






'''simple docstring'''







import functools
import operator

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


UpperCamelCase     :   Dict	      =		logging.get_logger(__name__)

UpperCamelCase     :   Optional[Any]	      =		{
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}




class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       'sew'






		def __init__(   self ,_lowerCAmelCase=32 ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=12 ,_lowerCAmelCase=12 ,_lowerCAmelCase=30_72 ,_lowerCAmelCase=2 ,_lowerCAmelCase="gelu" ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase="group" ,_lowerCAmelCase="gelu" ,_lowerCAmelCase=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12) ,_lowerCAmelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,_lowerCAmelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,_lowerCAmelCase=False ,_lowerCAmelCase=1_28 ,_lowerCAmelCase=16 ,_lowerCAmelCase=True ,_lowerCAmelCase=0.05 ,_lowerCAmelCase=10 ,_lowerCAmelCase=2 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=10 ,_lowerCAmelCase=0 ,_lowerCAmelCase="mean" ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=2_56 ,_lowerCAmelCase=0 ,_lowerCAmelCase=1 ,_lowerCAmelCase=2 ,**_lowerCAmelCase ,):
								super().__init__(**_lowerCAmelCase ,pad_token_id=_lowerCAmelCase ,bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase							)
								lowerCamelCase__								=	hidden_size
								lowerCamelCase__								=	feat_extract_norm
								lowerCamelCase__								=	feat_extract_activation
								lowerCamelCase__								=	list(_lowerCAmelCase							)
								lowerCamelCase__								=	list(_lowerCAmelCase							)
								lowerCamelCase__								=	list(_lowerCAmelCase							)
								lowerCamelCase__								=	conv_bias
								lowerCamelCase__								=	num_conv_pos_embeddings
								lowerCamelCase__								=	num_conv_pos_embedding_groups
								lowerCamelCase__								=	len(self.conv_dim							)
								lowerCamelCase__								=	num_hidden_layers
								lowerCamelCase__								=	intermediate_size
								lowerCamelCase__								=	squeeze_factor
								lowerCamelCase__								=	hidden_act
								lowerCamelCase__								=	num_attention_heads
								lowerCamelCase__								=	hidden_dropout
								lowerCamelCase__								=	attention_dropout
								lowerCamelCase__								=	activation_dropout
								lowerCamelCase__								=	feat_proj_dropout
								lowerCamelCase__								=	final_dropout
								lowerCamelCase__								=	layerdrop
								lowerCamelCase__								=	layer_norm_eps
								lowerCamelCase__								=	initializer_range
								lowerCamelCase__								=	vocab_size

								if (
								    (len(self.conv_stride							) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
								    or (len(self.conv_dim							) != self.num_feat_extract_layers)
								):
														raise ValueError(
														    """Configuration for convolutional layers is incorrect."""
														    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
														    F'''but is `len(config.conv_dim) = {len(self.conv_dim							)}`, `len(config.conv_stride)'''
														    F'''= {len(self.conv_stride							)}`, `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								lowerCamelCase__								=	apply_spec_augment
								lowerCamelCase__								=	mask_time_prob
								lowerCamelCase__								=	mask_time_length
								lowerCamelCase__								=	mask_time_min_masks
								lowerCamelCase__								=	mask_feature_prob
								lowerCamelCase__								=	mask_feature_length
								lowerCamelCase__								=	mask_feature_min_masks

								# ctc loss
								lowerCamelCase__								=	ctc_loss_reduction
								lowerCamelCase__								=	ctc_zero_infinity

								# sequence classification
								lowerCamelCase__								=	use_weighted_layer_sum
								lowerCamelCase__								=	classifier_proj_size






		@property
		def       UpperCamelCase_       (   self							):
								return functools.reduce(operator.mul ,self.conv_stride ,1							)





'''simple docstring'''






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def    _snake_case  (	A					,       A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      OmegaConf.load(A			)
							lowerCAmelCase__			  =      torch.load(A					,       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(A			):
																					lowerCAmelCase__			  =      state_dict[key]

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

							lowerCAmelCase__			  =      config.model.params.first_stage_config.params
							lowerCAmelCase__			  =      config.model.params.unet_config.params

							lowerCAmelCase__			  =      VQModel(**A			).eval()
							vqvae.load_state_dict(A			)

							lowerCAmelCase__			  =      UNetLDMModel(**A			).eval()
							unet.load_state_dict(A			)

							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=A					,       )

							lowerCAmelCase__			  =      LDMPipeline(A					,       A					,       A			)
							pipeline.save_pretrained(A			)


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)