





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_=14   ,				A_=7   ,				A_=True   ,				A_=True   ,				A_=False   ,				A_=True   ,				A_=99   ,				A_=32   ,				A_=4   ,				A_=4   ,				A_=4   ,				A_=37   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=5_12   ,				A_=0.02   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			seq_length
        SCREAMING_SNAKE_CASE__											=			is_training
        SCREAMING_SNAKE_CASE__											=			use_input_mask
        SCREAMING_SNAKE_CASE__											=			use_token_type_ids
        SCREAMING_SNAKE_CASE__											=			use_labels
        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			rotary_dim
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			hidden_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__											=			max_position_embeddings
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			vocab_size - 1
        SCREAMING_SNAKE_CASE__											=			vocab_size - 1
        SCREAMING_SNAKE_CASE__											=			vocab_size - 1



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )

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

        SCREAMING_SNAKE_CASE__											=			GPTJConfig(
            vocab_size=self.vocab_size   ,				n_embd=self.hidden_size   ,				n_layer=self.num_hidden_layers   ,				n_head=self.num_attention_heads   ,				n_positions=self.max_position_embeddings   ,				use_cache=A_   ,				bos_token_id=self.bos_token_id   ,				eos_token_id=self.eos_token_id   ,				pad_token_id=self.pad_token_id   ,				rotary_dim=self.rotary_dim   ,				)

        return (config, input_ids, input_mask)



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			config_and_inputs
        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			model_class_name(A_     )

        SCREAMING_SNAKE_CASE__											=			model.init_cache(input_ids.shape[0]   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			jnp.ones((input_ids.shape[0], max_decoder_length)   ,				dtype='''i4'''     )

        SCREAMING_SNAKE_CASE__											=			jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1     )[None, :]   ,				(input_ids.shape[0], input_ids.shape[-1] - 1)     )
        SCREAMING_SNAKE_CASE__											=			model(
            input_ids[:, :-1]   ,				attention_mask=A_   ,				past_key_values=A_   ,				position_ids=A_   ,				)

        SCREAMING_SNAKE_CASE__											=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,				dtype='''i4'''     )
        SCREAMING_SNAKE_CASE__											=			model(
            input_ids[:, -1:]   ,				attention_mask=A_   ,				past_key_values=outputs_cache.past_key_values   ,				position_ids=A_   ,				)

        SCREAMING_SNAKE_CASE__											=			model(A_     )

        SCREAMING_SNAKE_CASE__											=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1E-3   ,				msg=f'''Max diff is {diff}'''     )




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			model_class_name(A_     )

        SCREAMING_SNAKE_CASE__											=			jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])     )]   ,				axis=-1   ,				)

        SCREAMING_SNAKE_CASE__											=			model.init_cache(input_ids.shape[0]   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1     )[None, :]   ,				(input_ids.shape[0], input_ids.shape[-1] - 1)     )

        SCREAMING_SNAKE_CASE__											=			model(
            input_ids[:, :-1]   ,				attention_mask=A_   ,				past_key_values=A_   ,				position_ids=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,				dtype='''i4'''     )
        SCREAMING_SNAKE_CASE__											=			model(
            input_ids[:, -1:]   ,				past_key_values=outputs_cache.past_key_values   ,				attention_mask=A_   ,				position_ids=A_   ,				)

        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_     )

        SCREAMING_SNAKE_CASE__											=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1E-3   ,				msg=f'''Max diff is {diff}'''     )





@require_flax
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[int]				=	(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    lowerCamelCase__    :		Tuple				=	(FlaxGPTJForCausalLM,) if is_flax_available() else ()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			FlaxGPTJModelTester(self     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward(A_   ,				A_   ,				A_   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward_with_attn_mask(
                A_   ,				A_   ,				A_   ,				A_     )



    @tooslow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			GPTaTokenizer.from_pretrained('''gpt2'''   ,				pad_token='''<|endoftext|>'''   ,				padding_side='''left'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(['''Hello this is a long string''', '''Hey''']   ,				return_tensors='''np'''   ,				padding=A_   ,				truncation=A_     )

        SCREAMING_SNAKE_CASE__											=			FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''     )
        SCREAMING_SNAKE_CASE__											=			False
        SCREAMING_SNAKE_CASE__											=			model.config.eos_token_id

