

# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_ 							=       {
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_ 							=       ["""VivitImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_ 							=       [
					    """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """VivitModel""",
					    """VivitPreTrainedModel""",
					    """VivitForVideoClassification""",
					]


if TYPE_CHECKING:
					from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_vivit import VivitImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vivit import (
										    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VivitForVideoClassification,
										    VivitModel,
										    VivitPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase_ 							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Dict  )     ->	List[Any]:



					'''simple docstring'''




					_snake_case													=			OmegaConf.load(UpperCamelCase__  )
					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )['model']
					_snake_case													=			list(state_dict.keys()  )

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

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

					_snake_case													=			config.model.params.first_stage_config.params
					_snake_case													=			config.model.params.unet_config.params

					_snake_case													=			VQModel(**UpperCamelCase__  ).eval()
					vqvae.load_state_dict(UpperCamelCase__  )

					_snake_case													=			UNetLDMModel(**UpperCamelCase__  ).eval()
					unet.load_state_dict(UpperCamelCase__  )

					_snake_case													=			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__    ,      )

					_snake_case													=			LDMPipeline(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )
					pipeline.save_pretrained(UpperCamelCase__  )


if __name__ == "__main__":
					UpperCAmelCase_ 							=       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_ 							=       parser.parse_args()

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



