
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional

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


a_											=						logging.get_logger(__name__)

# TODO Update this
a_											=						{
    "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__					):
 UpperCAmelCase_		    =						"""esm"""





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       lowercase_=7_68 ,       lowercase_=12 ,       lowercase_=12 ,       lowercase_=30_72 ,       lowercase_=0.1 ,       lowercase_=0.1 ,       lowercase_=10_26 ,       lowercase_=0.02 ,       lowercase_=1E-12 ,       lowercase_="absolute" ,       lowercase_=True ,       lowercase_=None ,       lowercase_=False ,       lowercase_=False ,       lowercase_=None ,       lowercase_=None ,       **lowercase_ ,       ):
     super().__init__(pad_token_id=lowercase_ ,       mask_token_id=lowercase_ ,       **lowercase_)

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






@dataclass
class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						None
 UpperCAmelCase_		    =						True
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						0

 UpperCAmelCase_		    =						True
 UpperCAmelCase_		    =						False

 UpperCAmelCase_		    =						1_2_8
 UpperCAmelCase_		    =						None





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





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






@dataclass
class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						4_8
 UpperCAmelCase_		    =						1_0_2_4
 UpperCAmelCase_		    =						1_2_8
 UpperCAmelCase_		    =						3_2
 UpperCAmelCase_		    =						3_2
 UpperCAmelCase_		    =						3_2
 UpperCAmelCase_		    =						0
 UpperCAmelCase_		    =						0
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						4
 UpperCAmelCase_		    =						1_2_8
 UpperCAmelCase_		    =						None





 def        snake_case__		(    self):
     if self.structure_module is None:
         snake_case_ :    str     			=						StructureModuleConfig()
     elif isinstance(self.structure_module ,       lowercase_):
         snake_case_ :    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}.')

     snake_case_ :    str     			=						self.sequence_state_dim // self.sequence_head_width
     snake_case_ :    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        snake_case__		(    self):
     snake_case_ :    str     			=						asdict(self)
     snake_case_ :    int     			=						self.structure_module.to_dict()
     return output






@dataclass
class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						3_8_4
 UpperCAmelCase_		    =						1_2_8
 UpperCAmelCase_		    =						1_6
 UpperCAmelCase_		    =						1_2_8
 UpperCAmelCase_		    =						1_2
 UpperCAmelCase_		    =						4
 UpperCAmelCase_		    =						8
 UpperCAmelCase_		    =						0.1
 UpperCAmelCase_		    =						8
 UpperCAmelCase_		    =						1
 UpperCAmelCase_		    =						2
 UpperCAmelCase_		    =						7
 UpperCAmelCase_		    =						1_0
 UpperCAmelCase_		    =						1e-8
 UpperCAmelCase_		    =						1e5





 def        snake_case__		(    self):
     return asdict(self)


def 						UpperCamelCase_     (	):
    """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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""ClapFeatureExtractor"""
 UpperCAmelCase_		    =						("""RobertaTokenizer""", """RobertaTokenizerFast""")





 def __init__(    self ,       lowercase_ ,       lowercase_):
     super().__init__(lowercase_ ,       lowercase_)





 def __call__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       **lowercase_):
     snake_case_ :    Any     			=						kwargs.pop("sampling_rate" ,       lowercase_)

     if text is None and audios is None:
         raise ValueError("You have to specify either text or audios. Both cannot be none.")

     if text is not None:
         snake_case_ :    Optional[int]     			=						self.tokenizer(lowercase_ ,       return_tensors=lowercase_ ,       **lowercase_)

     if audios is not None:
         snake_case_ :    Any     			=						self.feature_extractor(
             lowercase_ ,       sampling_rate=lowercase_ ,       return_tensors=lowercase_ ,       **lowercase_)

     if text is not None and audios is not None:
         snake_case_ :    Dict     			=						audio_features.input_features
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_) ,       tensor_type=lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.batch_decode(*lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.decode(*lowercase_ ,       **lowercase_)





 @property
 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.tokenizer.model_input_names
     snake_case_ :    int     			=						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))




