


'''simple docstring'''





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Optional[int],_SCREAMING_SNAKE_CASE					: Any,_SCREAMING_SNAKE_CASE					: Optional[int] ):






			"""simple docstring"""




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

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

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

			__A=	config.model.params.first_stage_config.params
			__A=	config.model.params.unet_config.params

			__A=	VQModel(**_SCREAMING_SNAKE_CASE ).eval()
			vqvae.load_state_dict(_SCREAMING_SNAKE_CASE )

			__A=	UNetLDMModel(**_SCREAMING_SNAKE_CASE ).eval()
			unet.load_state_dict(_SCREAMING_SNAKE_CASE )

			__A=	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=_SCREAMING_SNAKE_CASE,)

			__A=	LDMPipeline(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			pipeline.save_pretrained(_SCREAMING_SNAKE_CASE )


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)







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Tuple					      =			'''xlm-roberta-xl'''
			def __init__(  self							:			Union[str, Any]			,    lowerCAmelCase_							:			Tuple=250_880			,    lowerCAmelCase_							:			Optional[int]=2_560			,    lowerCAmelCase_							:			Optional[Any]=36			,    lowerCAmelCase_							:			List[str]=32			,    lowerCAmelCase_							:			Optional[Any]=10_240			,    lowerCAmelCase_							:			Union[str, Any]="gelu"			,    lowerCAmelCase_							:			Optional[Any]=0.1			,    lowerCAmelCase_							:			Optional[Any]=0.1			,    lowerCAmelCase_							:			Optional[int]=514			,    lowerCAmelCase_							:			List[str]=1			,    lowerCAmelCase_							:			Optional[Any]=0.02			,    lowerCAmelCase_							:			List[str]=1E-05			,    lowerCAmelCase_							:			int=1			,    lowerCAmelCase_							:			List[str]=0			,    lowerCAmelCase_							:			List[Any]=2			,    lowerCAmelCase_							:			Optional[int]="absolute"			,    lowerCAmelCase_							:			List[Any]=True			,    lowerCAmelCase_							:			Tuple=None			,    **lowerCAmelCase_							:			List[str]			,    )      ->		str:
						super().__init__(pad_token_id=lowerCAmelCase_			,    bos_token_id=lowerCAmelCase_			,    eos_token_id=lowerCAmelCase_			,    **lowerCAmelCase_	)
						__A=	vocab_size
						__A=	hidden_size
						__A=	num_hidden_layers
						__A=	num_attention_heads
						__A=	hidden_act
						__A=	intermediate_size
						__A=	hidden_dropout_prob
						__A=	attention_probs_dropout_prob
						__A=	max_position_embeddings
						__A=	type_vocab_size
						__A=	initializer_range
						__A=	layer_norm_eps
						__A=	position_embedding_type
						__A=	use_cache
						__A=	classifier_dropout


class 						a__					(					a_   ):





			'''simple docstring'''

			@property
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=	{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=	{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]	)