        SCREAMING_SNAKE_CASE__											=			jax.jit(model.generate     )

        SCREAMING_SNAKE_CASE__											=			jit_generate(
            inputs['''input_ids''']   ,				attention_mask=inputs['''attention_mask''']   ,				pad_token_id=tokenizer.pad_token_id     ).sequences

        SCREAMING_SNAKE_CASE__											=			tokenizer.batch_decode(A_   ,				skip_special_tokens=A_     )

        SCREAMING_SNAKE_CASE__											=			[
            '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
            '''Hey, I\'m a little late to the party. I\'m going to''',
        ]

        self.assertListEqual(A_   ,				A_     )



    @is_pt_flax_cross_test
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__     ):
                # prepare inputs
                SCREAMING_SNAKE_CASE__											=			self._prepare_for_class(A_   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                SCREAMING_SNAKE_CASE__											=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
                SCREAMING_SNAKE_CASE__											=			getattr(A_   ,				A_     )

                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			pt_inputs['''input_ids'''].shape
                SCREAMING_SNAKE_CASE__											=			np.random.randint(0   ,				seq_length - 1   ,				size=(batch_size,)     )
                for batch_idx, start_index in enumerate(A_     ):
                    SCREAMING_SNAKE_CASE__											=			0
                    SCREAMING_SNAKE_CASE__											=			1
                    SCREAMING_SNAKE_CASE__											=			0
                    SCREAMING_SNAKE_CASE__											=			1
                SCREAMING_SNAKE_CASE__											=			pt_model_class(A_     ).eval()
                SCREAMING_SNAKE_CASE__											=			model_class(A_   ,				dtype=jnp.floataa     )

                SCREAMING_SNAKE_CASE__											=			convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			fx_state

                with torch.no_grad():
                    SCREAMING_SNAKE_CASE__											=			pt_model(**A_     ).to_tuple()

                SCREAMING_SNAKE_CASE__											=			fx_model(**A_     ).to_tuple()
                self.assertEqual(len(A_     )   ,				len(A_     )   ,				'''Output lengths differ between Flax and PyTorch'''     )
                for fx_output, pt_output in zip(A_   ,				A_     ):
                    self.assert_almost_equals(fx_output[:, -1]   ,				pt_output[:, -1].numpy()   ,				4E-2     )

                with tempfile.TemporaryDirectory() as tmpdirname:
                    pt_model.save_pretrained(A_     )
                    SCREAMING_SNAKE_CASE__											=			model_class.from_pretrained(A_   ,				from_pt=A_     )

                SCREAMING_SNAKE_CASE__											=			fx_model_loaded(**A_     ).to_tuple()
                self.assertEqual(
                    len(A_     )   ,				len(A_     )   ,				'''Output lengths differ between Flax and PyTorch'''     )
                for fx_output_loaded, pt_output in zip(A_   ,				A_     ):
                    self.assert_almost_equals(fx_output_loaded[:, -1]   ,				pt_output[:, -1].numpy()   ,				4E-2     )



    @is_pt_flax_cross_test
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__     ):
                # prepare inputs
                SCREAMING_SNAKE_CASE__											=			self._prepare_for_class(A_   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                SCREAMING_SNAKE_CASE__											=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
                SCREAMING_SNAKE_CASE__											=			getattr(A_   ,				A_     )

                SCREAMING_SNAKE_CASE__											=			pt_model_class(A_     ).eval()
                SCREAMING_SNAKE_CASE__											=			model_class(A_   ,				dtype=jnp.floataa     )

                SCREAMING_SNAKE_CASE__											=			load_flax_weights_in_pytorch_model(A_   ,				fx_model.params     )
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			pt_inputs['''input_ids'''].shape
                SCREAMING_SNAKE_CASE__											=			np.random.randint(0   ,				seq_length - 1   ,				size=(batch_size,)     )
                for batch_idx, start_index in enumerate(A_     ):
                    SCREAMING_SNAKE_CASE__											=			0
                    SCREAMING_SNAKE_CASE__											=			1
                    SCREAMING_SNAKE_CASE__											=			0
                    SCREAMING_SNAKE_CASE__											=			1

