
import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import 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 (
       MODEL_FOR_PRETRAINING_MAPPING,
       AlbertForMaskedLM,
       AlbertForMultipleChoice,
       AlbertForPreTraining,
       AlbertForQuestionAnswering,
       AlbertForSequenceClassification,
       AlbertForTokenClassification,
       AlbertModel,
   )
   from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class   lowerCamelCase	:





     def __init__(self   :					str       ,					_A   :					List[Any]       ,					_A   :					int=1_3       ,					_A   :					Optional[int]=7       ,					_A   :					Tuple=True       ,					_A   :					List[Any]=True       ,					_A   :					Union[str, Any]=True       ,					_A   :					List[Any]=True       ,					_A   :					List[Any]=9_9       ,					_A   :					str=1_6       ,					_A   :					List[Any]=3_6       ,					_A   :					Union[str, Any]=6       ,					_A   :					Dict=6       ,					_A   :					str=6       ,					_A   :					Union[str, Any]=3_7       ,					_A   :					List[str]="gelu"       ,					_A   :					Optional[Any]=0.1       ,					_A   :					Dict=0.1       ,					_A   :					Dict=5_1_2       ,					_A   :					Dict=1_6       ,					_A   :					Dict=2       ,					_A   :					Tuple=0.02       ,					_A   :					Any=3       ,					_A   :					str=4       ,					_A   :					Optional[Any]=None       ,					)		->		List[str]:
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      seq_length
         snake_case				  =      is_training
         snake_case				  =      use_input_mask
         snake_case				  =      use_token_type_ids
         snake_case				  =      use_labels
         snake_case				  =      vocab_size
         snake_case				  =      embedding_size
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_hidden_groups
         snake_case				  =      num_attention_heads
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_act
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      max_position_embeddings
         snake_case				  =      type_vocab_size
         snake_case				  =      type_sequence_label_size
         snake_case				  =      initializer_range
         snake_case				  =      num_labels
         snake_case				  =      num_choices
         snake_case				  =      scope





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Any:
         snake_case				  =      ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size     )

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

         snake_case				  =      None
         if self.use_token_type_ids:
             snake_case				  =      ids_tensor([self.batch_size, self.seq_length]       ,					self.type_vocab_size     )

         snake_case				  =      None
         snake_case				  =      None
         snake_case				  =      None
         if self.use_labels:
             snake_case				  =      ids_tensor([self.batch_size]       ,					self.type_sequence_label_size     )
             snake_case				  =      ids_tensor([self.batch_size, self.seq_length]       ,					self.num_labels     )
             snake_case				  =      ids_tensor([self.batch_size]       ,					self.num_choices     )

         snake_case				  =      self.get_config()

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





     def 	UpperCAmelCase(self   :					int     )		->		List[Any]:
         return AlbertConfig(
             vocab_size=self.vocab_size       ,					hidden_size=self.hidden_size       ,					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       ,					num_hidden_groups=self.num_hidden_groups       ,					)





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					List[Any]       ,					_A   :					List[Any]       ,					_A   :					Union[str, Any]       ,					_A   :					Dict       ,					_A   :					Tuple       ,					_A   :					Tuple       ,					_A   :					Any     )		->		Any:
         snake_case				  =      AlbertModel(config=__A     )
         model.to(__A     )
         model.eval()
         snake_case				  =      model(__A       ,					attention_mask=__A       ,					token_type_ids=__A     )
         snake_case				  =      model(__A       ,					token_type_ids=__A     )
         snake_case				  =      model(__A     )
         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 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					Optional[int]       ,					_A   :					Tuple       ,					_A   :					Optional[int]       ,					_A   :					Dict       ,					_A   :					List[Any]       ,					_A   :					Optional[int]       ,					_A   :					List[Any]     )		->		Tuple:
         snake_case				  =      AlbertForPreTraining(config=__A     )
         model.to(__A     )
         model.eval()
         snake_case				  =      model(
             __A       ,					attention_mask=__A       ,					token_type_ids=__A       ,					labels=__A       ,					sentence_order_label=__A       ,					)
         self.parent.assertEqual(result.prediction_logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size)     )
         self.parent.assertEqual(result.sop_logits.shape       ,					(self.batch_size, config.num_labels)     )





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					List[Any]       ,					_A   :					Any       ,					_A   :					Tuple       ,					_A   :					str       ,					_A   :					Optional[int]       ,					_A   :					Union[str, Any]       ,					_A   :					Union[str, Any]     )		->		Any:
         snake_case				  =      AlbertForMaskedLM(config=__A     )
         model.to(__A     )
         model.eval()
         snake_case				  =      model(__A       ,					attention_mask=__A       ,					token_type_ids=__A       ,					labels=__A     )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size)     )





