


import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def 				lowerCamelCase__	(      a	:   str	,  a	:   int	,  a	:   Union[str, Any]				)    ->	Optional[int]:





			"""simple docstring"""
			a__ :List[Any]						  =    OmegaConf.load(a				)
			a__ :Optional[Any]						  =    torch.load(a	,  map_location="cpu"				)["model"]
			a__ :Union[str, Any]						  =    list(state_dict.keys()				)

			# extract state_dict for VQVAE
			a__ :Tuple						  =    {}
			a__ :Any						  =    "first_stage_model."
			for key in keys:
						if key.startswith(a				):
									a__ :Optional[int]						  =    state_dict[key]

    # extract state_dict for UNetLDM
			a__ :Union[str, Any]						  =    {}
			a__ :Union[str, Any]						  =    "model.diffusion_model."
			for key in keys:
						if key.startswith(a				):
									a__ :List[str]						  =    state_dict[key]

			a__ :Optional[Any]						  =    config.model.params.first_stage_config.params
			a__ :int						  =    config.model.params.unet_config.params

			a__ :List[Any]						  =    VQModel(**a				).eval()
			vqvae.load_state_dict(a				)

			a__ :int						  =    UNetLDMModel(**a				).eval()
			unet.load_state_dict(a				)

			a__ :int						  =    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=a	,  )

			a__ :Union[str, Any]						  =    LDMPipeline(a	,  a	,  a				)
			pipeline.save_pretrained(a				)


if __name__ == "__main__":
		snake_case__			     =						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)
		snake_case__			     =						parser.parse_args()

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






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__			 =						{
    '''configuration_mgp_str''': ['''MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MgpstrConfig'''],
    '''processing_mgp_str''': ['''MgpstrProcessor'''],
    '''tokenization_mgp_str''': ['''MgpstrTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case__			 =						[
		    '''MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MgpstrModel''',
		    '''MgpstrPreTrainedModel''',
		    '''MgpstrForSceneTextRecognition''',
		]

if TYPE_CHECKING:
		from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
		from .processing_mgp_str import MgpstrProcessor
		from .tokenization_mgp_str import MgpstrTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mgp_str import (
				    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MgpstrForSceneTextRecognition,
				    MgpstrModel,
				    MgpstrPreTrainedModel,
				)
else:
		import sys

		snake_case__			 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