                # make sure weights are tied in PyTorch
                pt_model.tie_weights()

                with torch.no_grad():
                    SCREAMING_SNAKE_CASE__											=			pt_model(**A_     ).to_tuple()

                SCREAMING_SNAKE_CASE__											=			fx_model(**A_     ).to_tuple()
                self.assertEqual(len(A_     )   ,				len(A_     )   ,				'''Output lengths differ between Flax and PyTorch'''     )
                for fx_output, pt_output in zip(A_   ,				A_     ):
                    self.assert_almost_equals(fx_output[:, -1]   ,				pt_output[:, -1].numpy()   ,				4E-2     )

                with tempfile.TemporaryDirectory() as tmpdirname:
                    fx_model.save_pretrained(A_     )
                    SCREAMING_SNAKE_CASE__											=			pt_model_class.from_pretrained(A_   ,				from_flax=A_     )

                with torch.no_grad():
                    SCREAMING_SNAKE_CASE__											=			pt_model_loaded(**A_     ).to_tuple()

                self.assertEqual(
                    len(A_     )   ,				len(A_     )   ,				'''Output lengths differ between Flax and PyTorch'''     )
                for fx_output, pt_output in zip(A_   ,				A_     ):
                    self.assert_almost_equals(fx_output[:, -1]   ,				pt_output[:, -1].numpy()   ,				4E-2     )




    @tooslow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__											=			model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''     )
            SCREAMING_SNAKE_CASE__											=			model(np.ones((1, 1)     )     )
            self.assertIsNotNone(A_     )







import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


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



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	"""token-classification"""



    def __init__(       self   ,				A_     ):





        '''simple docstring'''

        if type(A_     ) == dict:
            SCREAMING_SNAKE_CASE__											=			Namespace(**A_     )
        SCREAMING_SNAKE_CASE__											=			import_module('''tasks'''     )
        try:
            SCREAMING_SNAKE_CASE__											=			getattr(A_   ,				hparams.task_type     )
            SCREAMING_SNAKE_CASE__											=			token_classification_task_clazz()
        except AttributeError:
            raise ValueError(
                f'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
                f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''     )
        SCREAMING_SNAKE_CASE__											=			self.token_classification_task.get_labels(hparams.labels     )
        SCREAMING_SNAKE_CASE__											=			CrossEntropyLoss().ignore_index
        super().__init__(A_   ,				len(self.labels     )   ,				self.mode     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        return self.model(**A_     )



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            SCREAMING_SNAKE_CASE__											=			(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids

        SCREAMING_SNAKE_CASE__											=			self(**A_     )
        SCREAMING_SNAKE_CASE__											=			outputs[0]
        # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
        return {"loss": loss}



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.hparams
        for mode in ["train", "dev", "test"]:
            SCREAMING_SNAKE_CASE__											=			self._feature_file(A_     )
            if os.path.exists(A_     ) and not args.overwrite_cache:
                logger.info('''Loading features from cached file %s'''   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			torch.load(A_     )
            else:
                logger.info('''Creating features from dataset file at %s'''   ,				args.data_dir     )
                SCREAMING_SNAKE_CASE__											=			self.token_classification_task.read_examples_from_file(args.data_dir   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			self.token_classification_task.convert_examples_to_features(
                    A_   ,				self.labels   ,				args.max_seq_length   ,				self.tokenizer   ,				cls_token_at_end=bool(self.config.model_type in ['''xlnet''']     )   ,				cls_token=self.tokenizer.cls_token   ,				cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0   ,				sep_token=self.tokenizer.sep_token   ,				sep_token_extra=A_   ,				pad_on_left=bool(self.config.model_type in ['''xlnet''']     )   ,				pad_token=self.tokenizer.pad_token_id   ,				pad_token_segment_id=self.tokenizer.pad_token_type_id   ,				pad_token_label_id=self.pad_token_label_id   ,				)
                logger.info('''Saving features into cached file %s'''   ,				A_     )
                torch.save(A_   ,				A_     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = False     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self._feature_file(A_     )
        logger.info('''Loading features from cached file %s'''   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			torch.load(A_     )
        SCREAMING_SNAKE_CASE__											=			torch.tensor([f.input_ids for f in features]   ,				dtype=torch.long     )
        SCREAMING_SNAKE_CASE__											=			torch.tensor([f.attention_mask for f in features]   ,				dtype=torch.long     )
        if features[0].token_type_ids is not None:
            SCREAMING_SNAKE_CASE__											=			torch.tensor([f.token_type_ids for f in features]   ,				dtype=torch.long     )
        else:
            SCREAMING_SNAKE_CASE__											=			torch.tensor([0 for f in features]   ,				dtype=torch.long     )
            # HACK(we will not use this anymore soon)
        SCREAMING_SNAKE_CASE__											=			torch.tensor([f.label_ids for f in features]   ,				dtype=torch.long     )
        return DataLoader(
            TensorDataset(A_   ,				A_   ,				A_   ,				A_     )   ,				batch_size=A_     )



