


'''simple docstring'''

import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__lowercase:						Dict  		=   [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-generation",
        "text-classification",
        "token-classification",
        "language-modeling",
        "multiple-choice",
        "question-answering",
        "summarization",
        "translation",
        "image-classification",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
       import run_clm
       import run_image_classification
       import run_mlm
       import run_ner
       import run_qa as run_squad
       import run_summarization
       import run_swag
       import run_text_classification
       import run_translation


logging.basicConfig(level=logging.DEBUG)

__lowercase:						Any  		=   logging.getLogger()



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





   '''simple docstring'''







   UpperCamelCase__         =  argparse.ArgumentParser()
   parser.add_argument("-f"      )
   UpperCamelCase__         =  parser.parse_args()
   return args.f



def 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Dict      )						->    str:





   '''simple docstring'''







   UpperCamelCase__         =  {}
   UpperCamelCase__         =  os.path.join(__UpperCamelCase			,						"all_results.json"      )
   if os.path.exists(__UpperCamelCase      ):
      with open(__UpperCamelCase			,						"r"      ) as f:
         UpperCamelCase__         =  json.load(__UpperCamelCase      )
   else:
      raise ValueError(F'can\'t find {path}'      )
   return results



def 				SCREAMING_SNAKE_CASE__(						)						->    Any:





   '''simple docstring'''







   return bool(tf.config.list_physical_devices("GPU"      )      )


__lowercase:						Any  		=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        UpperCAmelCase       (				UpperCamelCase__):

 @skip("Skipping until shape inference for to_tf_dataset PR is merged."    )
 def 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  self.get_auto_remove_tmp_dir()
    UpperCamelCase__         =  f'\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

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

    with patch.object(a_,    "argv",    a_    ):
       run_text_classification.main()
       # Reset the mixed precision policy so we don't break other tests
       tf.keras.mixed_precision.set_global_policy("float32"    )
       UpperCamelCase__         =  get_results(a_    )
       self.assertGreaterEqual(result["eval_accuracy"],    0.75    )

 def 						lowercase_  (       self       :		Union[str, Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  self.get_auto_remove_tmp_dir()
    UpperCamelCase__         =  f'\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

    if len(tf.config.list_physical_devices("GPU"    )    ) > 1:
       # Skipping because there are not enough batches to train the model + would need a drop_last to work.
       return

    with patch.object(a_,    "argv",    a_    ):
       run_clm.main()
       UpperCamelCase__         =  get_results(a_    )
       self.assertLess(result["eval_perplexity"],    100    )

 def 						lowercase_  (       self       :		Union[str, Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  self.get_auto_remove_tmp_dir()
    UpperCamelCase__         =  f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '.split()

    with patch.object(a_,    "argv",    a_    ):
       run_mlm.main()
       UpperCamelCase__         =  get_results(a_    )
       self.assertLess(result["eval_perplexity"],    42    )

 def 						lowercase_  (       self       :		int    ):





    """simple docstring"""



    UpperCamelCase__         =  7 if get_gpu_count() > 1 else 2

    UpperCamelCase__         =  self.get_auto_remove_tmp_dir()
    UpperCamelCase__         =  f'\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

    with patch.object(a_,    "argv",    a_    ):
       run_ner.main()
       UpperCamelCase__         =  get_results(a_    )
       self.assertGreaterEqual(result["accuracy"],    0.75    )

 def 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  self.get_auto_remove_tmp_dir()
    UpperCamelCase__         =  f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

    with patch.object(a_,    "argv",    a_    ):
       run_squad.main()
       UpperCamelCase__         =  get_results(a_    )
       self.assertGreaterEqual(result["f1"],    30    )
       self.assertGreaterEqual(result["exact"],    30    )

 def 						lowercase_  (       self       :		List[str]    ):





    """simple docstring"""



    UpperCamelCase__         =  self.get_auto_remove_tmp_dir()
    UpperCamelCase__         =  f'\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

    with patch.object(a_,    "argv",    a_    ):
       run_swag.main()
       UpperCamelCase__         =  get_results(a_    )
       self.assertGreaterEqual(result["val_accuracy"],    0.8    )

