
"""simple docstring"""

from dataclasses import asdict, dataclass
from typing import Optional

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


UpperCamelCase     :    Union[str, Any]       =			logging.get_logger(__name__)

# TODO Update this
UpperCamelCase     :    List[Any]       =			{
    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
    # See all ESM models at https://huggingface.co/models?filter=esm
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"esm"





     def __init__(     self     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=1026     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=1E-12     ,					__UpperCAmelCase="absolute"     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(pad_token_id=__UpperCAmelCase     ,					mask_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase				)

      __UpperCamelCase      =	vocab_size
      __UpperCamelCase      =	hidden_size
      __UpperCamelCase      =	num_hidden_layers
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	intermediate_size
      __UpperCamelCase      =	hidden_dropout_prob
      __UpperCamelCase      =	attention_probs_dropout_prob
      __UpperCamelCase      =	max_position_embeddings
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	layer_norm_eps
      __UpperCamelCase      =	position_embedding_type
      __UpperCamelCase      =	use_cache
      __UpperCamelCase      =	emb_layer_norm_before
      __UpperCamelCase      =	token_dropout
      __UpperCamelCase      =	is_folding_model
      if is_folding_model:
       if esmfold_config is None:
        logger.info('No esmfold_config supplied for folding model, using default values.'				)
        __UpperCamelCase      =	EsmFoldConfig()
       elif isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				):
        __UpperCamelCase      =	EsmFoldConfig(**__UpperCAmelCase				)
       __UpperCamelCase      =	esmfold_config
       if vocab_list is None:
        logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'				)
        __UpperCamelCase      =	get_default_vocab_list()
       else:
        __UpperCamelCase      =	vocab_list
      else:
       __UpperCamelCase      =	None
       __UpperCamelCase      =	None
      if self.esmfold_config is not None and getattr(self.esmfold_config     ,					'use_esm_attn_map'     ,					__UpperCAmelCase				):
       raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	super().to_dict()
      if isinstance(self.esmfold_config     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	self.esmfold_config.to_dict()
      return output


@dataclass
class __lowerCAmelCase		:
     lowercase						       =						None
     lowercase						       =						True
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						0

     lowercase						       =						True
     lowercase						       =						False

     lowercase						       =						128
     lowercase						       =						None





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      if self.trunk is None:
       __UpperCamelCase      =	TrunkConfig()
      elif isinstance(self.trunk     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	TrunkConfig(**self.trunk				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	asdict(self				)
      __UpperCamelCase      =	self.trunk.to_dict()
      return output


@dataclass
class __lowerCAmelCase		:
     lowercase						       =						48
     lowercase						       =						1_024
     lowercase						       =						128
     lowercase						       =						32
     lowercase						       =						32
     lowercase						       =						32
     lowercase						       =						0
     lowercase						       =						0
     lowercase						       =						False
     lowercase						       =						4
     lowercase						       =						128
     lowercase						       =						None





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      if self.structure_module is None:
       __UpperCamelCase      =	StructureModuleConfig()
      elif isinstance(self.structure_module     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	StructureModuleConfig(**self.structure_module				)

      if self.max_recycles <= 0:
       raise ValueError(F'`max_recycles` should be positive, got {self.max_recycles}.'				)
      if self.sequence_state_dim % self.sequence_state_dim != 0:
       raise ValueError(
           '`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
           F' {self.sequence_state_dim} and {self.sequence_state_dim}.'				)
      if self.pairwise_state_dim % self.pairwise_state_dim != 0:
       raise ValueError(
           '`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
           F' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'				)

      __UpperCamelCase      =	self.sequence_state_dim // self.sequence_head_width
      __UpperCamelCase      =	self.pairwise_state_dim // self.pairwise_head_width

      if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
       raise ValueError(
           '`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
           F' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'				)
      if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
       raise ValueError(
           '`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
           F' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'				)
      if self.pairwise_state_dim % 2 != 0:
       raise ValueError(F'`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'				)

      if self.dropout >= 0.4:
       raise ValueError(F'`dropout` should not be greater than 0.4, got {self.dropout}.'				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	asdict(self				)
      __UpperCamelCase      =	self.structure_module.to_dict()
      return output


@dataclass
class __lowerCAmelCase		:
     lowercase						       =						384
     lowercase						       =						128
     lowercase						       =						16
     lowercase						       =						128
     lowercase						       =						12
     lowercase						       =						4
     lowercase						       =						8
     lowercase						       =						0.1
     lowercase						       =						8
     lowercase						       =						1
     lowercase						       =						2
     lowercase						       =						7
     lowercase						       =						10
     lowercase						       =						1e-8
     lowercase						       =						1e5





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return asdict(self				)






def 							A						(    )				->     Optional[int]:
 return (
     "<cls>",
     "<pad>",
     "<eos>",
     "<unk>",
     "L",
     "A",
     "G",
     "V",
     "S",
     "E",
     "R",
     "T",
     "I",
     "D",
     "P",
     "K",
     "Q",
     "N",
     "F",
     "Y",
     "M",
     "H",
     "W",
     "C",
     "X",
     "B",
     "U",
     "Z",
     "O",
     ".",
     "-",
     "<null_1>",
     "<mask>",
 )





"""simple docstring"""

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 							A						(    snake_case				:Dict    , snake_case				:Optional[Any]    , snake_case				:List[Any]		)				->     Optional[Any]:
 __UpperCamelCase      =	OmegaConf.load(snake_case		)
 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)['model']
 __UpperCamelCase      =	list(state_dict.keys()		)

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

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

 __UpperCamelCase      =	config.model.params.first_stage_config.params
 __UpperCamelCase      =	config.model.params.unet_config.params

 __UpperCamelCase      =	VQModel(**snake_case		).eval()
 vqvae.load_state_dict(snake_case		)

 __UpperCamelCase      =	UNetLDMModel(**snake_case		).eval()
 unet.load_state_dict(snake_case		)

 __UpperCamelCase      =	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=snake_case    , )

 __UpperCamelCase      =	LDMPipeline(snake_case    , snake_case    , snake_case		)
 pipeline.save_pretrained(snake_case		)


if __name__ == "__main__":
      UpperCamelCase     :    Dict       =			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     :    Optional[int]       =			parser.parse_args()

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



