
"""simple docstring"""



import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			logging.get_logger()

											# the current default level is logging.WARNING
											_lowerCAmelCase  	=			logging.get_verbosity()

											logging.set_verbosity_error()
											self.assertEqual(logger.getEffectiveLevel()				,     logging.get_verbosity()  )

											logging.set_verbosity_warning()
											self.assertEqual(logger.getEffectiveLevel()				,     logging.get_verbosity()  )

											logging.set_verbosity_info()
											self.assertEqual(logger.getEffectiveLevel()				,     logging.get_verbosity()  )

											logging.set_verbosity_debug()
											self.assertEqual(logger.getEffectiveLevel()				,     logging.get_verbosity()  )

											# restore to the original level
											logging.set_verbosity(UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			logging.get_verbosity()

											_lowerCAmelCase  	=			logging.get_logger('transformers.models.bart.tokenization_bart'  )
											_lowerCAmelCase  	=			"""Testing 1, 2, 3"""

											# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
											if level_origin <= logging.WARNING:
																		with CaptureLogger(UpperCAmelCase_  ) as cl:
																									logger.warning(UpperCAmelCase_  )
																		self.assertEqual(cl.out				,     msg + '\n'  )

											# this is setting the level for all of `transformers.*` loggers
											logging.set_verbosity_error()

											# should not be able to log warnings
											with CaptureLogger(UpperCAmelCase_  ) as cl:
																		logger.warning(UpperCAmelCase_  )
											self.assertEqual(cl.out				,     ''  )

											# should be able to log warnings again
											logging.set_verbosity_warning()
											with CaptureLogger(UpperCAmelCase_  ) as cl:
																		logger.warning(UpperCAmelCase_  )
											self.assertEqual(cl.out				,     msg + '\n'  )

											# restore to the original level
											logging.set_verbosity(UpperCAmelCase_  )


				@mockenv(TRANSFORMERS_VERBOSITY='error'  )
				def 				__lowerCamelCase	(					self	:			str  )		->  Dict:







											"""simple docstring"""




											transformers.utils.logging._reset_library_root_logger()
											# this action activates the env var
											_lowerCAmelCase  	=			logging.get_logger('transformers.models.bart.tokenization_bart'  )

											_lowerCAmelCase  	=			os.getenv('TRANSFORMERS_VERBOSITY'				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			logging.log_levels[env_level_str]

											_lowerCAmelCase  	=			logging.get_verbosity()
											self.assertEqual(
											    UpperCAmelCase_				,     UpperCAmelCase_				,     F"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"""				,     )

											# restore to the original level
											_lowerCAmelCase  	=			""""""
											transformers.utils.logging._reset_library_root_logger()


				@mockenv(TRANSFORMERS_VERBOSITY='super-error'  )
				def 				__lowerCamelCase	(					self	:			str  )		->  str:







											"""simple docstring"""




											transformers.utils.logging._reset_library_root_logger()
											_lowerCAmelCase  	=			logging.logging.getLogger()
											with CaptureLogger(UpperCAmelCase_  ) as cl:
																		# this action activates the env var
																		logging.get_logger('transformers.models.bart.tokenization_bart'  )
											self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error'				,     cl.out  )

											# no need to restore as nothing was changed







				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[int]:







											"""simple docstring"""




											transformers.utils.logging._reset_library_root_logger()

											_lowerCAmelCase  	=			logging.get_logger('transformers.models.bart.tokenization_bart'  )
											_lowerCAmelCase  	=			"""Testing 1, 2, 3"""

											with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1'  ):
																		# nothing should be logged as env var disables this method
																		with CaptureLogger(UpperCAmelCase_  ) as cl:
																									logger.warning_advice(UpperCAmelCase_  )
																		self.assertEqual(cl.out				,     ''  )

											with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''  ):
																		# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
																		with CaptureLogger(UpperCAmelCase_  ) as cl:
																									logger.warning_advice(UpperCAmelCase_  )
																		self.assertEqual(cl.out				,     msg + '\n'  )







def 	__snake_case  (    ):






							"""simple docstring"""



							disable_progress_bar()
							assert are_progress_bars_disabled()

							enable_progress_bar()
							assert not are_progress_bars_disabled()








"""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''')






