






import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
SCREAMING_SNAKE_CASE__				     =			logging.getLogger(__name__)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       List[Any]				):
			__a						:  Any									=     np.argmax(lowerCamelCase_   ,  axis=1				)
			return np.sum(outputs == labels				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Tuple				):
			with open(lowerCamelCase_   ,  encoding='utf_8'				) as f:
						__a						:  Optional[Any]									=     csv.reader(lowerCamelCase_				)
						__a						:  Optional[int]									=     []
						next(lowerCamelCase_				)  # skip the first line
						for line in tqdm(lowerCamelCase_				):
									output.append((' '.join(line[1:5]				), line[5], line[6], int(line[-1]				) - 1)				)
			return output

def        UpperCAmelCase__		(						lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       str   ,  lowerCamelCase_       :       Optional[Any]				):
			__a						:  int									=     []
			for dataset in encoded_datasets:
						__a						:  List[Any]									=     len(lowerCamelCase_				)
						__a						:  Optional[Any]									=     np.zeros((n_batch, 2, input_len)   ,  dtype=np.intaa				)
						__a						:  Optional[int]									=     np.zeros((n_batch, 2)   ,  dtype=np.intaa				)
						__a						:  str									=     np.full((n_batch, 2, input_len)   ,  fill_value=-1_0_0   ,  dtype=np.intaa				)
						__a						:  List[Any]									=     np.zeros((n_batch,)   ,  dtype=np.intaa				)
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(lowerCamelCase_				):
									__a						:  str									=     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									__a						:  int									=     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									__a						:  List[str]									=     with_conta
									__a						:  Optional[Any]									=     with_conta
									__a						:  Any									=     len(lowerCamelCase_				) - 1
									__a						:  Any									=     len(lowerCamelCase_				) - 1
									__a						:  Optional[int]									=     with_conta
									__a						:  Tuple									=     with_conta
									__a						:  Optional[int]									=     mc_label
						__a						:  Union[str, Any]									=     (input_ids, mc_token_ids, lm_labels, mc_labels)
						tensor_datasets.append(tuple(torch.tensor(lowerCamelCase_				) for t in all_inputs				)				)
			return tensor_datasets

def        UpperCAmelCase__		(						):
			__a						:  List[str]									=     argparse.ArgumentParser()
			parser.add_argument('--model_name'   ,  type=lowerCamelCase_   ,  default='openai-gpt'   ,  help='pretrained model name'				)
			parser.add_argument('--do_train'   ,  action='store_true'   ,  help='Whether to run training.'				)
			parser.add_argument('--do_eval'   ,  action='store_true'   ,  help='Whether to run eval on the dev set.'				)
			parser.add_argument(
			    '--output_dir'   ,  default=lowerCamelCase_   ,  type=lowerCamelCase_   ,  required=lowerCamelCase_   ,  help='The output directory where the model predictions and checkpoints will be written.'   ,  )
			parser.add_argument('--train_dataset'   ,  type=lowerCamelCase_   ,  default=''				)
			parser.add_argument('--eval_dataset'   ,  type=lowerCamelCase_   ,  default=''				)
			parser.add_argument('--seed'   ,  type=lowerCamelCase_   ,  default=4_2				)
			parser.add_argument('--num_train_epochs'   ,  type=lowerCamelCase_   ,  default=3				)
			parser.add_argument('--train_batch_size'   ,  type=lowerCamelCase_   ,  default=8				)
			parser.add_argument('--eval_batch_size'   ,  type=lowerCamelCase_   ,  default=1_6				)
			parser.add_argument('--adam_epsilon'   ,  default=1e-8   ,  type=lowerCamelCase_   ,  help='Epsilon for Adam optimizer.'				)
			parser.add_argument('--max_grad_norm'   ,  type=lowerCamelCase_   ,  default=1				)
			parser.add_argument(
			    '--max_steps'   ,  default=-1   ,  type=lowerCamelCase_   ,  help=(
			        'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
			    )   ,  )
			parser.add_argument(
			    '--gradient_accumulation_steps'   ,  type=lowerCamelCase_   ,  default=1   ,  help='Number of updates steps to accumulate before                        performing a backward/update pass.'   ,  )
			parser.add_argument('--learning_rate'   ,  type=lowerCamelCase_   ,  default=6.25e-5				)
			parser.add_argument('--warmup_steps'   ,  default=0   ,  type=lowerCamelCase_   ,  help='Linear warmup over warmup_steps.'				)
			parser.add_argument('--lr_schedule'   ,  type=lowerCamelCase_   ,  default='warmup_linear'				)
			parser.add_argument('--weight_decay'   ,  type=lowerCamelCase_   ,  default=0.01				)
			parser.add_argument('--lm_coef'   ,  type=lowerCamelCase_   ,  default=0.9				)
			parser.add_argument('--n_valid'   ,  type=lowerCamelCase_   ,  default=3_7_4				)