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					List[Any]       ,					_A   :					int       ,					_A   :					List[Any]       ,					_A   :					int       ,					_A   :					Optional[int]       ,					_A   :					Optional[int]       ,					_A   :					Union[str, Any]     )		->		List[Any]:
         snake_case				  =      AlbertForQuestionAnswering(config=__A     )
         model.to(__A     )
         model.eval()
         snake_case				  =      model(
             __A       ,					attention_mask=__A       ,					token_type_ids=__A       ,					start_positions=__A       ,					end_positions=__A       ,					)
         self.parent.assertEqual(result.start_logits.shape       ,					(self.batch_size, self.seq_length)     )
         self.parent.assertEqual(result.end_logits.shape       ,					(self.batch_size, self.seq_length)     )





     def 	UpperCAmelCase(self   :					int       ,					_A   :					int       ,					_A   :					Tuple       ,					_A   :					int       ,					_A   :					Optional[Any]       ,					_A   :					Optional[Any]       ,					_A   :					Optional[Any]       ,					_A   :					str     )		->		str:
         snake_case				  =      self.num_labels
         snake_case				  =      AlbertForSequenceClassification(__A     )
         model.to(__A     )
         model.eval()
         snake_case				  =      model(__A       ,					attention_mask=__A       ,					token_type_ids=__A       ,					labels=__A     )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)     )





     def 	UpperCAmelCase(self   :					List[str]       ,					_A   :					Any       ,					_A   :					Optional[Any]       ,					_A   :					Dict       ,					_A   :					Any       ,					_A   :					Optional[int]       ,					_A   :					List[Any]       ,					_A   :					Dict     )		->		Optional[Any]:
         snake_case				  =      self.num_labels
         snake_case				  =      AlbertForTokenClassification(config=__A     )
         model.to(__A     )
         model.eval()
         snake_case				  =      model(__A       ,					attention_mask=__A       ,					token_type_ids=__A       ,					labels=__A     )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.num_labels)     )





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					Dict       ,					_A   :					Optional[int]       ,					_A   :					List[Any]       ,					_A   :					Optional[Any]       ,					_A   :					Any       ,					_A   :					str       ,					_A   :					Optional[int]     )		->		str:
         snake_case				  =      self.num_choices
         snake_case				  =      AlbertForMultipleChoice(config=__A     )
         model.to(__A     )
         model.eval()
         snake_case				  =      input_ids.unsqueeze(1     ).expand(-1       ,					self.num_choices       ,					-1     ).contiguous()
         snake_case				  =      token_type_ids.unsqueeze(1     ).expand(-1       ,					self.num_choices       ,					-1     ).contiguous()
         snake_case				  =      input_mask.unsqueeze(1     ).expand(-1       ,					self.num_choices       ,					-1     ).contiguous()
         snake_case				  =      model(
             __A       ,					attention_mask=__A       ,					token_type_ids=__A       ,					labels=__A       ,					)
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_choices)     )





     def 	UpperCAmelCase(self   :					Dict     )		->		List[str]:
         snake_case				  =      self.prepare_config_and_inputs()
         (
             (
             snake_case				
         )  ,							(
             snake_case				
         )  ,							(
             snake_case				
         )  ,							(
             snake_case				
         )  ,							(
             snake_case				
         )  ,							(
             snake_case				
         )  ,							(
             snake_case				
         )  ,											
         )				  =      config_and_inputs
         snake_case				  =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
         return config, inputs_dict







@require_torch
class   lowerCamelCase	(						__lowercase							,				__lowercase							,				unittest.TestCase						):
     UpperCAmelCase__       :							Dict     =	(
         (
             AlbertModel,
             AlbertForPreTraining,
             AlbertForMaskedLM,
             AlbertForMultipleChoice,
             AlbertForSequenceClassification,
             AlbertForTokenClassification,
             AlbertForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__       :							Union[str, Any]     =	(
         {
             'feature-extraction': AlbertModel,
             'fill-mask': AlbertForMaskedLM,
             'question-answering': AlbertForQuestionAnswering,
             'text-classification': AlbertForSequenceClassification,
             'token-classification': AlbertForTokenClassification,
             'zero-shot': AlbertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     UpperCAmelCase__       :							int     =	True





