




from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_=None ,				lowercase_=None						)   ->       List[str]:



       """simple docstring"""

       if attention_mask is None:
              A__       =    tf.cast(tf.math.not_equal(lowercase_ ,				config.pad_token_id						) ,				tf.inta						)
       return {"input_ids": input_ids, "attention_mask": attention_mask}


@require_tf
class 				UpperCamelCase_    :

    '''simple docstring'''






    UpperCAmelCase__							  =      OPTConfig
    UpperCAmelCase__							  =      {}
    UpperCAmelCase__							  =      '''gelu'''


    def __init__(			self      :	Tuple					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	Dict=13					, UpperCAmelCase__      :	List[str]=7					, UpperCAmelCase__      :	Dict=True					, UpperCAmelCase__      :	Any=False					, UpperCAmelCase__      :	Optional[int]=99					, UpperCAmelCase__      :	Dict=16					, UpperCAmelCase__      :	Dict=2					, UpperCAmelCase__      :	Tuple=4					, UpperCAmelCase__      :	Tuple=4					, UpperCAmelCase__      :	int="gelu"					, UpperCAmelCase__      :	List[str]=0.1					, UpperCAmelCase__      :	Optional[Any]=0.1					, UpperCAmelCase__      :	Dict=20					, UpperCAmelCase__      :	Any=2					, UpperCAmelCase__      :	Tuple=1					, UpperCAmelCase__      :	str=0					, UpperCAmelCase__      :	int=16					, UpperCAmelCase__      :	List[Any]=16					, )						->Optional[int]:





           '''simple docstring'''

           A__       =    parent
           A__       =    batch_size
           A__       =    seq_length
           A__       =    is_training
           A__       =    use_labels
           A__       =    vocab_size
           A__       =    hidden_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    intermediate_size
           A__       =    hidden_act
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    max_position_embeddings
           A__       =    eos_token_id
           A__       =    pad_token_id
           A__       =    bos_token_id
           A__       =    embed_dim
           A__       =    word_embed_proj_dim
           A__       =    False


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[Any]:





           '''simple docstring'''

           A__       =    ids_tensor([self.batch_size, self.seq_length - 1]					, self.vocab_size)
           A__       =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)					, 1)
           A__       =    tf.concat([input_ids, eos_tensor]					, axis=1)

           A__       =    self.config_cls(
               vocab_size=self.vocab_size					, hidden_size=self.hidden_size					, num_hidden_layers=self.num_hidden_layers					, num_attention_heads=self.num_attention_heads					, ffn_dim=self.intermediate_size					, dropout=self.hidden_dropout_prob					, attention_dropout=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, eos_token_id=self.eos_token_id					, bos_token_id=self.bos_token_id					, pad_token_id=self.pad_token_id					, embed_dim=self.embed_dim					, word_embed_proj_dim=self.word_embed_proj_dim					, is_encoder_decoder=UpperCAmelCase__					, **self.config_updates					, )
           A__       =    prepare_opt_inputs_dict(UpperCAmelCase__					, UpperCAmelCase__)
           return config, inputs_dict





    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	int)						->Optional[Any]:





           '''simple docstring'''

           A__       =    TFOPTModel(config=UpperCAmelCase__)
           A__       =    inputs_dict['''input_ids''']

           A__       =    input_ids[:1, :]
           A__       =    inputs_dict['''attention_mask'''][:1, :]
           A__       =    1

           # first forward pass
           A__       =    model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, use_cache=UpperCAmelCase__)

           A__ ,							A__       =    outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           A__       =    ids_tensor((self.batch_size, 3)					, config.vocab_size)
           A__       =    tf.cast(ids_tensor((self.batch_size, 3)					, 2)					, tf.inta)

           # append to next input_ids and
           A__       =    tf.concat([input_ids, next_tokens]					, axis=-1)
           A__       =    tf.concat([attention_mask, next_attn_mask]					, axis=-1)

           A__       =    model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__)[0]
           A__       =    model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, past_key_values=UpperCAmelCase__)[0]

           self.parent.assertEqual(next_tokens.shape[1]					, output_from_past.shape[1])

