




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     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''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(lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''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(lowerCamelCase__							) as cl:
															logger.warning(lowerCamelCase__							)
											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(lowerCamelCase__							) as cl:
											logger.warning(lowerCamelCase__							)
							self.assertEqual(cl.out			,						''							)

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

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


			@mockenv(TRANSFORMERS_VERBOSITY='error'							)
			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							# 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
							__lowerCamelCase		       =    logging.get_logger('transformers.models.bart.tokenization_bart'							)

							__lowerCamelCase		       =    os.getenv('TRANSFORMERS_VERBOSITY'			,						lowerCamelCase__							)
							__lowerCamelCase		       =    logging.log_levels[env_level_str]

							__lowerCamelCase		       =    logging.get_verbosity()
							self.assertEqual(
							    lowerCamelCase__			,						lowerCamelCase__			,						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       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							__lowerCamelCase		       =    logging.logging.getLogger()
							with CaptureLogger(lowerCamelCase__							) 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       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							# testing `logger.warning_advice()`
							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(lowerCamelCase__							) as cl:
															logger.warning_advice(lowerCamelCase__							)
											self.assertEqual(cl.out			,						''							)

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







def 			lowerCamelCase_		(       )     ->       Dict:





				"""simple docstring"""


				disable_progress_bar()
				assert are_progress_bars_disabled()

				enable_progress_bar()
				assert not are_progress_bars_disabled()



"""simple docstring"""






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCAmelCase    :       Dict					     =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :tuple					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[str]=False					,		) ->    Union[str, Any]:
      '''simple docstring'''


      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 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :bool = False		) ->    str:
      '''simple docstring'''


      lowercase														=	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            lowercase														=	"""cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
      else:
            lowercase														=	"""cpu"""
      lowercase														=	Path(lowerCAmelCase__		)

      # VAE DECODER
      lowercase														=	AutoencoderKL.from_pretrained(model_path + """/vae"""		)
      lowercase														=	vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowercase														=	vae_decoder.decode
      onnx_export(
          lowerCAmelCase__					,		model_args=(
              torch.randn(1					,		lowerCAmelCase__					,		2_5					,		2_5		).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    :       Tuple					     =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=1_4,
           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    :       Dict					     =parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("""SD: Done: ONNX""")



