





'''simple docstring'''

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def    a__							(							lowercase   :	int,   lowercase   :	Union[str, Any] )						->						List[Any]:





     """simple docstring"""

     assert isinstance(lowercase,   lowercase )
     assert dataset.num_rows == 4
     assert dataset.num_columns == 1
     assert dataset.column_names == ["text"]
     for feature, expected_dtype in expected_features.items():
          assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory''',   [False, True] )
def    a__							(							lowercase   :	Optional[int],   lowercase   :	Union[str, Any],   lowercase   :	int )						->						Tuple:





     """simple docstring"""

     _UpperCamelCase							       =			tmp_path / '''cache'''
     _UpperCamelCase							       =			{'''text''': '''string'''}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _UpperCamelCase							       =			TextDatasetReader(lowercase,   cache_dir=lowercase,   keep_in_memory=lowercase ).read()
     _check_text_dataset(lowercase,   lowercase )







@pytest.mark.parametrize(
    '''features''',   [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ],   )
def    a__							(							lowercase   :	Dict,   lowercase   :	Union[str, Any],   lowercase   :	int )						->						List[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			tmp_path / '''cache'''
     _UpperCamelCase							       =			{'''text''': '''string'''}
     _UpperCamelCase							       =			features.copy() if features else default_expected_features
     _UpperCamelCase							       =			(
         Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
     )
     _UpperCamelCase							       =			TextDatasetReader(lowercase,   features=lowercase,   cache_dir=lowercase ).read()
     _check_text_dataset(lowercase,   lowercase )







@pytest.mark.parametrize('''split''',   [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def    a__							(							lowercase   :	Optional[int],   lowercase   :	Any,   lowercase   :	str )						->						Tuple:





     """simple docstring"""

     _UpperCamelCase							       =			tmp_path / '''cache'''
     _UpperCamelCase							       =			{'''text''': '''string'''}
     _UpperCamelCase							       =			TextDatasetReader(lowercase,   cache_dir=lowercase,   split=lowercase ).read()
     _check_text_dataset(lowercase,   lowercase )
     assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type''',   [str, list] )
def    a__							(							lowercase   :	str,   lowercase   :	int,   lowercase   :	Dict )						->						Optional[Any]:





     """simple docstring"""

     if issubclass(lowercase,   lowercase ):
          _UpperCamelCase							       =			text_path
     elif issubclass(lowercase,   lowercase ):
          _UpperCamelCase							       =			[text_path]
     _UpperCamelCase							       =			tmp_path / '''cache'''
     _UpperCamelCase							       =			{'''text''': '''string'''}
     _UpperCamelCase							       =			TextDatasetReader(lowercase,   cache_dir=lowercase ).read()
     _check_text_dataset(lowercase,   lowercase )







def    a__							(							lowercase   :	Optional[int],   lowercase   :	Optional[int],   lowercase   :	int=("train",) )						->						List[str]:





     """simple docstring"""

     assert isinstance(lowercase,   lowercase )
     for split in splits:
          _UpperCamelCase							       =			dataset_dict[split]
          assert dataset.num_rows == 4
          assert dataset.num_columns == 1
          assert dataset.column_names == ["text"]
          for feature, expected_dtype in expected_features.items():
               assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory''',   [False, True] )
def    a__							(							lowercase   :	str,   lowercase   :	Optional[Any],   lowercase   :	int )						->						int:





     """simple docstring"""

     _UpperCamelCase							       =			tmp_path / '''cache'''
     _UpperCamelCase							       =			{'''text''': '''string'''}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _UpperCamelCase							       =			TextDatasetReader({'''train''': text_path},   cache_dir=lowercase,   keep_in_memory=lowercase ).read()
     _check_text_datasetdict(lowercase,   lowercase )







@pytest.mark.parametrize(
    '''features''',   [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ],   )
def    a__							(							lowercase   :	Optional[Any],   lowercase   :	Dict,   lowercase   :	int )						->						Any:





     """simple docstring"""

     _UpperCamelCase							       =			tmp_path / '''cache'''
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     _UpperCamelCase							       =			{'''text''': '''string'''}
     _UpperCamelCase							       =			features.copy() if features else default_expected_features
     _UpperCamelCase							       =			(
         Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
     )
     _UpperCamelCase							       =			TextDatasetReader({'''train''': text_path},   features=lowercase,   cache_dir=lowercase ).read()
     _check_text_datasetdict(lowercase,   lowercase )







@pytest.mark.parametrize('''split''',   [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def    a__							(							lowercase   :	List[Any],   lowercase   :	Optional[int],   lowercase   :	Any )						->						Dict:





     """simple docstring"""

     if split:
          _UpperCamelCase							       =			{split: text_path}
     else:
          _UpperCamelCase							       =			'''train'''
          _UpperCamelCase							       =			{'''train''': text_path, '''test''': text_path}
     _UpperCamelCase							       =			tmp_path / '''cache'''
     _UpperCamelCase							       =			{'''text''': '''string'''}
     _UpperCamelCase							       =			TextDatasetReader(lowercase,   cache_dir=lowercase ).read()
     _check_text_datasetdict(lowercase,   lowercase,   splits=list(path.keys() ) )
     assert all(dataset[split].split == split for split in path.keys() )










'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__		:     int			=							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')
lowercase__		:     Union[str, Any]			=							parser.parse_args()


lowercase__		:     Union[str, Any]			=							'cpu'
lowercase__		:     Tuple			=							'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

lowercase__		:     List[Any]			=							'path-to-your-trained-model'
lowercase__		:     Optional[Any]			=							StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
   lowercase__		:     Union[str, Any]			=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__		:     Dict			=							pipe.to(device)

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

# optimize with ipex
lowercase__		:     List[str]			=							torch.randn(2, 4, 64, 64)
lowercase__		:     int			=							torch.rand(1) * 9_99
lowercase__		:     Optional[int]			=							torch.randn(2, 77, 7_68)
lowercase__		:     List[str]			=							(sample, timestep, encoder_hidden_status)
try:
   lowercase__		:     Optional[Any]			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   lowercase__		:     Any			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Tuple			=							ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Any			=							ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   lowercase__		:     Union[str, Any]			=							ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase__		:     Optional[Any]			=							6_66
lowercase__		:     Tuple			=							torch.Generator(device).manual_seed(seed)
lowercase__		:     Dict			=							{'generator': generator}
if args.steps is not None:
   lowercase__		:     int			=							args.steps

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

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