           # select random slice
           A__       =    int(ids_tensor((1,)					, output_from_past.shape[-1]))
           A__       =    output_from_no_past[:, -3:, random_slice_idx]
           A__       =    output_from_past[:, :, random_slice_idx]

           # test that outputs are equal for slice
           tf.debugging.assert_near(UpperCAmelCase__					, UpperCAmelCase__					, rtol=1e-3)


@require_tf
class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
    UpperCAmelCase__							  =      (TFOPTForCausalLM,) if is_tf_available() else ()
    UpperCAmelCase__							  =      (
        {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
    )
    UpperCAmelCase__							  =      False
    UpperCAmelCase__							  =      False
    UpperCAmelCase__							  =      False
    UpperCAmelCase__							  =      10


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Any:





           '''simple docstring'''

           A__       =    TFOPTModelTester(self)
           A__       =    ConfigTester(self					, config_class=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->List[Any]:





           '''simple docstring'''

           self.config_tester.run_common_tests()


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





           '''simple docstring'''

           A__       =    self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__)





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





           '''simple docstring'''

           A__ ,							A__       =    self.model_tester.prepare_config_and_inputs_for_common()

           def _get_word_embedding_weight(UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	Optional[Any]):
                  if hasattr(UpperCAmelCase__					, '''weight'''):
                         return embedding_layer.weight
                  else:
                         # Here we build the word embeddings weights if not exists.
                         # And then we retry to get the attribute once built.
                         model.build()
                         if hasattr(UpperCAmelCase__					, '''weight'''):
                                return embedding_layer.weight
                         else:
                                return None

           for model_class in self.all_model_classes:
                  for size in [config.vocab_size - 10, config.vocab_size + 10]:
                         # build the embeddings
                         A__       =    model_class(config=UpperCAmelCase__)
                         A__       =    _get_word_embedding_weight(UpperCAmelCase__					, model.get_input_embeddings())
                         A__       =    _get_word_embedding_weight(UpperCAmelCase__					, model.get_output_embeddings())

                         # reshape the embeddings
                         model.resize_token_embeddings(UpperCAmelCase__)
                         A__       =    _get_word_embedding_weight(UpperCAmelCase__					, model.get_input_embeddings())
                         A__       =    _get_word_embedding_weight(UpperCAmelCase__					, model.get_output_embeddings())

                         # check that the resized embeddings size matches the desired size.
                         A__       =    size if size is not None else config.vocab_size

                         self.assertEqual(new_input_embeddings.shape[0]					, UpperCAmelCase__)

                         # check that weights remain the same after resizing
                         A__       =    True
                         for pa, pa in zip(old_input_embeddings.value()					, new_input_embeddings.value()):
                                if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
                                       A__       =    False
                         self.assertTrue(UpperCAmelCase__)

                         if old_output_embeddings is not None and new_output_embeddings is not None:
                                self.assertEqual(new_output_embeddings.shape[0]					, UpperCAmelCase__)

                                A__       =    True
                                for pa, pa in zip(old_output_embeddings.value()					, new_output_embeddings.value()):
                                       if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
                                              A__       =    False
                                self.assertTrue(UpperCAmelCase__)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Dict:



       """simple docstring"""

       return tf.constant(lowercase_ ,				dtype=tf.intaa						)


@require_tf
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      99


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





           '''simple docstring'''

           A__       =    tf.ones((4, 1)					, dtype=tf.intaa) * 2
           A__       =    tf.concat([ids_tensor((4, 6)					, self.vocab_size - 3) + 3, eos_column_vector]					, axis=1)
           A__       =    input_ids.shape[0]
           A__       =    OPTConfig(
               vocab_size=self.vocab_size					, hidden_size=24					, num_hidden_layers=2					, num_attention_heads=2					, ffn_dim=32					, max_position_embeddings=48					, eos_token_id=2					, pad_token_id=1					, bos_token_id=0					, )
           return config, input_ids, batch_size


