





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case							)				->      Optional[int]:







	'''simple docstring'''







	lowerCamelCase__								=		OmegaConf.load(UpperCAmelCase__							)
	lowerCamelCase__								=		torch.load(UpperCAmelCase__       ,map_location='''cpu'''							)["""model"""]
	lowerCamelCase__								=		list(state_dict.keys()							)

	# extract state_dict for VQVAE
	lowerCamelCase__								=		{}
	lowerCamelCase__								=		"""first_stage_model."""
	for key in keys:
		if key.startswith(UpperCAmelCase__							):
			lowerCamelCase__								=		state_dict[key]

    # extract state_dict for UNetLDM
	lowerCamelCase__								=		{}
	lowerCamelCase__								=		"""model.diffusion_model."""
	for key in keys:
		if key.startswith(UpperCAmelCase__							):
			lowerCamelCase__								=		state_dict[key]

	lowerCamelCase__								=		config.model.params.first_stage_config.params
	lowerCamelCase__								=		config.model.params.unet_config.params

	lowerCamelCase__								=		VQModel(**UpperCAmelCase__							).eval()
	vqvae.load_state_dict(UpperCAmelCase__							)

	lowerCamelCase__								=		UNetLDMModel(**UpperCAmelCase__							).eval()
	unet.load_state_dict(UpperCAmelCase__							)

	lowerCamelCase__								=		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__								=		LDMPipeline(UpperCAmelCase__       ,UpperCAmelCase__       ,UpperCAmelCase__							)
	pipeline.save_pretrained(UpperCAmelCase__							)


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

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




'''simple docstring'''




import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
						import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin





@require_torch
class     __magic_name__						(    unittest.TestCase,     _UpperCAmelCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			List[str]										=       load_tool("""text-to-speech"""    )
										self.tool.setup()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										# SpeechT5 isn't deterministic
										torch.manual_seed(0    )
										lowercase_					:			List[str]										=       self.tool("""hey"""    )
										lowercase_					:			Optional[int]										=       result.to_raw()
										self.assertTrue(
										    torch.allclose(
										        resulting_tensor[:3]				,			torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]    )				,			)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										# SpeechT5 isn't deterministic
										torch.manual_seed(0    )
										lowercase_					:			Union[str, Any]										=       self.tool("""hey"""    )
										lowercase_					:			int										=       result.to_raw()
										self.assertTrue(
										    torch.allclose(
										        resulting_tensor[:3]				,			torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]    )				,			)    )





