





'''simple docstring'''

import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


a   	=    logging.get_logger(__name__)







@dataclass
class __a			:
   __UpperCamelCase :			str						   =						field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()      )}      )
   __UpperCamelCase :			str						   =						field(
       metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}      )
   __UpperCamelCase :			int						   =						field(
       default=128, metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       }, )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Overwrite the cached training and evaluation sets'}      )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.task_name.lower()







class __a			( _snake_case      ):
   __UpperCamelCase :			Union[str, Any]						   =						'train'
   __UpperCamelCase :			Union[str, Any]						   =						'dev'
   __UpperCamelCase :			List[str]						   =						'test'







class __a			( _snake_case      ):
   __UpperCamelCase :			GlueDataTrainingArguments
   __UpperCamelCase :			str
   __UpperCamelCase :			List[InputFeatures]





   def __init__(       self					:      List[str]					,lowerCamelCase					:      GlueDataTrainingArguments					,lowerCamelCase					:      PreTrainedTokenizerBase					,lowerCamelCase					:      Optional[int] = None					,lowerCamelCase					:      Union[str, Split] = Split.train					,lowerCamelCase					:      Optional[str] = None					,):

      '''simple docstring'''

      warnings.warn(
          """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
          """library. You can have a look at this example script for pointers: """
          """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""					,lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							args
      __SCREAMING_SNAKE_CASE   				=							glue_processors[args.task_name]()
      __SCREAMING_SNAKE_CASE   				=							glue_output_modes[args.task_name]
      if isinstance(lowerCamelCase					,lowerCamelCase     ):
         try:
            __SCREAMING_SNAKE_CASE   				=							Split[mode]
         except KeyError:
            raise KeyError("""mode is not a valid split name"""     )
        # Load data features from cache or dataset file
      __SCREAMING_SNAKE_CASE   				=							os.path.join(
          cache_dir if cache_dir is not None else args.data_dir					,f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""					,)
      __SCREAMING_SNAKE_CASE   				=							self.processor.get_labels()
      if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
          "RobertaTokenizer",
          "RobertaTokenizerFast",
          "XLMRobertaTokenizer",
          "BartTokenizer",
          "BartTokenizerFast",
      ):
         # HACK(label indices are swapped in RoBERTa pretrained model)
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							label_list[2], label_list[1]
      __SCREAMING_SNAKE_CASE   				=							label_list

      # Make sure only the first process in distributed training processes the dataset,
      # and the others will use the cache.
      __SCREAMING_SNAKE_CASE   				=							cached_features_file + """.lock"""
      with FileLock(lowerCamelCase     ):
         if os.path.exists(lowerCamelCase     ) and not args.overwrite_cache:
            __SCREAMING_SNAKE_CASE   				=							time.time()
            __SCREAMING_SNAKE_CASE   				=							torch.load(lowerCamelCase     )
            logger.info(
                f"""Loading features from cached file {cached_features_file} [took %.3f s]"""					,time.time() - start     )
         else:
            logger.info(f"""Creating features from dataset file at {args.data_dir}"""     )

            if mode == Split.dev:
               __SCREAMING_SNAKE_CASE   				=							self.processor.get_dev_examples(args.data_dir     )
            elif mode == Split.test:
               __SCREAMING_SNAKE_CASE   				=							self.processor.get_test_examples(args.data_dir     )
            else:
               __SCREAMING_SNAKE_CASE   				=							self.processor.get_train_examples(args.data_dir     )
            if limit_length is not None:
               __SCREAMING_SNAKE_CASE   				=							examples[:limit_length]
            __SCREAMING_SNAKE_CASE   				=							glue_convert_examples_to_features(
                lowerCamelCase					,lowerCamelCase					,max_length=args.max_seq_length					,label_list=lowerCamelCase					,output_mode=self.output_mode					,)
            __SCREAMING_SNAKE_CASE   				=							time.time()
            torch.save(self.features					,lowerCamelCase     )
            # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
            logger.info(
                f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""     )





   def __len__(       self					:      Optional[Any]     ):

      '''simple docstring'''

      return len(self.features     )





   def __getitem__(       self					:      Tuple					,lowerCamelCase					:      Dict     ):

      '''simple docstring'''

      return self.features[i]





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      return self.label_list












'''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 __a			( _snake_case      ):
   __UpperCamelCase :			str						   =						'esm'





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

      '''simple docstring'''

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

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





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							super().to_dict()
      if isinstance(self.esmfold_config					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							self.esmfold_config.to_dict()
      return output







@dataclass
class __a			:
   __UpperCamelCase :			str						   =						None
   __UpperCamelCase :			bool						   =						True
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			float						   =						0

   __UpperCamelCase :			bool						   =						True
   __UpperCamelCase :			bool						   =						False

   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			"TrunkConfig"						   =						None





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      if self.trunk is None:
         __SCREAMING_SNAKE_CASE   				=							TrunkConfig()
      elif isinstance(self.trunk					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							TrunkConfig(**self.trunk     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							asdict(self     )
      __SCREAMING_SNAKE_CASE   				=							self.trunk.to_dict()
      return output







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						48
   __UpperCamelCase :			int						   =						1024
   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			int						   =						32
   __UpperCamelCase :			int						   =						32
   __UpperCamelCase :			int						   =						32
   __UpperCamelCase :			float						   =						0
   __UpperCamelCase :			float						   =						0
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			int						   =						4
   __UpperCamelCase :			Optional[int]						   =						128
   __UpperCamelCase :			"StructureModuleConfig"						   =						None





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      if self.structure_module is None:
         __SCREAMING_SNAKE_CASE   				=							StructureModuleConfig()
      elif isinstance(self.structure_module					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							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}."""     )

      __SCREAMING_SNAKE_CASE   				=							self.sequence_state_dim // self.sequence_head_width
      __SCREAMING_SNAKE_CASE   				=							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					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							asdict(self     )
      __SCREAMING_SNAKE_CASE   				=							self.structure_module.to_dict()
      return output







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						384
   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			int						   =						16
   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			int						   =						12
   __UpperCamelCase :			int						   =						4
   __UpperCamelCase :			int						   =						8
   __UpperCamelCase :			float						   =						0.1
   __UpperCamelCase :			int						   =						8
   __UpperCamelCase :			int						   =						1
   __UpperCamelCase :			int						   =						2
   __UpperCamelCase :			int						   =						7
   __UpperCamelCase :			int						   =						10
   __UpperCamelCase :			float						   =						1E-8
   __UpperCamelCase :			float						   =						1E5





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      return asdict(self     )





def     __magic_name__							(				)    ->       Dict:

   '''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>",
   )