@require_sentencepiece
@require_tf
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Dict:





           '''simple docstring'''

           A__       =    TFOPTModel.from_pretrained('''facebook/opt-350m''')
           A__       =    _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]])
           A__       =    tf.not_equal(UpperCAmelCase__					, model.config.pad_token_id)
           with tf.GradientTape():
                  A__       =    model(input_ids=UpperCAmelCase__					, attention_mask=UpperCAmelCase__).last_hidden_state
           A__       =    (1, 11, 512)
           self.assertEqual(output.shape					, UpperCAmelCase__)
           A__       =    tf.constant(
               [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]])
           self.assertTrue(np.allclose(output[:, :3, :3]					, UpperCAmelCase__					, atol=4e-3))

           A__       =    tf.function(UpperCAmelCase__					, jit_compile=UpperCAmelCase__)
           A__       =    xla_generate(UpperCAmelCase__					, UpperCAmelCase__)[0]
           self.assertTrue(np.allclose(output[:, :3, :3]					, UpperCAmelCase__					, atol=4e-2))


@require_tf
@slow
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






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





           '''simple docstring'''

           super().setUp()
           A__       =    '''facebook/opt-350m'''





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Optional[int]:





           '''simple docstring'''

           A__       =    TFOPTForCausalLM.from_pretrained(self.path_model)
           A__       =    GPTaTokenizer.from_pretrained(self.path_model)

           A__       =    [
               '''Today is a beautiful day and I want to''',
               '''In the city of''',
               '''Paris is the capital of France and''',
               '''Computers and mobile phones have taken''',
           ]
           # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
           A__       =    tokenizer(UpperCAmelCase__					, return_tensors='''tf'''					, padding=UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__)
           A__       =    tf.math.reduce_mean(model(inputs.input_ids					, attention_mask=inputs.attention_mask)[0]					, axis=-1)
           A__       =    tf.constant(
               [
                   [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                   [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                   [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                   [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
               ])
           self.assertTrue(np.allclose(UpperCAmelCase__					, UpperCAmelCase__					, atol=1e-4))

           A__       =    tf.function(UpperCAmelCase__					, jit_compile=UpperCAmelCase__)
           A__       =    tf.math.reduce_mean(xla_generate(inputs.input_ids					, attention_mask=inputs.attention_mask)[0]					, axis=-1)
           self.assertTrue(np.allclose(UpperCAmelCase__					, UpperCAmelCase__					, atol=1e-4))


@require_tf
@slow
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->int:





           '''simple docstring'''

           return [
               "Today is a beautiful day and I want",
               "In the city of",
               "Paris is the capital of France and",
               "Computers and mobile phones have taken",
           ]


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





           '''simple docstring'''

           A__       =    '''facebook/opt-125m'''

           A__       =    [
               '''Today is a beautiful day and I want to''',
               '''In the city of New York, the city''',
               '''Paris is the capital of France and the capital''',
               '''Computers and mobile phones have taken over the''',
           ]

           A__       =    []
           A__       =    GPTaTokenizer.from_pretrained(UpperCAmelCase__)
           A__       =    TFOPTForCausalLM.from_pretrained(UpperCAmelCase__)

           for prompt in self.prompts:
                  A__       =    tokenizer(UpperCAmelCase__					, return_tensors='''tf''').input_ids

                  A__       =    model.generate(UpperCAmelCase__					, max_length=10)

                  A__       =    tokenizer.batch_decode(UpperCAmelCase__					, skip_special_tokens=UpperCAmelCase__)
                  predicted_outputs += generated_string

           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)


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





           '''simple docstring'''

           A__       =    '''facebook/opt-350m'''

           A__       =    GPTaTokenizer.from_pretrained(UpperCAmelCase__)
           A__       =    TFOPTForCausalLM.from_pretrained(UpperCAmelCase__)

           A__       =    '''left'''

           # use different length sentences to test batching
           A__       =    [
               '''Hello, my dog is a little''',
               '''Today, I''',
           ]

