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_	).read()
 SqlDatasetWriter(UpperCAmelCase_      ,       "dataset"      ,       "sqlite:///" + output_sqlite_path      ,       num_proc=1	).write()

 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)
 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)

 for rowa, rowa in zip(UpperCAmelCase_      ,       UpperCAmelCase_	):
  assert rowa == rowa





@require_sqlalchemy
def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Tuple	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "tmp.sql"	)
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_	).read()
 SqlDatasetWriter(UpperCAmelCase_      ,       "dataset"      ,       "sqlite:///" + output_sqlite_path      ,       num_proc=2	).write()

 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)
 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)

 for rowa, rowa in zip(UpperCAmelCase_      ,       UpperCAmelCase_	):
  assert rowa == rowa







@require_sqlalchemy
def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		Tuple	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "tmp.sql"	)
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_	).read()
 with pytest.raises(UpperCAmelCase_	):
  SqlDatasetWriter(UpperCAmelCase_      ,       "dataset"      ,       "sqlite:///" + output_sqlite_path      ,       num_proc=0	).write()



