






import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

snake_case     					=		logging.getLogger()


def 	lowerCamelCase__      (  ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     argparse.ArgumentParser()
  parser.add_argument("-f"					)
  SCREAMING_SNAKE_CASE							:   List[str]						    =     parser.parse_args()
  return args.f


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     {}
  SCREAMING_SNAKE_CASE							:   Tuple						    =     os.path.join(lowercase    ,  "all_results.json"					)
  if os.path.exists(lowercase					):
    with open(lowercase    ,  "r"					) as f:
      SCREAMING_SNAKE_CASE							:   List[Any]						    =     json.load(lowercase					)
  else:
    raise ValueError(F'''can\'t find {path}'''					)
  return results


def 	lowerCamelCase__      (  ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   List[str]						    =     torch.cuda.is_available() and torch_device == "cuda"
  return is_using_cuda and is_apex_available()


snake_case     					=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     @classmethod
     def 					_A							(					cls							:						List[str]   ):
       # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tempfile.mkdtemp()
       SCREAMING_SNAKE_CASE							:   str						    =     os.path.join(cls.tmpdir       ,			"default_config.yml"   )
       write_basic_config(save_location=cls.configPath   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     ["accelerate", "launch", "--config_file", cls.configPath]


     @classmethod
     def 					_A							(					cls							:						int   ):
       shutil.rmtree(cls.tmpdir   )


     @mock.patch.dict(os.environ       ,			{"WANDB_MODE": "offline"}   )
     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE							:   List[str]						    =     f'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

       if is_cuda_and_apex_available():
         testargs.append("--fp16"   )

       run_command(self._launch_args + testargs   )
       SCREAMING_SNAKE_CASE							:   str						    =     get_results(UpperCAmelCase_   )
       self.assertGreaterEqual(result["eval_accuracy"]       ,			0.75   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"epoch_0"   )   )   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"glue_no_trainer"   )   )   )


     @mock.patch.dict(os.environ       ,			{"WANDB_MODE": "offline"}   )
     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     f'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

       if torch.cuda.device_count() > 1:
         # Skipping because there are not enough batches to train the model + would need a drop_last to work.
         return

       run_command(self._launch_args + testargs   )
       SCREAMING_SNAKE_CASE							:   str						    =     get_results(UpperCAmelCase_   )
       self.assertLess(result["perplexity"]       ,			100   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"epoch_0"   )   )   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"clm_no_trainer"   )   )   )


     @mock.patch.dict(os.environ       ,			{"WANDB_MODE": "offline"}   )
     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE							:   Tuple						    =     f'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

       run_command(self._launch_args + testargs   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     get_results(UpperCAmelCase_   )
       self.assertLess(result["perplexity"]       ,			42   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"epoch_0"   )   )   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"mlm_no_trainer"   )   )   )


     @mock.patch.dict(os.environ       ,			{"WANDB_MODE": "offline"}   )
     def 					_A							(					self							:						Any   ):
       # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
       SCREAMING_SNAKE_CASE							:   str						    =     7 if get_gpu_count() > 1 else 2

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE							:   Any						    =     f'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

       run_command(self._launch_args + testargs   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     get_results(UpperCAmelCase_   )
       self.assertGreaterEqual(result["eval_accuracy"]       ,			0.75   )
       self.assertLess(result["train_loss"]       ,			0.5   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"epoch_0"   )   )   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"ner_no_trainer"   )   )   )


     @unittest.skip(reason="Fix me @muellerzr"   )
     @mock.patch.dict(os.environ       ,			{"WANDB_MODE": "offline"}   )
     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   int						    =     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     f'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

       run_command(self._launch_args + testargs   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     get_results(UpperCAmelCase_   )
       # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
       self.assertGreaterEqual(result["eval_f1"]       ,			28   )
       self.assertGreaterEqual(result["eval_exact"]       ,			28   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"epoch_0"   )   )   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"qa_no_trainer"   )   )   )


     @mock.patch.dict(os.environ       ,			{"WANDB_MODE": "offline"}   )
     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE							:   List[str]						    =     f'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

       run_command(self._launch_args + testargs   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     get_results(UpperCAmelCase_   )
       self.assertGreaterEqual(result["eval_accuracy"]       ,			0.8   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"swag_no_trainer"   )   )   )


