
"""simple docstring"""

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCamelCase     :    Union[str, Any]       =			abspath(join(dirname(__file__), "src"))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)






def 							A						(    snake_case				:List[Any]		)				->     Tuple:
 config.addinivalue_line(
     'markers'    , 'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested'		)
 config.addinivalue_line(
     'markers'    , 'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested'		)
 config.addinivalue_line('markers'    , 'is_pipeline_test: mark test to run only when pipelines are tested'		)
 config.addinivalue_line('markers'    , 'is_staging_test: mark test to run only in the staging environment'		)
 config.addinivalue_line('markers'    , 'accelerate_tests: mark test that require accelerate'		)
 config.addinivalue_line('markers'    , 'tool_tests: mark the tool tests that are run on their specific schedule'		)






def 							A						(    snake_case				:str		)				->     Any:
 from transformers.testing_utils import pytest_addoption_shared

 pytest_addoption_shared(__UpperCAmelCase		)






def 							A						(    snake_case				:Tuple		)				->     List[Any]:
 from transformers.testing_utils import pytest_terminal_summary_main

 __UpperCamelCase      =	terminalreporter.config.getoption('--make-reports'		)
 if make_reports:
  pytest_terminal_summary_main(__UpperCAmelCase    , id=__UpperCAmelCase		)






def 							A						(    snake_case				:str    , snake_case				:int		)				->     List[str]:
 # If no tests are collected, pytest exists with code 5, which makes the CI fail.
 if exitstatus == 5:
  __UpperCamelCase      =	0


# Doctest custom flag to ignore output.
UpperCamelCase     :    str       =			doctest.register_optionflag("IGNORE_RESULT")

UpperCamelCase     :    List[Any]       =			doctest.OutputChecker
class __lowerCAmelCase		(				_UpperCAmelCase      ):





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      if IGNORE_RESULT & optionflags:
       return True
      return OutputChecker.check_output(self     ,					_UpperCAmelCase     ,					_UpperCAmelCase     ,					_UpperCAmelCase				)


UpperCamelCase     :    List[str]       =			CustomOutputChecker
UpperCamelCase     :    Any       =			HfDoctestModule





UpperCamelCase     :    Optional[int]       =			HfDocTestParser





"""simple docstring"""

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 							A						(    snake_case				:Dict    , snake_case				:Optional[Any]    , snake_case				:List[Any]		)				->     Optional[Any]:
 __UpperCamelCase      =	OmegaConf.load(snake_case		)
 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)['model']
 __UpperCamelCase      =	list(state_dict.keys()		)

 # extract state_dict for VQVAE
 __UpperCamelCase      =	{}
 __UpperCamelCase      =	'first_stage_model.'
 for key in keys:
  if key.startswith(snake_case		):
   __UpperCamelCase      =	state_dict[key]

    # extract state_dict for UNetLDM
 __UpperCamelCase      =	{}
 __UpperCamelCase      =	'model.diffusion_model.'
 for key in keys:
  if key.startswith(snake_case		):
   __UpperCamelCase      =	state_dict[key]

 __UpperCamelCase      =	config.model.params.first_stage_config.params
 __UpperCamelCase      =	config.model.params.unet_config.params

 __UpperCamelCase      =	VQModel(**snake_case		).eval()
 vqvae.load_state_dict(snake_case		)

 __UpperCamelCase      =	UNetLDMModel(**snake_case		).eval()
 unet.load_state_dict(snake_case		)

 __UpperCamelCase      =	DDIMScheduler(
     timesteps=config.model.params.timesteps    , beta_schedule='scaled_linear'    , beta_start=config.model.params.linear_start    , beta_end=config.model.params.linear_end    , clip_sample=snake_case    , )

 __UpperCamelCase      =	LDMPipeline(snake_case    , snake_case    , snake_case		)
 pipeline.save_pretrained(snake_case		)


if __name__ == "__main__":
      UpperCamelCase     :    Dict       =			argparse.ArgumentParser()
      parser.add_argument("--checkpoint_path", type=str, required=True)
      parser.add_argument("--config_path", type=str, required=True)
      parser.add_argument("--output_path", type=str, required=True)
      UpperCamelCase     :    Optional[int]       =			parser.parse_args()

      convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)



