
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def    a      ( a		,						a		,						a	)     ->Any:




			'''simple docstring'''


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

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

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

			SCREAMING_SNAKE_CASE     						= config.model.params.first_stage_config.params
			SCREAMING_SNAKE_CASE     						= config.model.params.unet_config.params

			SCREAMING_SNAKE_CASE     						= VQModel(**a	).eval()
			vqvae.load_state_dict(a	)

			SCREAMING_SNAKE_CASE     						= UNetLDMModel(**a	).eval()
			unet.load_state_dict(a	)

			SCREAMING_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=a		,						)

			SCREAMING_SNAKE_CASE     						= LDMPipeline(a		,						a		,						a	)
			pipeline.save_pretrained(a	)


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

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

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch





@require_torch
@require_optimum
@slow
class     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained(lowercase			)
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= tokenizer('''This is me'''   ,     return_tensors='''pt'''			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				SCREAMING_SNAKE_CASE     						= model.generate(**lowercase			)

				SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

							self.assertFalse(
							    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

							SCREAMING_SNAKE_CASE     						= model_reloaded.generate(**lowercase			)
							self.assertTrue(torch.allclose(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
							with self.assertRaises(lowercase			):
										model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()
							model.save_pretrained(lowercase			)