 @slow
 def 						lowercase_  (       self       :		Optional[Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  self.get_auto_remove_tmp_dir()
    UpperCamelCase__         =  f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

    with patch.object(a_,    "argv",    a_    ):
       run_summarization.main()
       UpperCamelCase__         =  get_results(a_    )
       self.assertGreaterEqual(result["rouge1"],    10    )
       self.assertGreaterEqual(result["rouge2"],    2    )
       self.assertGreaterEqual(result["rougeL"],    7    )
       self.assertGreaterEqual(result["rougeLsum"],    7    )

 @slow
 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



    UpperCamelCase__         =  self.get_auto_remove_tmp_dir()
    UpperCamelCase__         =  f'\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        '.split()

    with patch.object(a_,    "argv",    a_    ):
       run_translation.main()
       UpperCamelCase__         =  get_results(a_    )
       self.assertGreaterEqual(result["bleu"],    30    )

 def 						lowercase_  (       self       :		Tuple    ):





    """simple docstring"""



    UpperCamelCase__         =  self.get_auto_remove_tmp_dir()
    UpperCamelCase__         =  f'\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '.split()

    with patch.object(a_,    "argv",    a_    ):
       run_image_classification.main()
       UpperCamelCase__         =  get_results(a_    )
       self.assertGreaterEqual(result["accuracy"],    0.7    )





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowercase__      :			List[Any]           =					logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase__      :			List[str]           =					{
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowercase__      :			Union[str, Any]           =					{
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase__      :			Any           =					sorted(arg_to_scheduler.keys())
lowercase__      :			Union[str, Any]           =					"{" + ", ".join(arg_to_scheduler_choices) + "}"




class 			a__       (					pl.LightningModule      ):







     def __init__(	self  ,		A  ,		A=None  ,		A="base"  ,		A=None  ,		A=None  ,		A=None  ,		**A  ,		)  ->    Any:



          '''simple docstring'''



          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading

          self.save_hyperparameters(A )
          a      =      0
          a      =      Path(self.hparams.output_dir )
          a      =      self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
               a      =      AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path  ,		**({"num_labels": num_labels} if num_labels is not None else {})  ,		cache_dir=A  ,		**A  ,		)
          else:
               a      =      config

          a      =      ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
          for p in extra_model_params:
               if getattr(self.hparams  ,		A  ,		A ):
                    assert hasattr(self.config  ,		A ), F'''model config doesn\'t have a `{p}` attribute'''
                    setattr(self.config  ,		A  ,		getattr(self.hparams  ,		A ) )

          if tokenizer is None:
               a      =      AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,		cache_dir=A  ,		)
          else:
               a      =      tokenizer
          a      =      MODEL_MODES[mode]
          if model is None:
               a      =      self.model_type.from_pretrained(
                   self.hparams.model_name_or_path  ,		from_tf=bool(".ckpt" in self.hparams.model_name_or_path )  ,		config=self.config  ,		cache_dir=A  ,		)
          else:
               a      =      model







     def 			lowerCAmelCase_				(	self  ,		*A  ,		**A )  ->    Dict:



          '''simple docstring'''



          a      =      self.model_type.from_pretrained(*A  ,		**A )







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



          '''simple docstring'''



          a      =      arg_to_scheduler[self.hparams.lr_scheduler]
          a      =      get_schedule_func(
              self.opt  ,		num_warmup_steps=self.hparams.warmup_steps  ,		num_training_steps=self.total_steps() )
          a      =      {"scheduler": scheduler, "interval": "step", "frequency": 1}
          return scheduler







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



          '''simple docstring'''



          a      =      self.model
          a      =      ["bias", "LayerNorm.weight"]
          a      =      [
              {
                  "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )],
                  "weight_decay": self.hparams.weight_decay,
              },
              {
                  "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
                  "weight_decay": 0.0,
              },
          ]
          if self.hparams.adafactor:
               a      =      Adafactor(
                   A  ,		lr=self.hparams.learning_rate  ,		scale_parameter=A  ,		relative_step=A )

          else:
               a      =      AdamW(
                   A  ,		lr=self.hparams.learning_rate  ,		eps=self.hparams.adam_epsilon )
          a      =      optimizer

          a      =      self.get_lr_scheduler()

          return [optimizer], [scheduler]







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Dict:



          '''simple docstring'''



          return self.validation_step(A  ,		A )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Any:



          '''simple docstring'''



          return self.validation_end(A )







     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          a      =      max(1  ,		self.hparams.gpus )  # TODO: consider num_tpu_cores
          a      =      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
          return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







     def 			lowerCAmelCase_				(	self  ,		A )  ->    List[Any]:



          '''simple docstring'''



          if mode == "test":
               a      =      len(self.test_dataloader().dataset )
          else:
               a      =      self.get_dataloader("train"  ,		self.hparams.train_batch_size  ,		shuffle=A )
               a      =      len(self.train_dataloader().dataset )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A = False )  ->    int:



          '''simple docstring'''



          raise NotImplementedError("You must implement this for your task" )







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



          '''simple docstring'''



          return self.train_loader







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



          '''simple docstring'''



          return self.get_dataloader("dev"  ,		self.hparams.eval_batch_size  ,		shuffle=A )







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          return self.get_dataloader("test"  ,		self.hparams.eval_batch_size  ,		shuffle=A )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Optional[int]:



          '''simple docstring'''



          return os.path.join(
              self.hparams.data_dir  ,		"cached_{}_{}_{}".format(
                  A  ,		list(filter(A  ,		self.hparams.model_name_or_path.split("/" ) ) ).pop()  ,		str(self.hparams.max_seq_length )  ,		)  ,		)







     @pl.utilities.rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A )  ->    None:



          '''simple docstring'''



          a      =      self.output_dir.joinpath("best_tfmr" )
          a      =      self.step_count
          self.model.save_pretrained(A )
          self.tokenizer.save_pretrained(A )







     @staticmethod
     def 			lowerCAmelCase_				(	A  ,		A )  ->    Any:



          '''simple docstring'''



          parser.add_argument(
              "--model_name_or_path"  ,		default=A  ,		type=A  ,		required=A  ,		help="Path to pretrained model or model identifier from huggingface.co/models"  ,		)
          parser.add_argument(
              "--config_name"  ,		default=""  ,		type=A  ,		help="Pretrained config name or path if not the same as model_name" )
          parser.add_argument(
              "--tokenizer_name"  ,		default=A  ,		type=A  ,		help="Pretrained tokenizer name or path if not the same as model_name"  ,		)
          parser.add_argument(
              "--cache_dir"  ,		default=""  ,		type=A  ,		help="Where do you want to store the pre-trained models downloaded from huggingface.co"  ,		)
          parser.add_argument(
              "--encoder_layerdrop"  ,		type=A  ,		help="Encoder layer dropout probability (Optional). Goes into model.config"  ,		)
          parser.add_argument(
              "--decoder_layerdrop"  ,		type=A  ,		help="Decoder layer dropout probability (Optional). Goes into model.config"  ,		)
          parser.add_argument(
              "--dropout"  ,		type=A  ,		help="Dropout probability (Optional). Goes into model.config"  ,		)
          parser.add_argument(
              "--attention_dropout"  ,		type=A  ,		help="Attention dropout probability (Optional). Goes into model.config"  ,		)
          parser.add_argument("--learning_rate"  ,		default=5e-5  ,		type=A  ,		help="The initial learning rate for Adam." )
          parser.add_argument(
              "--lr_scheduler"  ,		default="linear"  ,		choices=A  ,		metavar=A  ,		type=A  ,		help="Learning rate scheduler"  ,		)
          parser.add_argument("--weight_decay"  ,		default=0.0  ,		type=A  ,		help="Weight decay if we apply some." )
          parser.add_argument("--adam_epsilon"  ,		default=1e-8  ,		type=A  ,		help="Epsilon for Adam optimizer." )
          parser.add_argument("--warmup_steps"  ,		default=0  ,		type=A  ,		help="Linear warmup over warmup_steps." )
          parser.add_argument("--num_workers"  ,		default=4  ,		type=A  ,		help="kwarg passed to DataLoader" )
          parser.add_argument("--num_train_epochs"  ,		dest="max_epochs"  ,		default=3  ,		type=A )
          parser.add_argument("--train_batch_size"  ,		default=32  ,		type=A )
          parser.add_argument("--eval_batch_size"  ,		default=32  ,		type=A )
          parser.add_argument("--adafactor"  ,		action="store_true" )







class 			a__       (					pl.Callback      ):