           A__       =    tokenizer(UpperCAmelCase__					, return_tensors='''tf'''					, padding=UpperCAmelCase__)
           A__       =    inputs['''input_ids''']

           A__       =    model.generate(input_ids=UpperCAmelCase__					, attention_mask=inputs['''attention_mask'''])

           A__       =    tokenizer(sentences[0]					, return_tensors='''tf''').input_ids
           A__       =    model.generate(input_ids=UpperCAmelCase__)

           A__       =    inputs_non_padded.shape[-1] - tf.math.reduce_sum(
               tf.cast(inputs['''attention_mask'''][-1]					, tf.intaa))
           A__       =    tokenizer(sentences[1]					, return_tensors='''tf''').input_ids
           A__       =    model.generate(input_ids=UpperCAmelCase__					, max_length=model.config.max_length - num_paddings)

           A__       =    tokenizer.batch_decode(UpperCAmelCase__					, skip_special_tokens=UpperCAmelCase__)
           A__       =    tokenizer.decode(output_non_padded[0]					, skip_special_tokens=UpperCAmelCase__)
           A__       =    tokenizer.decode(output_padded[0]					, skip_special_tokens=UpperCAmelCase__)

           A__       =    [
               '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
               '''Today, I was in the middle of a conversation with a friend about the''',
           ]
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__					, [non_padded_sentence, padded_sentence])





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





           '''simple docstring'''

           A__       =    '''facebook/opt-350m'''

           A__       =    [
               '''Today is a beautiful day and I want to''',
               '''In the city of San Francisco, the city''',
               '''Paris is the capital of France and the capital''',
               '''Computers and mobile phones have taken over the''',
           ]

           A__       =    []
           A__       =    GPTaTokenizer.from_pretrained(UpperCAmelCase__)
           A__       =    TFOPTForCausalLM.from_pretrained(UpperCAmelCase__)

           for prompt in self.prompts:
                  A__       =    tokenizer(UpperCAmelCase__					, return_tensors='''tf''').input_ids

                  A__       =    model.generate(UpperCAmelCase__					, max_length=10)

                  A__       =    tokenizer.batch_decode(UpperCAmelCase__					, skip_special_tokens=UpperCAmelCase__)
                  predicted_outputs += generated_string

           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)









import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


a__:      List[str]									=		logging.getLogger(__name__)




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''sequence-classification'''



 def __init__(							self,__lowerCamelCase ):
        if type(__lowerCamelCase ) == dict:
               A__											=						Namespace(**__lowerCamelCase )
        A__											=						glue_output_modes[hparams.task]
        A__											=						glue_tasks_num_labels[hparams.task]

        super().__init__(__lowerCamelCase,__lowerCamelCase,self.mode )



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        return self.model(**__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
               A__											=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

        A__											=						self(**__lowerCamelCase )
        A__											=						outputs[0]

        A__											=						self.trainer.lr_schedulers[0]['''scheduler''']
        A__											=						{'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
        return {"loss": loss, "log": tensorboard_logs}



 def 				UpperCamelCase			(							self ):
        A__											=						self.hparams
        A__											=						processors[args.task]()
        A__											=						processor.get_labels()

        for mode in ["train", "dev"]:
               A__											=						self._feature_file(__lowerCamelCase )
               if os.path.exists(__lowerCamelCase ) and not args.overwrite_cache:
                      logger.info('''Loading features from cached file %s''',__lowerCamelCase )
               else:
                      logger.info('''Creating features from dataset file at %s''',args.data_dir )
                      A__											=						(
                          processor.get_dev_examples(args.data_dir )
                          if mode == '''dev'''
                          else processor.get_train_examples(args.data_dir )
                      )
                      A__											=						convert_examples_to_features(
                          __lowerCamelCase,self.tokenizer,max_length=args.max_seq_length,label_list=self.labels,output_mode=args.glue_output_mode,)
                      logger.info('''Saving features into cached file %s''',__lowerCamelCase )
                      torch.save(__lowerCamelCase,__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = False ):
        A__											=						'''dev''' if mode == '''test''' else mode

