





'''simple docstring'''



import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					List[str]			):
			__lowercase			  =							OmegaConf.load(lowerCamelCase_			)
			__lowercase			  =							torch.load(lowerCamelCase_						,    map_location='''cpu'''			)['''model''']
			__lowercase			  =							list(state_dict.keys()			)

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

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

			__lowercase			  =							config.model.params.first_stage_config.params
			__lowercase			  =							config.model.params.unet_config.params

			__lowercase			  =							VQModel(**lowerCamelCase_			).eval()
			vqvae.load_state_dict(lowerCamelCase_			)

			__lowercase			  =							UNetLDMModel(**lowerCamelCase_			).eval()
			unet.load_state_dict(lowerCamelCase_			)

			__lowercase			  =							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=lowerCamelCase_						,    )

			__lowercase			  =							LDMPipeline(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
			pipeline.save_pretrained(lowerCamelCase_			)


if __name__ == "__main__":
						_SCREAMING_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)
						_SCREAMING_SNAKE_CASE   =      parser.parse_args()

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












'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Dict     =    "open-llama"


	def __init__(self       ,_lowerCamelCase=100000       ,_lowerCamelCase=4096       ,_lowerCamelCase=11008       ,_lowerCamelCase=32       ,_lowerCamelCase=32       ,_lowerCamelCase="silu"       ,_lowerCamelCase=2048       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-6       ,_lowerCamelCase=True       ,_lowerCamelCase=0       ,_lowerCamelCase=1       ,_lowerCamelCase=2       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.1       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							vocab_size
				__lowercase			  =							max_position_embeddings
				__lowercase			  =							hidden_size
				__lowercase			  =							intermediate_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							hidden_act
				__lowercase			  =							initializer_range
				__lowercase			  =							rms_norm_eps
				__lowercase			  =							use_cache
				__lowercase			  =							kwargs.pop(
				    '''use_memorry_efficient_attention'''       ,_lowerCamelCase	)
				__lowercase			  =							hidden_dropout_prob
				__lowercase			  =							attention_dropout_prob
				__lowercase			  =							use_stable_embedding
				__lowercase			  =							shared_input_output_embedding
				__lowercase			  =							rope_scaling
				self._rope_scaling_validation()

				super().__init__(
				    pad_token_id=_lowerCamelCase       ,bos_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,tie_word_embeddings=_lowerCamelCase       ,**_lowerCamelCase       ,)


	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				if self.rope_scaling is None:
							return

				if not isinstance(self.rope_scaling       ,_lowerCamelCase	) or len(self.rope_scaling	) != 2:
							raise ValueError(
							    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
							    f"got {self.rope_scaling}"	)
				__lowercase			  =							self.rope_scaling.get('''type'''       ,_lowerCamelCase	)
				__lowercase			  =							self.rope_scaling.get('''factor'''       ,_lowerCamelCase	)
				if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
							raise ValueError(
							    f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"	)
				if rope_scaling_factor is None or not isinstance(_lowerCamelCase       ,_lowerCamelCase	) or rope_scaling_factor <= 1.0:
							raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"	)





