





from dataclasses import asdict, dataclass
from typing import Optional

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


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

# TODO Update this
__lowerCamelCase :  Optional[int]										=  {
    """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       A__     ( snake_case__    ):
      _UpperCAmelCase			:Optional[int]							=     'esm'





      def __init__(				self ,							A_=None ,							A_=None ,							A_=None ,							A_=768 ,							A_=12 ,							A_=12 ,							A_=3072 ,							A_=0.1 ,							A_=0.1 ,							A_=1026 ,							A_=0.02 ,							A_=1e-12 ,							A_="absolute" ,							A_=True ,							A_=None ,							A_=False ,							A_=False ,							A_=None ,							A_=None ,							**A_ ,							):
       '''simple docstring'''




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

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     super().to_dict()
       if isinstance(self.esmfold_config ,							lowercase_     ):
        UpperCamelCase			:      Tuple 			=     self.esmfold_config.to_dict()
       return output



@dataclass
class       A__     :
      _UpperCAmelCase			:int							=     None
      _UpperCAmelCase			:int							=     True
      _UpperCAmelCase			:Optional[int]							=     False
      _UpperCAmelCase			:Optional[int]							=     False
      _UpperCAmelCase			:Optional[Any]							=     False
      _UpperCAmelCase			:List[Any]							=     0

      _UpperCAmelCase			:List[Any]							=     True
      _UpperCAmelCase			:List[str]							=     False

      _UpperCAmelCase			:List[Any]							=     1_2_8
      _UpperCAmelCase			:int							=     None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.trunk is None:
        UpperCamelCase			:      List[str] 			=     TrunkConfig()
       elif isinstance(self.trunk ,							lowercase_     ):
        UpperCamelCase			:      str 			=     TrunkConfig(**self.trunk     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     asdict(self     )
       UpperCamelCase			:      Optional[Any] 			=     self.trunk.to_dict()
       return output



@dataclass
class       A__     :
      _UpperCAmelCase			:Any							=     4_8
      _UpperCAmelCase			:int							=     1_0_2_4
      _UpperCAmelCase			:str							=     1_2_8
      _UpperCAmelCase			:str							=     3_2
      _UpperCAmelCase			:Any							=     3_2
      _UpperCAmelCase			:Optional[int]							=     3_2
      _UpperCAmelCase			:Dict							=     0
      _UpperCAmelCase			:str							=     0
      _UpperCAmelCase			:Optional[Any]							=     False
      _UpperCAmelCase			:str							=     4
      _UpperCAmelCase			:Optional[Any]							=     1_2_8
      _UpperCAmelCase			:Dict							=     None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.structure_module is None:
        UpperCamelCase			:      str 			=     StructureModuleConfig()
       elif isinstance(self.structure_module ,							lowercase_     ):
        UpperCamelCase			:      List[Any] 			=     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			:      str 			=     self.sequence_state_dim // self.sequence_head_width
       UpperCamelCase			:      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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     asdict(self     )
       UpperCamelCase			:      int 			=     self.structure_module.to_dict()
       return output



@dataclass
class       A__     :
      _UpperCAmelCase			:Tuple							=     3_8_4
      _UpperCAmelCase			:List[Any]							=     1_2_8
      _UpperCAmelCase			:int							=     1_6
      _UpperCAmelCase			:Tuple							=     1_2_8
      _UpperCAmelCase			:List[Any]							=     1_2
      _UpperCAmelCase			:List[str]							=     4
      _UpperCAmelCase			:Tuple							=     8
      _UpperCAmelCase			:Optional[int]							=     0.1
      _UpperCAmelCase			:Optional[int]							=     8
      _UpperCAmelCase			:Dict							=     1
      _UpperCAmelCase			:Tuple							=     2
      _UpperCAmelCase			:List[str]							=     7
      _UpperCAmelCase			:Tuple							=     1_0
      _UpperCAmelCase			:str							=     1e-8
      _UpperCAmelCase			:Union[str, Any]							=     1e5





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return asdict(self     )



def 	A_       (  )		->				Union[str, Any]:
 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>",
 )







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__lowerCamelCase :  Dict										=  logging.get_logger(__name__)

__lowerCamelCase :  Union[str, Any]										=  {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__lowerCamelCase :  Dict										=  {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__lowerCamelCase :  Tuple										=  {
    """facebook/blenderbot_small-90M""": 512,
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Union[str, Any]							=     VOCAB_FILES_NAMES
      _UpperCAmelCase			:Dict							=     PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase			:List[Any]							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase			:Optional[Any]							=     BlenderbotSmallTokenizer





      def __init__(				self ,							A_=None ,							A_=None ,							A_="<|endoftext|>" ,							A_="<|endoftext|>" ,							A_="<|endoftext|>" ,							A_=False ,							A_=True ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           ByteLevelBPETokenizer(
               vocab=A_ ,							merges=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_ ,							) ,							bos_token=A_ ,							eos_token=A_ ,							unk_token=A_ ,							**A_ ,							)
       UpperCamelCase			:      Union[str, Any] 			=     add_prefix_space





      def  __UpperCamelCase(				self ,							A_ ,							A_=None     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
        return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     [self.sep_token_id]
       UpperCamelCase			:      int 			=     [self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep     ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]