        A__											=						self._feature_file(__lowerCamelCase )
        logger.info('''Loading features from cached file %s''',__lowerCamelCase )
        A__											=						torch.load(__lowerCamelCase )
        A__											=						torch.tensor([f.input_ids for f in features],dtype=torch.long )
        A__											=						torch.tensor([f.attention_mask for f in features],dtype=torch.long )
        A__											=						torch.tensor([f.token_type_ids for f in features],dtype=torch.long )
        if self.hparams.glue_output_mode == "classification":
               A__											=						torch.tensor([f.label for f in features],dtype=torch.long )
        elif self.hparams.glue_output_mode == "regression":
               A__											=						torch.tensor([f.label for f in features],dtype=torch.float )

        return DataLoader(
            TensorDataset(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ),batch_size=__lowerCamelCase,shuffle=__lowerCamelCase,)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
               A__											=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

        A__											=						self(**__lowerCamelCase )
        A__					,	A__											=						outputs[:2]
        A__											=						logits.detach().cpu().numpy()
        A__											=						inputs['''labels'''].detach().cpu().numpy()

        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
        A__											=						np.concatenate([x['''pred'''] for x in outputs],axis=0 )

        if self.hparams.glue_output_mode == "classification":
               A__											=						np.argmax(__lowerCamelCase,axis=1 )
        elif self.hparams.glue_output_mode == "regression":
               A__											=						np.squeeze(__lowerCamelCase )

        A__											=						np.concatenate([x['''target'''] for x in outputs],axis=0 )
        A__											=						[[] for _ in range(out_label_ids.shape[0] )]
        A__											=						[[] for _ in range(out_label_ids.shape[0] )]

        A__											=						{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task,__lowerCamelCase,__lowerCamelCase )}

        A__											=						dict(results.items() )
        A__											=						results
        return ret, preds_list, out_label_list



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__					,	A__					,	A__											=						self._eval_end(__lowerCamelCase )
        A__											=						ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__					,	A__					,	A__											=						self._eval_end(__lowerCamelCase )
        A__											=						ret['''log''']
        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




 @staticmethod
 def 				UpperCamelCase			(							__lowerCamelCase,__lowerCamelCase ):
        BaseTransformer.add_model_specific_args(__lowerCamelCase,__lowerCamelCase )
        parser.add_argument(
            '''--max_seq_length''',default=128,type=__lowerCamelCase,help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            ),)

        parser.add_argument(
            '''--task''',default='''''',type=__lowerCamelCase,required=__lowerCamelCase,help='''The GLUE task to run''',)
        parser.add_argument(
            '''--gpus''',default=0,type=__lowerCamelCase,help='''The number of GPUs allocated for this, it is by default 0 meaning none''',)

        parser.add_argument(
            '''--overwrite_cache''',action='''store_true''',help='''Overwrite the cached training and evaluation sets''' )

        return parser


def 	UpperCamelCase__(       )->Any:
       A__											=						argparse.ArgumentParser()
       add_generic_args(UpperCamelCase__ ,     os.getcwd()  )
       A__											=						GLUETransformer.add_model_specific_args(UpperCamelCase__ ,     os.getcwd()  )
       A__											=						parser.parse_args()

       # If output_dir not provided, a folder will be generated in pwd
       if args.output_dir is None:
              A__											=						os.path.join(
                  '''./results''' ,     f"{args.task}_{time.strftime('%Y%m%d_%H%M%S'  )}" ,     )
              os.makedirs(args.output_dir  )

       A__											=						GLUETransformer(UpperCamelCase__  )
       A__											=						generic_train(UpperCamelCase__ ,     UpperCamelCase__  )

       # Optionally, predict on dev set and write to output_dir
       if args.do_predict:
              A__											=						sorted(glob.glob(os.path.join(args.output_dir ,     '''checkpoint-epoch=*.ckpt'''  ) ,     recursive=UpperCamelCase__  )  )
              A__											=						model.load_from_checkpoint(checkpoints[-1]  )
              return trainer.test(UpperCamelCase__  )


if __name__ == "__main__":
       main()





