






from dataclasses import asdict, dataclass
from typing import Optional

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


lowerCAmelCase_           = logging.get_logger(__name__)

# TODO Update this
lowerCAmelCase_           = {
    '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      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   "esm"
		def __init__(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Any=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=None						,_SCREAMING_SNAKE_CASE		:       Optional[int]=7_6_8						,_SCREAMING_SNAKE_CASE		:       Optional[int]=1_2						,_SCREAMING_SNAKE_CASE		:       Dict=1_2						,_SCREAMING_SNAKE_CASE		:       Optional[int]=3_0_7_2						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=0.1						,_SCREAMING_SNAKE_CASE		:       str=0.1						,_SCREAMING_SNAKE_CASE		:       int=1_0_2_6						,_SCREAMING_SNAKE_CASE		:       Dict=0.02						,_SCREAMING_SNAKE_CASE		:       Tuple=1E-12						,_SCREAMING_SNAKE_CASE		:       Any="absolute"						,_SCREAMING_SNAKE_CASE		:       Tuple=True						,_SCREAMING_SNAKE_CASE		:       Dict=None						,_SCREAMING_SNAKE_CASE		:       int=False						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=False						,_SCREAMING_SNAKE_CASE		:       Dict=None						,_SCREAMING_SNAKE_CASE		:       List[Any]=None						,**_SCREAMING_SNAKE_CASE		:       List[str]						,)    ->   Optional[int]:


						'''simple docstring'''
						super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,mask_token_id=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)

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







		def       A(  self		:       Tuple		)    ->   int:


						'''simple docstring'''
						A	 =      super().to_dict()
						if isinstance(self.esmfold_config						,_SCREAMING_SNAKE_CASE		):
										A	 =      self.esmfold_config.to_dict()
						return output


@dataclass
class      UpperCamelCase :







		"""simple docstring"""



		snake_case    =   None
		snake_case    =   True
		snake_case    =   False
		snake_case    =   False
		snake_case    =   False
		snake_case    =   0

		snake_case    =   True
		snake_case    =   False

		snake_case    =   1_2_8
		snake_case    =   None
		def       A(  self		:       Tuple		)    ->   Any:


						'''simple docstring'''
						if self.trunk is None:
										A	 =      TrunkConfig()
						elif isinstance(self.trunk						,_SCREAMING_SNAKE_CASE		):
										A	 =      TrunkConfig(**self.trunk		)







		def       A(  self		:       Optional[int]		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      asdict(self		)
						A	 =      self.trunk.to_dict()
						return output


@dataclass
class      UpperCamelCase :







		"""simple docstring"""



		snake_case    =   4_8
		snake_case    =   1_0_2_4
		snake_case    =   1_2_8
		snake_case    =   3_2
		snake_case    =   3_2
		snake_case    =   3_2
		snake_case    =   0
		snake_case    =   0
		snake_case    =   False
		snake_case    =   4
		snake_case    =   1_2_8
		snake_case    =   None
		def       A(  self		:       str		)    ->   Tuple:


						'''simple docstring'''
						if self.structure_module is None:
										A	 =      StructureModuleConfig()
						elif isinstance(self.structure_module						,_SCREAMING_SNAKE_CASE		):
										A	 =      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}.'		)

						A	 =      self.sequence_state_dim // self.sequence_head_width
						A	 =      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       A(  self		:       Union[str, Any]		)    ->   str:


						'''simple docstring'''
						A	 =      asdict(self		)
						A	 =      self.structure_module.to_dict()
						return output


@dataclass
class      UpperCamelCase :







		"""simple docstring"""



		snake_case    =   3_8_4
		snake_case    =   1_2_8
		snake_case    =   1_6
		snake_case    =   1_2_8
		snake_case    =   1_2
		snake_case    =   4
		snake_case    =   8
		snake_case    =   0.1
		snake_case    =   8
		snake_case    =   1
		snake_case    =   2
		snake_case    =   7
		snake_case    =   1_0
		snake_case    =   1E-8
		snake_case    =   1E5
		def       A(  self		:       Tuple		)    ->   Optional[Any]:


						'''simple docstring'''
						return asdict(self		)







def      snake_case		(							):
				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>",
				)













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				if "model" in orig_key:
								A	 =      orig_key.replace('model.',						''   )
				if "norm1" in orig_key:
								A	 =      orig_key.replace('norm1',						'attention.output.LayerNorm'   )
				if "norm2" in orig_key:
								A	 =      orig_key.replace('norm2',						'output.LayerNorm'   )
				if "norm" in orig_key:
								A	 =      orig_key.replace('norm',						'LayerNorm'   )
				if "transformer" in orig_key:
								A	 =      orig_key.split('.'   )[0].split('_'   )[-1]
								A	 =      orig_key.replace(f'transformer_{layer_num}',						f'encoder.layer.{layer_num}'   )
				if "mha.attn" in orig_key:
								A	 =      orig_key.replace('mha.attn',						'attention.self'   )
				if "mha" in orig_key:
								A	 =      orig_key.replace('mha',						'attention'   )
				if "W_q" in orig_key:
								A	 =      orig_key.replace('W_q',						'self.query'   )
				if "W_k" in orig_key:
								A	 =      orig_key.replace('W_k',						'self.key'   )
				if "W_v" in orig_key:
								A	 =      orig_key.replace('W_v',						'self.value'   )
				if "ff1" in orig_key:
								A	 =      orig_key.replace('ff1',						'intermediate.dense'   )
				if "ff2" in orig_key:
								A	 =      orig_key.replace('ff2',						'output.dense'   )
				if "ff" in orig_key:
								A	 =      orig_key.replace('ff',						'output.dense'   )
				if "mlm_class" in orig_key:
								A	 =      orig_key.replace('mlm.mlm_class',						'cls.predictions.decoder'   )
				if "mlm" in orig_key:
								A	 =      orig_key.replace('mlm',						'cls.predictions.transform'   )
				if "cls" not in orig_key:
								A	 =      'yoso.' + orig_key

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      convert_checkpoint_helper(config.max_position_embeddings,						UpperCAmelCase   )

				print(model.load_state_dict(UpperCAmelCase   )   )
				model.eval()
				model.save_pretrained(UpperCAmelCase   )

				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