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        """Compute validation""" ""
        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            SCREAMING_SNAKE_CASE__											=			(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids
        SCREAMING_SNAKE_CASE__											=			self(**A_     )
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			outputs[:2]
        SCREAMING_SNAKE_CASE__											=			logits.detach().cpu().numpy()
        SCREAMING_SNAKE_CASE__											=			inputs['''labels'''].detach().cpu().numpy()
        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			torch.stack([x['''val_loss'''] for x in outputs]     ).mean()
        SCREAMING_SNAKE_CASE__											=			np.concatenate([x['''pred'''] for x in outputs]   ,				axis=0     )
        SCREAMING_SNAKE_CASE__											=			np.argmax(A_   ,				axis=2     )
        SCREAMING_SNAKE_CASE__											=			np.concatenate([x['''target'''] for x in outputs]   ,				axis=0     )

        SCREAMING_SNAKE_CASE__											=			dict(enumerate(self.labels     )     )
        SCREAMING_SNAKE_CASE__											=			[[] for _ in range(out_label_ids.shape[0]     )]
        SCREAMING_SNAKE_CASE__											=			[[] for _ in range(out_label_ids.shape[0]     )]

        for i in range(out_label_ids.shape[0]     ):
            for j in range(out_label_ids.shape[1]     ):
                if out_label_ids[i, j] != self.pad_token_label_id:
                    out_label_list[i].append(label_map[out_label_ids[i][j]]     )
                    preds_list[i].append(label_map[preds[i][j]]     )

        SCREAMING_SNAKE_CASE__											=			{
            '''val_loss''': val_loss_mean,
            '''accuracy_score''': accuracy_score(A_   ,				A_     ),
            '''precision''': precision_score(A_   ,				A_     ),
            '''recall''': recall_score(A_   ,				A_     ),
            '''f1''': fa_score(A_   ,				A_     ),
        }

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



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self._eval_end(A_     )
        SCREAMING_SNAKE_CASE__											=			ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self._eval_end(A_     )

        # Converting to the dict required by pl
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
        # pytorch_lightning/trainer/logging.py#L139
        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       A_   ,				A_     ):





        '''simple docstring'''

        BaseTransformer.add_model_specific_args(A_   ,				A_     )
        parser.add_argument(
            '''--task_type'''   ,				default='''NER'''   ,				type=A_   ,				help='''Task type to fine tune in training (e.g. NER, POS, etc)'''     )
        parser.add_argument(
            '''--max_seq_length'''   ,				default=1_28   ,				type=A_   ,				help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            )   ,				)

        parser.add_argument(
            '''--labels'''   ,				default=''''''   ,				type=A_   ,				help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''   ,				)
        parser.add_argument(
            '''--gpus'''   ,				default=0   ,				type=A_   ,				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


if __name__ == "__main__":
  _A					:				List[str]				=			argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  _A					:				List[Any]				=			NERTransformer.add_model_specific_args(parser, os.getcwd())
  _A					:				Optional[Any]				=			parser.parse_args()
  _A					:				Any				=			NERTransformer(args)
  _A					:				Any				=			generic_train(model, args)

  if args.do_predict:
    # See https://github.com/huggingface/transformers/issues/3159
    # pl use this default format to create a checkpoint:
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
    # /pytorch_lightning/callbacks/model_checkpoint.py#L322
    _A					:				Optional[int]				=			sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    _A					:				Optional[int]				=			model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)
