
import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
   from transformers.models.esm.modeling_esm import (
       ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
       EsmEmbeddings,
       create_position_ids_from_input_ids,
   )






class   UpperCAmelCase		:



   def __init__(self		:					Tuple    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					List[str]=13    ,						snake_case__		:					List[Any]=7    ,						snake_case__		:					Tuple=False    ,						snake_case__		:					Dict=True    ,						snake_case__		:					Optional[int]=False    ,						snake_case__		:					Dict=True    ,						snake_case__		:					Tuple=33    ,						snake_case__		:					str=32    ,						snake_case__		:					Tuple=5    ,						snake_case__		:					int=4    ,						snake_case__		:					str=37    ,						snake_case__		:					Optional[Any]="gelu"    ,						snake_case__		:					Optional[int]=0.1    ,						snake_case__		:					List[Any]=0.1    ,						snake_case__		:					str=5_12    ,						snake_case__		:					List[Any]=16    ,						snake_case__		:					Union[str, Any]=2    ,						snake_case__		:					List[str]=0.02    ,						snake_case__		:					str=3    ,						snake_case__		:					Optional[Any]=4    ,						snake_case__		:					str=None    ,						)		->	Tuple:


          '''simple docstring'''


          snake_case       :   int							=      parent
          snake_case       :   Dict							=      batch_size
          snake_case       :   List[Any]							=      seq_length
          snake_case       :   List[str]							=      is_training
          snake_case       :   Optional[Any]							=      use_input_mask
          snake_case       :   Optional[Any]							=      use_token_type_ids
          snake_case       :   Optional[Any]							=      use_labels
          snake_case       :   Union[str, Any]							=      vocab_size
          snake_case       :   Tuple							=      hidden_size
          snake_case       :   List[Any]							=      num_hidden_layers
          snake_case       :   int							=      num_attention_heads
          snake_case       :   Any							=      intermediate_size
          snake_case       :   Tuple							=      hidden_act
          snake_case       :   Dict							=      hidden_dropout_prob
          snake_case       :   Union[str, Any]							=      attention_probs_dropout_prob
          snake_case       :   Dict							=      max_position_embeddings
          snake_case       :   str							=      type_vocab_size
          snake_case       :   Optional[Any]							=      type_sequence_label_size
          snake_case       :   Any							=      initializer_range
          snake_case       :   Union[str, Any]							=      num_labels
          snake_case       :   Tuple							=      num_choices
          snake_case       :   str							=      scope



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	int:


          '''simple docstring'''


          snake_case       :   Any							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size )

          snake_case       :   Union[str, Any]							=      None
          if self.use_input_mask:
                 snake_case       :   List[Any]							=      random_attention_mask([self.batch_size, self.seq_length] )

          snake_case       :   int							=      None
          snake_case       :   Any							=      None
          snake_case       :   Tuple							=      None
          if self.use_labels:
                 snake_case       :   Dict							=      ids_tensor([self.batch_size]    ,						self.type_sequence_label_size )
                 snake_case       :   Optional[int]							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.num_labels )
                 snake_case       :   Optional[Any]							=      ids_tensor([self.batch_size]    ,						self.num_choices )

          snake_case       :   List[Any]							=      self.get_config()

          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	List[Any]:


          '''simple docstring'''


          return EsmConfig(
              vocab_size=self.vocab_size    ,						hidden_size=self.hidden_size    ,						pad_token_id=1    ,						num_hidden_layers=self.num_hidden_layers    ,						num_attention_heads=self.num_attention_heads    ,						intermediate_size=self.intermediate_size    ,						hidden_act=self.hidden_act    ,						hidden_dropout_prob=self.hidden_dropout_prob    ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,						max_position_embeddings=self.max_position_embeddings    ,						type_vocab_size=self.type_vocab_size    ,						initializer_range=self.initializer_range    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					int    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					List[str]    ,						snake_case__		:					List[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Tuple							=      EsmModel(config=snake_case__ )
          model.to(snake_case__ )
          model.eval()
          snake_case       :   List[str]							=      model(snake_case__    ,						attention_mask=snake_case__ )
          snake_case       :   List[str]							=      model(snake_case__ )
          snake_case       :   List[Any]							=      model(snake_case__ )