			parser.add_argument('--server_ip'   ,  type=lowerCamelCase_   ,  default=''   ,  help='Can be used for distant debugging.'				)
			parser.add_argument('--server_port'   ,  type=lowerCamelCase_   ,  default=''   ,  help='Can be used for distant debugging.'				)
			__a						:  int									=     parser.parse_args()
			print(lowerCamelCase_				)

			if args.server_ip and args.server_port:
						# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
						import ptvsd

						print('Waiting for debugger attach'				)
						ptvsd.enable_attach(address=(args.server_ip, args.server_port)   ,  redirect_output=lowerCamelCase_				)
						ptvsd.wait_for_attach()

			random.seed(args.seed				)
			np.random.seed(args.seed				)
			torch.manual_seed(args.seed				)
			torch.cuda.manual_seed_all(args.seed				)

			__a						:  Any									=     torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)
			__a						:  Union[str, Any]									=     torch.cuda.device_count()
			logger.info('device: {}, n_gpu {}'.format(lowerCamelCase_   ,  lowerCamelCase_				)				)

			if not args.do_train and not args.do_eval:
						raise ValueError('At least one of `do_train` or `do_eval` must be True.'				)

			if not os.path.exists(args.output_dir				):
						os.makedirs(args.output_dir				)

			# Load tokenizer and model
			# This loading functions also add new tokens and embeddings called `special tokens`
			# These new embeddings will be fine-tuned on the RocStories dataset
			__a						:  int									=     ['_start_', '_delimiter_', '_classify_']
			__a						:  Optional[int]									=     OpenAIGPTTokenizer.from_pretrained(args.model_name				)
			tokenizer.add_tokens(lowerCamelCase_				)
			__a						:  Optional[int]									=     tokenizer.convert_tokens_to_ids(lowerCamelCase_				)
			__a						:  int									=     OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name				)
			model.resize_token_embeddings(len(lowerCamelCase_				)				)
			model.to(lowerCamelCase_				)

			# Load and encode the datasets
			def tokenize_and_encode(lowerCamelCase_       :       Optional[Any]				):
						if isinstance(lowerCamelCase_   ,  lowerCamelCase_				):
									return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(lowerCamelCase_				)				)
						elif isinstance(lowerCamelCase_   ,  lowerCamelCase_				):
									return obj
						return [tokenize_and_encode(lowerCamelCase_				) for o in obj]

			logger.info('Encoding dataset...'				)
			__a						:  Tuple									=     load_rocstories_dataset(args.train_dataset				)
			__a						:  List[Any]									=     load_rocstories_dataset(args.eval_dataset				)
			__a						:  str									=     (train_dataset, eval_dataset)
			__a						:  str									=     tokenize_and_encode(lowerCamelCase_				)

			# Compute the max input length for the Transformer
			__a						:  Any									=     model.config.n_positions // 2 - 2
			__a						:  Dict									=     max(
			    len(story[:max_length]				) + max(len(conta[:max_length]				)   ,  len(conta[:max_length]				)				) + 3
			    for dataset in encoded_datasets
			    for story, conta, conta, _ in dataset				)
			__a						:  Dict									=     min(lowerCamelCase_   ,  model.config.n_positions				)  # Max size of input for the pre-trained model

			# Prepare inputs tensors and dataloaders
			__a						:  Dict									=     pre_process_datasets(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  *lowerCamelCase_				)
			__a						,					__a						:  str									=     tensor_datasets[0], tensor_datasets[1]

			__a						:  str									=     TensorDataset(*lowerCamelCase_				)
			__a						:  int									=     RandomSampler(lowerCamelCase_				)
			__a						:  str									=     DataLoader(lowerCamelCase_   ,  sampler=lowerCamelCase_   ,  batch_size=args.train_batch_size				)

			__a						:  List[Any]									=     TensorDataset(*lowerCamelCase_				)
			__a						:  str									=     SequentialSampler(lowerCamelCase_				)
			__a						:  Dict									=     DataLoader(lowerCamelCase_   ,  sampler=lowerCamelCase_   ,  batch_size=args.eval_batch_size				)

			# Prepare optimizer
			if args.do_train:
						if args.max_steps > 0:
									__a						:  Optional[Any]									=     args.max_steps
									__a						:  int									=     args.max_steps // (len(lowerCamelCase_				) // args.gradient_accumulation_steps) + 1
						else:
									__a						:  List[Any]									=     len(lowerCamelCase_				) // args.gradient_accumulation_steps * args.num_train_epochs

