


'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xlnet import XLNetTokenizer
else:
							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		None


__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE					:				List[str]									=		{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

__SCREAMING_SNAKE_CASE					:				Tuple									=		"""▁"""

# Segments (not really needed)
__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		0
__SCREAMING_SNAKE_CASE					:				Dict									=		1
__SCREAMING_SNAKE_CASE					:				Any									=		2
__SCREAMING_SNAKE_CASE					:				Dict									=		3
__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		4






class 					__lowerCAmelCase   (   _UpperCAmelCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[int]   						=VOCAB_FILES_NAMES
						_UpperCAmelCase		:       List[str]   						=PRETRAINED_VOCAB_FILES_MAP
						_UpperCAmelCase		:       Dict   						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCAmelCase		:       List[Any]   						='left'
						_UpperCAmelCase		:       Optional[Any]   						=XLNetTokenizer






						def __init__(  self							:      Any ,  lowerCAmelCase							:      int=None ,  lowerCAmelCase							:      str=None ,  lowerCAmelCase							:      Union[str, Any]=False ,  lowerCAmelCase							:      str=True ,  lowerCAmelCase							:      List[Any]=False ,  lowerCAmelCase							:      List[Any]="<s>" ,  lowerCAmelCase							:      str="</s>" ,  lowerCAmelCase							:      Any="<unk>" ,  lowerCAmelCase							:      List[str]="<sep>" ,  lowerCAmelCase							:      str="<pad>" ,  lowerCAmelCase							:      str="<cls>" ,  lowerCAmelCase							:      List[Any]="<mask>" ,  lowerCAmelCase							:      Union[str, Any]=["<eop>", "<eod>"] ,  **lowerCAmelCase							:      List[Any] ,  ):
													# Mask token behave like a normal word, i.e. include the space before it
													A_			    =		AddedToken(SCREAMING_SNAKE_CASE_ ,  lstrip=SCREAMING_SNAKE_CASE_ ,  rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_ ,  SCREAMING_SNAKE_CASE_	) else mask_token

													super().__init__(
													    vocab_file=SCREAMING_SNAKE_CASE_ ,  tokenizer_file=SCREAMING_SNAKE_CASE_ ,  do_lower_case=SCREAMING_SNAKE_CASE_ ,  remove_space=SCREAMING_SNAKE_CASE_ ,  keep_accents=SCREAMING_SNAKE_CASE_ ,  bos_token=SCREAMING_SNAKE_CASE_ ,  eos_token=SCREAMING_SNAKE_CASE_ ,  unk_token=SCREAMING_SNAKE_CASE_ ,  sep_token=SCREAMING_SNAKE_CASE_ ,  pad_token=SCREAMING_SNAKE_CASE_ ,  cls_token=SCREAMING_SNAKE_CASE_ ,  mask_token=SCREAMING_SNAKE_CASE_ ,  additional_special_tokens=SCREAMING_SNAKE_CASE_ ,  **SCREAMING_SNAKE_CASE_ ,  )

													A_			    =		3
													A_			    =		do_lower_case
													A_			    =		remove_space
													A_			    =		keep_accents
													A_			    =		vocab_file
													A_			    =		False if not self.vocab_file else True






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      str = None	):
													A_			    =		[self.sep_token_id]
													A_			    =		[self.cls_token_id]
													if token_ids_a is None:
																				return token_ids_a + sep + cls
													return token_ids_a + sep + token_ids_a + sep + cls






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      List[Any] = None	):
													A_			    =		[self.sep_token_id]
													A_			    =		[2]

													if token_ids_a is None:
																				return len(token_ids_a + sep	) * [0] + cls_segment_id
													return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1] + cls_segment_id






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Tuple = None	):
													if not self.can_save_slow_tokenizer:
																				raise ValueError(
																				    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																				    "tokenizer."	)

													if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
																				logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
																				return
													A_			    =		os.path.join(
													    SCREAMING_SNAKE_CASE_ ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

													if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	):
																				copyfile(self.vocab_file ,  SCREAMING_SNAKE_CASE_	)

													return (out_vocab_file,)









'''simple docstring'''



from dataclasses import asdict, dataclass
from typing import Optional

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


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

# TODO Update this
A__						:   Tuple				=    {
    """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_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Tuple					=     'esm'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=7_68     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=30_72     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10_26     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1E-12     ,	SCREAMING_SNAKE_CASE_="absolute"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	List[str]:
         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_     ,	mask_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						vocab_size
         __lowerCamelCase  :      List[Any]														=						hidden_size
         __lowerCamelCase  :      str														=						num_hidden_layers
         __lowerCamelCase  :      List[str]														=						num_attention_heads
         __lowerCamelCase  :      Any														=						intermediate_size
         __lowerCamelCase  :      Optional[Any]														=						hidden_dropout_prob
         __lowerCamelCase  :      Tuple														=						attention_probs_dropout_prob
         __lowerCamelCase  :      Optional[int]														=						max_position_embeddings
         __lowerCamelCase  :      str														=						initializer_range
         __lowerCamelCase  :      Optional[int]														=						layer_norm_eps
         __lowerCamelCase  :      List[str]														=						position_embedding_type
         __lowerCamelCase  :      int														=						use_cache
         __lowerCamelCase  :      Optional[Any]														=						emb_layer_norm_before
         __lowerCamelCase  :      Optional[Any]														=						token_dropout
         __lowerCamelCase  :      str														=						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  :      Dict														=						EsmFoldConfig()
              elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                   __lowerCamelCase  :      Optional[int]														=						EsmFoldConfig(**SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[Any]														=						esmfold_config
              if vocab_list is None:
                   logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'      )
                   __lowerCamelCase  :      List[str]														=						get_default_vocab_list()
              else:
                   __lowerCamelCase  :      Optional[Any]														=						vocab_list
         else:
              __lowerCamelCase  :      Dict														=						None
              __lowerCamelCase  :      Optional[Any]														=						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  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Any														=						super().to_dict()
         if isinstance(self.esmfold_config     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      int														=						self.esmfold_config.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   str					=     None
    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   float					=     0

    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False

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







    def  lowercase_							(				self      )							->	Any:
         if self.trunk is None:
              __lowerCamelCase  :      List[str]														=						TrunkConfig()
         elif isinstance(self.trunk     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Any														=						TrunkConfig(**self.trunk      )







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Optional[int]														=						asdict(self      )
         __lowerCamelCase  :      str														=						self.trunk.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     4_8
    lowerCamelCase    :   int					=     1_0_2_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   Optional[int]					=     1_2_8
    lowerCamelCase    :   "StructureModuleConfig"					=     None







    def  lowercase_							(				self      )							->	Optional[int]:
         if self.structure_module is None:
              __lowerCamelCase  :      Dict														=						StructureModuleConfig()
         elif isinstance(self.structure_module     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[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}.'      )

         __lowerCamelCase  :      Tuple														=						self.sequence_state_dim // self.sequence_head_width
         __lowerCamelCase  :      str														=						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  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      List[str]														=						asdict(self      )
         __lowerCamelCase  :      int														=						self.structure_module.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     3_8_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_6
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_2
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   float					=     0.1
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   int					=     2
    lowerCamelCase    :   int					=     7
    lowerCamelCase    :   int					=     1_0
    lowerCamelCase    :   float					=     1e-8
    lowerCamelCase    :   float					=     1e5







    def  lowercase_							(				self      )							->	Any:
         return asdict(self      )







def      UpperCAmelCase__   (      )	->						Optional[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>",
     )






