






'''simple docstring'''

from dataclasses import asdict, dataclass
from typing import Optional

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


UpperCAmelCase__						:Tuple          =			logging.get_logger(__name__)

# TODO Update this
UpperCAmelCase__						:Union[str, 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 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Any   		=			'esm'




       def __init__(					self      : Any	,    A__      : Dict=None	,    A__      : Any=None	,    A__      : Union[str, Any]=None	,    A__      : List[str]=768	,    A__      : Tuple=12	,    A__      : Union[str, Any]=12	,    A__      : Union[str, Any]=3072	,    A__      : str=0.1	,    A__      : List[str]=0.1	,    A__      : List[str]=1026	,    A__      : str=0.02	,    A__      : Union[str, Any]=1e-1_2	,    A__      : Union[str, Any]="absolute"	,    A__      : Union[str, Any]=True	,    A__      : Optional[Any]=None	,    A__      : Optional[Any]=False	,    A__      : Optional[int]=False	,    A__      : Union[str, Any]=None	,    A__      : Tuple=None	,    **A__      : Union[str, Any]	,    ):


            """simple docstring"""







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

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




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				super().to_dict()
            if isinstance(self.esmfold_config	,    A__					):
                 __lowerCamelCase						:       Any				   =				self.esmfold_config.to_dict()
            return output







@dataclass
class 	SCREAMING_SNAKE_CASE	:
       snake_case__    :     str   		=			None
       snake_case__    :     bool   		=			True
       snake_case__    :     bool   		=			False
       snake_case__    :     bool   		=			False
       snake_case__    :     bool   		=			False
       snake_case__    :     float   		=			0

       snake_case__    :     bool   		=			True
       snake_case__    :     bool   		=			False

       snake_case__    :     int   		=			1_2_8
       snake_case__    :     "TrunkConfig"   		=			None




       def 					a_					(					self      : str					):


            """simple docstring"""







            if self.trunk is None:
                 __lowerCamelCase						:       Tuple				   =				TrunkConfig()
            elif isinstance(self.trunk	,    A__					):
                 __lowerCamelCase						:       Optional[int]				   =				TrunkConfig(**self.trunk					)




       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				asdict(self					)
            __lowerCamelCase						:       Tuple				   =				self.trunk.to_dict()
            return output







@dataclass
class 	SCREAMING_SNAKE_CASE	:
       snake_case__    :     int   		=			4_8
       snake_case__    :     int   		=			1_0_2_4
       snake_case__    :     int   		=			1_2_8
       snake_case__    :     int   		=			3_2
       snake_case__    :     int   		=			3_2
       snake_case__    :     int   		=			3_2
       snake_case__    :     float   		=			0
       snake_case__    :     float   		=			0
       snake_case__    :     bool   		=			False
       snake_case__    :     int   		=			4
       snake_case__    :     Optional[int]   		=			1_2_8
       snake_case__    :     "StructureModuleConfig"   		=			None




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            if self.structure_module is None:
                 __lowerCamelCase						:       str				   =				StructureModuleConfig()
            elif isinstance(self.structure_module	,    A__					):
                 __lowerCamelCase						:       Optional[int]				   =				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}."					)

            __lowerCamelCase						:       List[Any]				   =				self.sequence_state_dim // self.sequence_head_width
            __lowerCamelCase						:       Tuple				   =				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      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				asdict(self					)
            __lowerCamelCase						:       int				   =				self.structure_module.to_dict()
            return output







@dataclass
class 	SCREAMING_SNAKE_CASE	:
       snake_case__    :     int   		=			3_8_4
       snake_case__    :     int   		=			1_2_8
       snake_case__    :     int   		=			1_6
       snake_case__    :     int   		=			1_2_8
       snake_case__    :     int   		=			1_2
       snake_case__    :     int   		=			4
       snake_case__    :     int   		=			8
       snake_case__    :     float   		=			0.1
       snake_case__    :     int   		=			8
       snake_case__    :     int   		=			1
       snake_case__    :     int   		=			2
       snake_case__    :     int   		=			7
       snake_case__    :     int   		=			1_0
       snake_case__    :     float   		=			1E-8
       snake_case__    :     float   		=			1E5




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            return asdict(self					)

def     __lowercase  ()   ->     int:


     """simple docstring"""
     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'''

from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def     __lowercase  ()   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				HfArgumentParser(_lowercase     )
     __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     __lowerCamelCase						:       Dict				   =				TensorFlowBenchmark(args=_lowercase     )
     try:
          __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          __lowerCamelCase						:       Tuple				   =				"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
          __lowerCamelCase						:       List[Any]				   =				""" """.join(str(_lowercase     ).split(""" """     )[:-1]     )
          __lowerCamelCase						:       Tuple				   =				""""""
          __lowerCamelCase						:       List[str]				   =				eval(str(_lowercase     ).split(""" """     )[-1]     )
          __lowerCamelCase						:       Tuple				   =				[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in TensorFlowBenchmark.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]     )
               else:
                    wrong_args.append(_lowercase     )
          if len(_lowercase     ) > 0:
               __lowerCamelCase						:       Tuple				   =				full_error_msg + begin_error_msg + str(_lowercase     )
          raise ValueError(_lowercase     )
     benchmark.run()


if __name__ == "__main__":
   main()