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					str       ,					_A   :					Optional[Any]       ,					_A   :					Any=False     )		->		int:
         snake_case				  =      super()._prepare_for_class(__A       ,					__A       ,					return_labels=__A     )

         if return_labels:
             if model_class in get_values(__A     ):
                 snake_case				  =      torch.zeros(
                     (self.model_tester.batch_size, self.model_tester.seq_length)       ,					dtype=torch.long       ,					device=__A     )
                 snake_case				  =      torch.zeros(
                     self.model_tester.batch_size       ,					dtype=torch.long       ,					device=__A     )
         return inputs_dict





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		List[Any]:
         snake_case				  =      AlbertModelTester(self     )
         snake_case				  =      ConfigTester(self       ,					config_class=__A       ,					hidden_size=3_7     )





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:
         self.config_tester.run_common_tests()





     def 	UpperCAmelCase(self   :					Dict     )		->		int:
         snake_case				  =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__A     )





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Any:
         snake_case				  =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*__A     )





     def 	UpperCAmelCase(self   :					str     )		->		Optional[int]:
         snake_case				  =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*__A     )





     def 	UpperCAmelCase(self   :					str     )		->		List[Any]:
         snake_case				  =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*__A     )





     def 	UpperCAmelCase(self   :					int     )		->		Optional[Any]:
         snake_case				  =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*__A     )





     def 	UpperCAmelCase(self   :					int     )		->		List[Any]:
         snake_case				  =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*__A     )





     def 	UpperCAmelCase(self   :					str     )		->		Any:
         snake_case				  =      self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             snake_case				  =      type
             self.model_tester.create_and_check_model(*__A     )





     @slow
     def 	UpperCAmelCase(self   :					str     )		->		Optional[int]:
         for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case				  =      AlbertModel.from_pretrained(__A     )
             self.assertIsNotNone(__A     )







@require_torch
class   lowerCamelCase	(						unittest.TestCase						):





     @slow
     def 	UpperCAmelCase(self   :					List[Any]     )		->		List[Any]:
         snake_case				  =      AlbertModel.from_pretrained("albert-base-v2"     )
         snake_case				  =      torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]     )
         snake_case				  =      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
         with torch.no_grad():
             snake_case				  =      model(__A       ,					attention_mask=__A     )[0]
         snake_case				  =      torch.Size((1, 1_1, 7_6_8)     )
         self.assertEqual(output.shape       ,					__A     )
         snake_case				  =      torch.tensor(
             [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]     )

         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,					__A       ,					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 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__				,	A__ = None				,	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      {}

    if train_file is not None:
        snake_case				  =      [train_file]
    if eval_file is not None:
        snake_case				  =      [eval_file]
    if test_file is not None:
        snake_case				  =      [test_file]

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

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

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

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

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

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({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				  =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({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				  =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({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				  =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

    return train_ds, val_ds, test_ds, labelaid


_A						      =				logging.getLogger(__name__)







@dataclass
class   lowerCamelCase	:
     UpperCAmelCase__       :							int     =	field(metadata={"help": "Which column contains the label"}						)
     UpperCAmelCase__       :							str     =	field(default=A_							,				metadata={"help": "The path of the training file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the development file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the test file"}						)
     UpperCAmelCase__       :							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."
             )
         }							,				)
     UpperCAmelCase__       :							bool     =	field(
         default=A_							,				metadata={"help": "Overwrite the cached training and evaluation sets"}						)







@dataclass
class   lowerCamelCase	:
     UpperCAmelCase__       :							str     =	field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
     UpperCAmelCase__       :							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.
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,				)



def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

    snake_case				  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    snake_case  ,							snake_case  ,							snake_case				  =      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				  =      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				  =      get_tfds(
        train_file=data_args.train_file				,	eval_file=data_args.dev_file				,	test_file=data_args.test_file				,	tokenizer=A__				,	label_column_id=data_args.label_column_id				,	max_seq_length=data_args.max_seq_length				,	)

    snake_case				  =      AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path				,	num_labels=len(A__	)				,	labelaid=A__				,	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				  =      TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path				,	from_pt=bool(".bin" in model_args.model_name_or_path	)				,	config=A__				,	cache_dir=model_args.cache_dir				,	)

    def compute_metrics(A__	) -> Dict:
        snake_case				  =      np.argmax(p.predictions				,	axis=1	)

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

    # Initialize our Trainer
    snake_case				  =      TFTrainer(
        model=A__				,	args=A__				,	train_dataset=A__				,	eval_dataset=A__				,	compute_metrics=A__				,	)

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

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

        with open(A__				,	"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(A__	)

    return results


if __name__ == "__main__":
   main()