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    List[str]:



          '''simple docstring'''



          a      =      trainer.lr_schedulers[0]["scheduler"]
          a      =      {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
          pl_module.logger.log_metrics(A )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    List[str]:



          '''simple docstring'''



          rank_zero_info("***** Validation results *****" )
          a      =      trainer.callback_metrics
          # Log results
          for key in sorted(A ):
               if key not in ["log", "progress_bar"]:
                    rank_zero_info("{} = {}\n".format(A  ,		str(metrics[key] ) ) )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Tuple:



          '''simple docstring'''



          rank_zero_info("***** Test results *****" )
          a      =      trainer.callback_metrics
          # Log and save results to file
          a      =      os.path.join(pl_module.hparams.output_dir  ,		"test_results.txt" )
          with open(A  ,		"w" ) as writer:
               for key in sorted(A ):
                    if key not in ["log", "progress_bar"]:
                         rank_zero_info("{} = {}\n".format(A  ,		str(metrics[key] ) ) )
                         writer.write("{} = {}\n".format(A  ,		str(metrics[key] ) ) )







def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     None:
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         "--output_dir"		,					default=__UpperCamelCase		,					type=__UpperCamelCase		,					required=__UpperCamelCase		,					help="The output directory where the model predictions and checkpoints will be written."		,					)
     parser.add_argument(
         "--fp16"		,					action="store_true"		,					help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"		,					)

     parser.add_argument(
         "--fp16_opt_level"		,					type=__UpperCamelCase		,					default="O2"		,					help=(
             "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
             "See details at https://nvidia.github.io/apex/amp.html"
         )		,					)
     parser.add_argument("--n_tpu_cores"		,					dest="tpu_cores"		,					type=__UpperCamelCase)
     parser.add_argument("--max_grad_norm"		,					dest="gradient_clip_val"		,					default=1.0		,					type=__UpperCamelCase		,					help="Max gradient norm")
     parser.add_argument("--do_train"		,					action="store_true"		,					help="Whether to run training.")
     parser.add_argument("--do_predict"		,					action="store_true"		,					help="Whether to run predictions on the test set.")
     parser.add_argument(
         "--gradient_accumulation_steps"		,					dest="accumulate_grad_batches"		,					type=__UpperCamelCase		,					default=1		,					help="Number of updates steps to accumulate before performing a backward/update pass."		,					)
     parser.add_argument("--seed"		,					type=__UpperCamelCase		,					default=42		,					help="random seed for initialization")
     parser.add_argument(
         "--data_dir"		,					default=__UpperCamelCase		,					type=__UpperCamelCase		,					required=__UpperCamelCase		,					help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."		,					)





def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase=None		,					__UpperCamelCase=True		,					__UpperCamelCase=[]		,					__UpperCamelCase=None		,					__UpperCamelCase=None		,					**__UpperCamelCase		,					)			->     Optional[Any]:
     pl.seed_everything(args.seed)

     # init model
     a      =      Path(model.hparams.output_dir)
     odir.mkdir(exist_ok=__UpperCamelCase)

     # add custom checkpoints
     if checkpoint_callback is None:
          a      =      pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir		,					prefix="checkpoint"		,					monitor="val_loss"		,					mode="min"		,					save_top_k=1)
     if early_stopping_callback:
          extra_callbacks.append(__UpperCamelCase)
     if logging_callback is None:
          a      =      LoggingCallback()

     a      =      {}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          a      =      16
          a      =      args.fpaa_opt_level

     if args.gpus > 1:
          a      =      "ddp"

     a      =      args.accumulate_grad_batches
     a      =      extra_train_kwargs.get("accelerator"		,					__UpperCamelCase)
     a      =      extra_train_kwargs.get("profiler"		,					__UpperCamelCase)

     a      =      pl.Trainer.from_argparse_args(
         __UpperCamelCase		,					weights_summary=__UpperCamelCase		,					callbacks=[logging_callback] + extra_callbacks		,					logger=__UpperCamelCase		,					checkpoint_callback=__UpperCamelCase		,					**__UpperCamelCase		,					)

     if args.do_train:
          trainer.fit(__UpperCamelCase)

     return trainer