          self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size) )
          self.parent.assertEqual(result.pooler_output.shape    ,						(self.batch_size, self.hidden_size) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					List[str]    ,						snake_case__		:					str    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					List[Any] )		->	str:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      EsmForMaskedLM(config=snake_case__ )
          model.to(snake_case__ )
          model.eval()
          snake_case       :   int							=      model(snake_case__    ,						attention_mask=snake_case__    ,						labels=snake_case__ )
          self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					str    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					str    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					List[str] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Any							=      self.num_labels
          snake_case       :   int							=      EsmForTokenClassification(config=snake_case__ )
          model.to(snake_case__ )
          model.eval()
          snake_case       :   Dict							=      model(snake_case__    ,						attention_mask=snake_case__    ,						labels=snake_case__ )
          self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.num_labels) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.prepare_config_and_inputs()
          (
              (
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					(
              snake_case
          )				,					
          )       :   Tuple							=      config_and_inputs
          snake_case       :   Optional[Any]							=      {"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict






@require_torch
class   UpperCAmelCase		(						A_       ,A_       ,unittest.TestCase ):
   A__					:		List[Any]						     =     False

   A__					:		int						     =     (
       (
           EsmForMaskedLM,
           EsmModel,
           EsmForSequenceClassification,
           EsmForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   A__					:		Any						     =     ()
   A__					:		Optional[Any]						     =     (
       {
           "feature-extraction": EsmModel,
           "fill-mask": EsmForMaskedLM,
           "text-classification": EsmForSequenceClassification,
           "token-classification": EsmForTokenClassification,
           "zero-shot": EsmForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   A__					:		List[Any]						     =     True



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   int							=      EsmModelTester(self )
          snake_case       :   Tuple							=      ConfigTester(self    ,						config_class=snake_case__    ,						hidden_size=37 )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Any:


          '''simple docstring'''


          self.config_tester.run_common_tests()



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                 snake_case       :   Optional[int]							=      type
                 self.model_tester.create_and_check_model(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Any							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case__ )



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	str:


          '''simple docstring'''


          for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case       :   int							=      EsmModel.from_pretrained(snake_case__ )
                 self.assertIsNotNone(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.model_tester.prepare_config_and_inputs()[0]
          snake_case       :   int							=      EsmEmbeddings(config=snake_case__ )

          snake_case       :   List[Any]							=      torch.as_tensor([[12, 31, 13, model.padding_idx]] )
          snake_case       :   List[Any]							=      torch.as_tensor(
              [
                  [
                      0 + model.padding_idx + 1,
                      1 + model.padding_idx + 1,
                      2 + model.padding_idx + 1,
                      model.padding_idx,
                  ]
              ] )
          snake_case       :   Optional[int]							=      create_position_ids_from_input_ids(snake_case__    ,						model.padding_idx )
          self.assertEqual(position_ids.shape    ,						expected_positions.shape )
          self.assertTrue(torch.all(torch.eq(snake_case__    ,						snake_case__ ) ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.model_tester.prepare_config_and_inputs()[0]
          snake_case       :   Any							=      EsmEmbeddings(config=snake_case__ )

          snake_case       :   Optional[int]							=      torch.empty(2    ,						4    ,						30 )
          snake_case       :   Optional[Any]							=      [
              0 + embeddings.padding_idx + 1,
              1 + embeddings.padding_idx + 1,
              2 + embeddings.padding_idx + 1,
              3 + embeddings.padding_idx + 1,
          ]
          snake_case       :   Dict							=      torch.as_tensor([expected_single_positions, expected_single_positions] )
          snake_case       :   int							=      embeddings.create_position_ids_from_inputs_embeds(snake_case__ )
          self.assertEqual(position_ids.shape    ,						expected_positions.shape )
          self.assertTrue(torch.all(torch.eq(snake_case__    ,						snake_case__ ) ) )



   @unittest.skip("Esm does not support embedding resizing" )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Tuple:


          '''simple docstring'''


          pass



   @unittest.skip("Esm does not support embedding resizing" )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          pass



   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[Any]:


          '''simple docstring'''


          pass






@require_torch
class   UpperCAmelCase		(						A_ ):



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	str:


