






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       UNetaDModel(**_lowercase       )
					_A									=       checkpoint['''all_modules.0.W'''].data
					_A									=       checkpoint['''all_modules.0.W'''].data
					_A									=       checkpoint['''all_modules.1.weight'''].data
					_A									=       checkpoint['''all_modules.1.bias'''].data

					_A									=       checkpoint['''all_modules.2.weight'''].data
					_A									=       checkpoint['''all_modules.2.bias'''].data

					_A									=       checkpoint['''all_modules.3.weight'''].data
					_A									=       checkpoint['''all_modules.3.bias'''].data

					_A									=       checkpoint[list(checkpoint.keys()       )[-4]].data
					_A									=       checkpoint[list(checkpoint.keys()       )[-3]].data
					_A									=       checkpoint[list(checkpoint.keys()       )[-2]].data
					_A									=       checkpoint[list(checkpoint.keys()       )[-1]].data

					_A									=       4

					def set_attention_weights(_lowercase    ,	_lowercase    ,	_lowercase       ):
										_A									=       old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
										_A									=       old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
										_A									=       old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

										_A									=       old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
										_A									=       old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
										_A									=       old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

										_A									=       old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
										_A									=       old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

										_A									=       old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
										_A									=       old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

					def set_resnet_weights(_lowercase    ,	_lowercase    ,	_lowercase       ):
										_A									=       old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
										_A									=       old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
										_A									=       old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
										_A									=       old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

										_A									=       old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
										_A									=       old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
										_A									=       old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
										_A									=       old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

										_A									=       old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
										_A									=       old_checkpoint[f"""all_modules.{index}.Dense_0.bias"""].data

										if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
															_A									=       old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
															_A									=       old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

					for i, block in enumerate(new_model_architecture.downsample_blocks       ):
										_A									=       hasattr(_lowercase    ,	'''attentions'''       )
										for j in range(len(block.resnets       )       ):
															set_resnet_weights(block.resnets[j]    ,	_lowercase    ,	_lowercase       )
															module_index += 1
															if has_attentions:
																				set_attention_weights(block.attentions[j]    ,	_lowercase    ,	_lowercase       )
																				module_index += 1

										if hasattr(_lowercase    ,	'''downsamplers'''       ) and block.downsamplers is not None:
															set_resnet_weights(block.resnet_down    ,	_lowercase    ,	_lowercase       )
															module_index += 1
															_A									=       checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
															_A									=       checkpoint[f"""all_modules.{module_index}.Conv_0.bias"""].data
															module_index += 1

					set_resnet_weights(new_model_architecture.mid_block.resnets[0]    ,	_lowercase    ,	_lowercase       )
					module_index += 1
					set_attention_weights(new_model_architecture.mid_block.attentions[0]    ,	_lowercase    ,	_lowercase       )
					module_index += 1
					set_resnet_weights(new_model_architecture.mid_block.resnets[1]    ,	_lowercase    ,	_lowercase       )
					module_index += 1

					for i, block in enumerate(new_model_architecture.up_blocks       ):
										_A									=       hasattr(_lowercase    ,	'''attentions'''       )
										for j in range(len(block.resnets       )       ):
															set_resnet_weights(block.resnets[j]    ,	_lowercase    ,	_lowercase       )
															module_index += 1
										if has_attentions:
															set_attention_weights(
															    block.attentions[0]    ,	_lowercase    ,	_lowercase       )  # why can there only be a single attention layer for up?
															module_index += 1

										if hasattr(_lowercase    ,	'''resnet_up'''       ) and block.resnet_up is not None:
															_A									=       checkpoint[f"""all_modules.{module_index}.weight"""].data
															_A									=       checkpoint[f"""all_modules.{module_index}.bias"""].data
															module_index += 1
															_A									=       checkpoint[f"""all_modules.{module_index}.weight"""].data
															_A									=       checkpoint[f"""all_modules.{module_index}.bias"""].data
															module_index += 1
															set_resnet_weights(block.resnet_up    ,	_lowercase    ,	_lowercase       )
															module_index += 1

					_A									=       checkpoint[f"""all_modules.{module_index}.weight"""].data
					_A									=       checkpoint[f"""all_modules.{module_index}.bias"""].data
					module_index += 1
					_A									=       checkpoint[f"""all_modules.{module_index}.weight"""].data
					_A									=       checkpoint[f"""all_modules.{module_index}.bias"""].data

					return new_model_architecture.state_dict()


if __name__ == "__main__":
				__A          = argparse.ArgumentParser()

				parser.add_argument(
				    '--checkpoint_path',
				    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
				    type=str,
				    required=False,
				    help='Path to the checkpoint to convert.',
				)

				parser.add_argument(
				    '--config_file',
				    default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
				    type=str,
				    required=False,
				    help='The config json file corresponding to the architecture.',
				)

				parser.add_argument(
				    '--dump_path',
				    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
				    type=str,
				    required=False,
				    help='Path to the output model.',
				)

				__A          = parser.parse_args()

				__A          = torch.load(args.checkpoint_path, map_location='cpu')

				with open(args.config_file) as f:
								__A          = json.loads(f.read())

				__A          = convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

				if "sde" in config:
								del config["sde"]

				__A          = UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								__A          = ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

								__A          = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)









import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Tuple					  =  ["UtteranceLevel"]




@torch.no_grad()
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[str]        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"				)

				_lowerCAmelCase			:			List[str]        =					checkpoint["""Downstream"""]

				_lowerCAmelCase			:			Optional[int]        =					HubertConfig.from_pretrained(UpperCamelCase_				)
				_lowerCAmelCase			:			str        =					HubertForSequenceClassification.from_pretrained(UpperCamelCase_					,			config=UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					WavaVecaFeatureExtractor.from_pretrained(
				    UpperCamelCase_					,			return_attention_mask=UpperCamelCase_					,			do_normalize=UpperCamelCase_				)

				if hf_congfig.use_weighted_layer_sum:
								_lowerCAmelCase			:			int        =					checkpoint["""Featurizer"""]["""weights"""]

				_lowerCAmelCase			:			Optional[int]        =					downstream_dict["""projector.weight"""]
				_lowerCAmelCase			:			int        =					downstream_dict["""projector.bias"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(UpperCamelCase_				)
				hf_model.save_pretrained(UpperCamelCase_				)


if __name__ == "__main__":
	_lowerCamelCase					:     Any					  =  argparse.ArgumentParser()
	parser.add_argument(
	    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
	)
	parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
	parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
	parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
	_lowerCamelCase					:     Dict					  =  parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

