
import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowerCamelCase_						: int         =  version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=False     ,    )  ->	Union[str, Any]:
				output_path.parent.mkdir(parents=lowerCamelCase     ,    exist_ok=lowerCamelCase						)
				# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
				# so we check the torch version for backwards compatibility
				if is_torch_less_than_1_11:
								export(
								    lowerCamelCase     ,    lowerCamelCase     ,    f=output_path.as_posix()     ,    input_names=lowerCamelCase     ,    output_names=lowerCamelCase     ,    dynamic_axes=lowerCamelCase     ,    do_constant_folding=lowerCamelCase     ,    use_external_data_format=lowerCamelCase     ,    enable_onnx_checker=lowerCamelCase     ,    opset_version=lowerCamelCase     ,    )
				else:
								export(
								    lowerCamelCase     ,    lowerCamelCase     ,    f=output_path.as_posix()     ,    input_names=lowerCamelCase     ,    output_names=lowerCamelCase     ,    dynamic_axes=lowerCamelCase     ,    do_constant_folding=lowerCamelCase     ,    opset_version=lowerCamelCase     ,    )







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = False						)  ->	Dict:
				UpperCamelCase_:							Optional[Any]	=						torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								UpperCamelCase_:							Tuple	=						"""cuda"""
				elif fpaa and not torch.cuda.is_available():
								raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""						)
				else:
								UpperCamelCase_:							Optional[Any]	=						"""cpu"""
				UpperCamelCase_:							int	=						Path(lowerCamelCase						)

				# VAE DECODER
				UpperCamelCase_:							Dict	=						AutoencoderKL.from_pretrained(model_path + """/vae"""						)
				UpperCamelCase_:							Optional[Any]	=						vae_decoder.config.latent_channels
				# forward only through the decoder part
				UpperCamelCase_:							Tuple	=						vae_decoder.decode
				onnx_export(
				    lowerCamelCase     ,    model_args=(
				        torch.randn(1     ,    lowerCamelCase     ,    25     ,    25						).to(device=lowerCamelCase     ,    dtype=lowerCamelCase						),
				        False,
				    )     ,    output_path=output_path / """vae_decoder""" / """model.onnx"""     ,    ordered_input_names=["""latent_sample""", """return_dict"""]     ,    output_names=["""sample"""]     ,    dynamic_axes={
				        """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
				    }     ,    opset=lowerCamelCase     ,    )
				del vae_decoder


if __name__ == "__main__":
							lowerCamelCase_						: Union[str, Any]         =  argparse.ArgumentParser()

							parser.add_argument(
							    """--model_path""",
							    type=str,
							    required=True,
							    help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
							)

							parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
							parser.add_argument(
							    """--opset""",
							    default=14,
							    type=int,
							    help="""The version of the ONNX operator set to use.""",
							)
							parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

							lowerCamelCase_						: List[Any]         =  parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print("""SD: Done: ONNX""")








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        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Optional[int]	=						logging.get_logger()

							# the current default level is logging.WARNING
							UpperCamelCase_:							Dict	=						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(snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Union[str, Any]	=						logging.get_verbosity()

							UpperCamelCase_:							int	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Union[str, Any]	=						"""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(snake_case_      ) as cl:
															logger.warning(snake_case_      )
											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(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	""""""      )

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

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

			@mockenv(TRANSFORMERS_VERBOSITY="""error"""      )
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							# this action activates the env var
							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )

							UpperCamelCase_:							str	=						os.getenv("""TRANSFORMERS_VERBOSITY"""						,	snake_case_      )
							UpperCamelCase_:							Any	=						logging.log_levels[env_level_str]

							UpperCamelCase_:							Dict	=						logging.get_verbosity()
							self.assertEqual(
							    snake_case_						,	snake_case_						,	f'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''						,	)

							# restore to the original level
							UpperCamelCase_:							str	=						""""""
							transformers.utils.logging._reset_library_root_logger()

			@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""      )
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							UpperCamelCase_:							str	=						logging.logging.getLogger()
							with CaptureLogger(snake_case_      ) 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[Any]      ):
							# testing `logger.warning_advice()`
							transformers.utils.logging._reset_library_root_logger()

							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Any	=						"""Testing 1, 2, 3"""

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

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







def        A__					(						)  ->	Union[str, Any]:
				disable_progress_bar()
				assert are_progress_bars_disabled()

				enable_progress_bar()
				assert not are_progress_bars_disabled()