          '''simple docstring'''


          with torch.no_grad():
                 snake_case       :   Tuple							=      EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )
                 model.eval()
                 snake_case       :   str							=      torch.tensor([[0, 1, 2, 3, 4, 5]] )
                 snake_case       :   Optional[Any]							=      model(snake_case__ )[0]

                 snake_case       :   List[str]							=      33

                 snake_case       :   Tuple							=      torch.Size((1, 6, vocab_size) )
                 self.assertEqual(output.shape    ,						snake_case__ )

                 snake_case       :   str							=      torch.tensor(
                     [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] )
                 self.assertTrue(torch.allclose(output[:, :3, :3]    ,						snake_case__    ,						atol=1e-4 ) )



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Tuple:


          '''simple docstring'''


          with torch.no_grad():
                 snake_case       :   Dict							=      EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )
                 model.eval()

                 snake_case       :   str							=      torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
                 snake_case       :   int							=      model(snake_case__ )[0]
                 # compare the actual values for a slice.
                 snake_case       :   Optional[Any]							=      torch.tensor(
                     [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] )
                 self.assertTrue(torch.allclose(output[:, :3, :3]    ,						snake_case__    ,						atol=1e-4 ) )







import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : PreTrainedTokenizer				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Optional[int] = None				,					):
       snake_case       :   int							=      {}

       if train_file is not None:
              snake_case       :   List[Any]							=      [train_file]
       if eval_file is not None:
              snake_case       :   Optional[int]							=      [eval_file]
       if test_file is not None:
              snake_case       :   Any							=      [test_file]

       snake_case       :   int							=      datasets.load_dataset("csv"				,					data_files=__lowerCamelCase    )
       snake_case       :   str							=      list(ds[list(files.keys()    )[0]].features.keys()    )
       snake_case       :   int							=      features_name.pop(__lowerCamelCase    )
       snake_case       :   str							=      list(set(ds[list(files.keys()    )[0]][label_name]    )    )
       snake_case       :   str							=      {label: i for i, label in enumerate(__lowerCamelCase    )}
       snake_case       :   List[Any]							=      tokenizer.model_input_names
       snake_case       :   List[Any]							=      {}

       if len(__lowerCamelCase    ) == 1:
              for k in files.keys():
                     snake_case       :   Tuple							=      ds[k].map(
                         lambda __lowerCamelCase    : tokenizer.batch_encode_plus(
                             example[features_name[0]]				,					truncation=__lowerCamelCase				,					max_length=__lowerCamelCase				,					padding="max_length"    )				,					batched=__lowerCamelCase				,					)
       elif len(__lowerCamelCase    ) == 2:
              for k in files.keys():
                     snake_case       :   List[Any]							=      ds[k].map(
                         lambda __lowerCamelCase    : tokenizer.batch_encode_plus(
                             (example[features_name[0]], example[features_name[1]])				,					truncation=__lowerCamelCase				,					max_length=__lowerCamelCase				,					padding="max_length"				,					)				,					batched=__lowerCamelCase				,					)

       def gen_train():
              for ex in transformed_ds[datasets.Split.TRAIN]:
                     snake_case       :   Dict							=      {k: v for k, v in ex.items() if k in input_names}
                     snake_case       :   Union[str, Any]							=      labelaid[ex[label_name]]
                     yield (d, label)

       def gen_val():
              for ex in transformed_ds[datasets.Split.VALIDATION]:
                     snake_case       :   str							=      {k: v for k, v in ex.items() if k in input_names}
                     snake_case       :   Any							=      labelaid[ex[label_name]]
                     yield (d, label)

       def gen_test():
              for ex in transformed_ds[datasets.Split.TEST]:
                     snake_case       :   str							=      {k: v for k, v in ex.items() if k in input_names}
                     snake_case       :   List[str]							=      labelaid[ex[label_name]]
                     yield (d, label)

       snake_case       :   int							=      (
           tf.data.Dataset.from_generator(
               __lowerCamelCase				,					({k: tf.intaa for k in input_names}, tf.intaa)				,					({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))				,					)
           if datasets.Split.TRAIN in transformed_ds
           else None
       )

       if train_ds is not None:
              snake_case       :   Optional[Any]							=      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]    )    )    )

       snake_case       :   Tuple							=      (
           tf.data.Dataset.from_generator(
               __lowerCamelCase				,					({k: tf.intaa for k in input_names}, tf.intaa)				,					({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))				,					)
           if datasets.Split.VALIDATION in transformed_ds
           else None
       )

