


import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      Optional[Any]       						=						OmegaConf.load(UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						torch.load(UpperCAmelCase						,	map_location="""cpu"""      )["""model"""]
      __lowerCamelCase     :      str       						=						list(state_dict.keys()      )

      # extract state_dict for VQVAE
      __lowerCamelCase     :      Dict       						=						{}
      __lowerCamelCase     :      List[Any]       						=						"""first_stage_model."""
      for key in keys:
            if key.startswith(UpperCAmelCase      ):
                  __lowerCamelCase     :      Tuple       						=						state_dict[key]

    # extract state_dict for UNetLDM
      __lowerCamelCase     :      Optional[int]       						=						{}
      __lowerCamelCase     :      Optional[Any]       						=						"""model.diffusion_model."""
      for key in keys:
            if key.startswith(UpperCAmelCase      ):
                  __lowerCamelCase     :      Optional[int]       						=						state_dict[key]

      __lowerCamelCase     :      Dict       						=						config.model.params.first_stage_config.params
      __lowerCamelCase     :      str       						=						config.model.params.unet_config.params

      __lowerCamelCase     :      Any       						=						VQModel(**UpperCAmelCase      ).eval()
      vqvae.load_state_dict(UpperCAmelCase      )

      __lowerCamelCase     :      List[str]       						=						UNetLDMModel(**UpperCAmelCase      ).eval()
      unet.load_state_dict(UpperCAmelCase      )

      __lowerCamelCase     :      str       						=						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=UpperCAmelCase						,	)

      __lowerCamelCase     :      Union[str, Any]       						=						LDMPipeline(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
      pipeline.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						Tuple     =							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    :						List[str]     =							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


__UpperCamelCase    :						Any     =							{
    '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:
    __UpperCamelCase    :						Tuple     =							[
        '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

    __UpperCamelCase    :						Optional[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