     @slow
     @mock.patch.dict(os.environ       ,			{"WANDB_MODE": "offline"}   )
     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE							:   Dict						    =     f'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

       run_command(self._launch_args + testargs   )
       SCREAMING_SNAKE_CASE							:   Any						    =     get_results(UpperCAmelCase_   )
       self.assertGreaterEqual(result["eval_rouge1"]       ,			10   )
       self.assertGreaterEqual(result["eval_rouge2"]       ,			2   )
       self.assertGreaterEqual(result["eval_rougeL"]       ,			7   )
       self.assertGreaterEqual(result["eval_rougeLsum"]       ,			7   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"epoch_0"   )   )   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"summarization_no_trainer"   )   )   )


     @slow
     @mock.patch.dict(os.environ       ,			{"WANDB_MODE": "offline"}   )
     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     f'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

       run_command(self._launch_args + testargs   )
       SCREAMING_SNAKE_CASE							:   Any						    =     get_results(UpperCAmelCase_   )
       self.assertGreaterEqual(result["eval_bleu"]       ,			30   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"epoch_0"   )   )   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"translation_no_trainer"   )   )   )


     @slow
     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     logging.StreamHandler(sys.stdout   )
       logger.addHandler(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE							:   Dict						    =     f'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

       run_command(self._launch_args + testargs   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     get_results(UpperCAmelCase_   )
       self.assertGreaterEqual(result["eval_overall_accuracy"]       ,			0.10   )






     @mock.patch.dict(os.environ       ,			{"WANDB_MODE": "offline"}   )
     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE							:   Dict						    =     f'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

       if is_cuda_and_apex_available():
         testargs.append("--fp16"   )

       run_command(self._launch_args + testargs   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     get_results(UpperCAmelCase_   )
       # The base model scores a 25%
       self.assertGreaterEqual(result["eval_accuracy"]       ,			0.6   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"step_1"   )   )   )
       self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_       ,			"image_classification_no_trainer"   )   )   )








from dataclasses import asdict, dataclass
from typing import Optional

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


_lowercase											=    logging.get_logger(__name__)

# TODO Update this
_lowercase											=    {
    """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 			lowercase_		(      A       ):
       __lowerCamelCase				       =						"esm"


       def __init__(    self						,       __A=None						,       __A=None						,       __A=None						,       __A=768						,       __A=12						,       __A=12						,       __A=3_072						,       __A=0.1						,       __A=0.1						,       __A=1_026						,       __A=0.02						,       __A=1e-1_2						,       __A="absolute"						,       __A=True						,       __A=None						,       __A=False						,       __A=False						,       __A=None						,       __A=None						,       **__A						,       )						->    int:
              super().__init__(pad_token_id=__A						,       mask_token_id=__A						,       **__A				)

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





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=super().to_dict()
              if isinstance(self.esmfold_config						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.esmfold_config.to_dict()
              return output






@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						None
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						0

       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						False

       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						None


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





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=asdict(self				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.trunk.to_dict()
              return output






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


       def 	_snake_case				(    self				)						->    int:
              if self.structure_module is None:
                     SCREAMING_SNAKE_CASE_							:				Dict		=StructureModuleConfig()
              elif isinstance(self.structure_module						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				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}.'				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=self.sequence_state_dim // self.sequence_head_width
              SCREAMING_SNAKE_CASE_							:				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 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				str		=asdict(self				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.structure_module.to_dict()
              return output






@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						3_8_4
       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						1_6
       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						1_2
       __lowerCamelCase				       =						4
       __lowerCamelCase				       =						8
       __lowerCamelCase				       =						0.1
       __lowerCamelCase				       =						8
       __lowerCamelCase				       =						1
       __lowerCamelCase				       =						2
       __lowerCamelCase				       =						7
       __lowerCamelCase				       =						1_0
       __lowerCamelCase				       =						1E-8
       __lowerCamelCase				       =						1E5


       def 	_snake_case				(    self				)						->    str:
              return asdict(self				)






def 							SCREAMING_SNAKE_CASE_	(					)			->						int:
       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>",
       )