       if val_ds is not None:
              snake_case       :   List[str]							=      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]    )    )    )

       snake_case       :   Optional[int]							=      (
           tf.data.Dataset.from_generator(
               __lowerCamelCase				,					({k: tf.intaa for k in input_names}, tf.intaa)				,					({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))				,					)
           if datasets.Split.TEST in transformed_ds
           else None
       )

       if test_ds is not None:
              snake_case       :   str							=      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]    )    )    )

       return train_ds, val_ds, test_ds, labelaid


__lowerCamelCase							=       logging.getLogger(__name__)






@dataclass
class   UpperCAmelCase		:
   A__					:		int						     =     field(metadata={"help": "Which column contains the label"} )
   A__					:		str						     =     field(default=A_       ,metadata={"help": "The path of the training file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,metadata={"help": "The path of the development file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,metadata={"help": "The path of the test file"} )
   A__					:		int						     =     field(
       default=1_28       ,metadata={
           "help": (
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )
       }       ,)
   A__					:		bool						     =     field(
       default=A_       ,metadata={"help": "Overwrite the cached training and evaluation sets"} )






@dataclass
class   UpperCAmelCase		:
   A__					:		str						     =     field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
   A__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
   A__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
   A__					:		bool						     =     field(default=A_       ,metadata={"help": "Set this flag to use fast tokenization."} )
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   A__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,)



def 			UpperCamelCase					(    ):
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.
       snake_case       :   List[Any]							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
       snake_case				,					snake_case				,					snake_case       :   int							=      parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir    )
           and os.listdir(training_args.output_dir    )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
                  " --overwrite_output_dir to overcome."    )

       # Setup logging
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,					datefmt="%m/%d/%Y %H:%M:%S"				,					level=logging.INFO				,					)
       logger.info(
           f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1    )}, """
           f"""16-bits training: {training_args.fpaa}"""    )
       logger.info(f"""Training/evaluation parameters {training_args}"""    )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       snake_case       :   Tuple							=      AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,					cache_dir=model_args.cache_dir				,					)

       snake_case				,					snake_case				,					snake_case				,					snake_case       :   Tuple							=      get_tfds(
           train_file=data_args.train_file				,					eval_file=data_args.dev_file				,					test_file=data_args.test_file				,					tokenizer=__lowerCamelCase				,					label_column_id=data_args.label_column_id				,					max_seq_length=data_args.max_seq_length				,					)

       snake_case       :   Optional[Any]							=      AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path				,					num_labels=len(__lowerCamelCase    )				,					labelaid=__lowerCamelCase				,					idalabel={id: label for label, id in labelaid.items()}				,					finetuning_task="text-classification"				,					cache_dir=model_args.cache_dir				,					)

       with training_args.strategy.scope():
              snake_case       :   int							=      TFAutoModelForSequenceClassification.from_pretrained(
                  model_args.model_name_or_path				,					from_pt=bool(".bin" in model_args.model_name_or_path    )				,					config=__lowerCamelCase				,					cache_dir=model_args.cache_dir				,					)

       def compute_metrics(__lowerCamelCase     : EvalPrediction    ) -> Dict:
              snake_case       :   Optional[int]							=      np.argmax(p.predictions				,					axis=1    )

              return {"acc": (preds == p.label_ids).mean()}

       # Initialize our Trainer
       snake_case       :   int							=      TFTrainer(
           model=__lowerCamelCase				,					args=__lowerCamelCase				,					train_dataset=__lowerCamelCase				,					eval_dataset=__lowerCamelCase				,					compute_metrics=__lowerCamelCase				,					)

       # Training
       if training_args.do_train:
              trainer.train()
              trainer.save_model()
              tokenizer.save_pretrained(training_args.output_dir    )

       # Evaluation
       snake_case       :   int							=      {}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"    )
              snake_case       :   Any							=      trainer.evaluate()
              snake_case       :   List[Any]							=      os.path.join(training_args.output_dir				,					"eval_results.txt"    )

              with open(__lowerCamelCase				,					"w"    ) as writer:
                     logger.info("***** Eval results *****"    )

                     for key, value in result.items():
                            logger.info(f"""  {key} = {value}"""    )
                            writer.write(f"""{key} = {value}\n"""    )

                     results.update(__lowerCamelCase    )

       return results


if __name__ == "__main__":
   main()