						__a						:  Any									=     list(model.named_parameters()				)
						__a						:  Optional[int]									=     ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
						__a						:  int									=     [
						    {
						        'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay				)],
						        'weight_decay': args.weight_decay,
						    },
						    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay				)], 'weight_decay': 0.0},
						]
						__a						:  List[str]									=     AdamW(lowerCamelCase_   ,  lr=args.learning_rate   ,  eps=args.adam_epsilon				)
						__a						:  Optional[Any]									=     get_linear_schedule_with_warmup(
						    lowerCamelCase_   ,  num_warmup_steps=args.warmup_steps   ,  num_training_steps=lowerCamelCase_				)

			if args.do_train:
						__a						,					__a						,					__a						:  List[Any]									=     0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs				)   ,  desc='Epoch'				):
									__a						:  Union[str, Any]									=     0
									__a						:  int									=     0
									__a						:  str									=     tqdm(lowerCamelCase_   ,  desc='Training'				)
									for step, batch in enumerate(lowerCamelCase_				):
												__a						:  int									=     tuple(t.to(lowerCamelCase_				) for t in batch				)
												__a						,					__a						,					__a						,					__a						:  Any									=     batch
												__a						:  List[Any]									=     model(lowerCamelCase_   ,  mc_token_ids=lowerCamelCase_   ,  lm_labels=lowerCamelCase_   ,  mc_labels=lowerCamelCase_				)
												__a						:  Union[str, Any]									=     args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												__a						:  Optional[int]									=     (
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												__a						:  Optional[Any]									=     'Training loss: {:.2e} lr: {:.2e}'.format(lowerCamelCase_   ,  scheduler.get_lr()[0]				)

    # Save a trained model
			if args.do_train:
						# Save a trained model, configuration and tokenizer
						__a						:  List[str]									=     model.module if hasattr(lowerCamelCase_   ,  'module'				) else model  # Only save the model itself

						# If we save using the predefined names, we can load using `from_pretrained`
						__a						:  Union[str, Any]									=     os.path.join(args.output_dir   ,  lowerCamelCase_				)
						__a						:  List[Any]									=     os.path.join(args.output_dir   ,  lowerCamelCase_				)

						torch.save(model_to_save.state_dict()   ,  lowerCamelCase_				)
						model_to_save.config.to_json_file(lowerCamelCase_				)
						tokenizer.save_vocabulary(args.output_dir				)

						# Load a trained model and vocabulary that you have fine-tuned
						__a						:  str									=     OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir				)
						__a						:  Tuple									=     OpenAIGPTTokenizer.from_pretrained(args.output_dir				)
						model.to(lowerCamelCase_				)

			if args.do_eval:
						model.eval()
						__a						,					__a						:  Union[str, Any]									=     0, 0
						__a						,					__a						:  List[Any]									=     0, 0
						for batch in tqdm(lowerCamelCase_   ,  desc='Evaluating'				):
									__a						:  int									=     tuple(t.to(lowerCamelCase_				) for t in batch				)
									__a						,					__a						,					__a						,					__a						:  Dict									=     batch
									with torch.no_grad():
												__a						,					__a						,					__a						,					__a						:  Dict									=     model(
												    lowerCamelCase_   ,  mc_token_ids=lowerCamelCase_   ,  lm_labels=lowerCamelCase_   ,  mc_labels=lowerCamelCase_				)

									__a						:  int									=     mc_logits.detach().cpu().numpy()
									__a						:  List[str]									=     mc_labels.to('cpu'				).numpy()
									__a						:  str									=     accuracy(lowerCamelCase_   ,  lowerCamelCase_				)

									eval_loss += mc_loss.mean().item()
									eval_accuracy += tmp_eval_accuracy

									nb_eval_examples += input_ids.size(0				)
									nb_eval_steps += 1

						__a						:  Optional[int]									=     eval_loss / nb_eval_steps
						__a						:  Dict									=     eval_accuracy / nb_eval_examples
						__a						:  Dict									=     tr_loss / nb_tr_steps if args.do_train else None
						__a						:  Dict									=     {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

						__a						:  str									=     os.path.join(args.output_dir   ,  'eval_results.txt'				)
						with open(lowerCamelCase_   ,  'w'				) as writer:
									logger.info('***** Eval results *****'				)
									for key in sorted(result.keys()				):
												logger.info('  %s = %s'   ,  lowerCamelCase_   ,  str(result[key]				)				)
												writer.write('%s = %s\n' % (key, str(result[key]				))				)


if __name__ == "__main__":
							main()












'''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>",
   )





