



"""simple docstring"""
import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
						from tests.trainer.test_trainer import (  # noqa
						    RegressionModelConfig,
						    RegressionPreTrainedModel,
						    get_regression_trainer,
						)


set_seed(42)

__a           =							get_tests_dir("fixtures")
__a           =							os.path.join(dirname(get_tests_dir()))
__a           =							dirname(os.path.abspath(__file__))

# default torch.distributed port
__a           =							"""10999"""

__a           =							"""t5-small"""

# *** Working Models ***
__a           =							"""hf-internal-testing/tiny-albert"""
__a           =							"""sshleifer/bart-tiny-random"""
__a           =							"""hf-internal-testing/tiny-bert"""
__a           =							"""hf-internal-testing/tiny-random-bigbird_pegasus"""
__a           =							"""hf-internal-testing/tiny-random-big_bird"""
__a           =							"""hf-internal-testing/tiny-random-blenderbot"""
__a           =							"""bigscience/bigscience-small-testing"""
__a           =							"""hf-internal-testing/tiny-random-deberta"""
__a           =							"""hf-internal-testing/tiny-random-deberta-v2"""
__a           =							"""sshleifer/tiny-distilbert-base-cased"""
__a           =							"""hf-internal-testing/tiny-electra"""
__a           =							"""hf-internal-testing/tiny-random-flaubert"""
__a           =							"""stas/tiny-wmt19-en-de"""
__a           =							"""hf-internal-testing/tiny-random-funnel"""
__a           =							"""sshleifer/tiny-gpt2"""
__a           =							"""hf-internal-testing/tiny-random-gptj"""
__a           =							"""hf-internal-testing/tiny-random-gpt_neo"""
__a           =							"""hf-internal-testing/tiny-layoutlm"""
__a           =							"""hf-internal-testing/tiny-random-led"""
__a           =							"""hf-internal-testing/tiny-random-longformer"""
__a           =							"""stas/tiny-m2m_100"""  # hf tiny model is unsuitable
__a           =							"""sshleifer/tiny-marian-en-de"""
__a           =							"""sshleifer/tiny-mbart"""
__a           =							"""hf-internal-testing/tiny-random-mobilebert"""
__a           =							"""hf-internal-testing/tiny-random-mpnet"""
__a           =							"""stas/pegasus-cnn_dailymail-tiny-random"""
__a           =							"""hf-internal-testing/tiny-random-prophetnet"""
__a           =							"""sshleifer/tiny-distilroberta-base"""
__a           =							"""hf-internal-testing/tiny-random-squeezebert"""
__a           =							"""patrickvonplaten/t5-tiny-random"""
__a           =							"""hf-internal-testing/tiny-random-t5-v1.1"""
__a           =							"""hf-internal-testing/tiny-random-vit"""
__a           =							"""hf-internal-testing/tiny-xlm-roberta"""
__a           =							"""sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__a           =							"""hf-internal-testing/tiny-random-mt5"""
__a           =							"""hf-internal-testing/tiny-random-camembert"""
__a           =							"""hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
__a           =							"""hf-internal-testing/tiny-random-convbert"""
__a           =							"""hf-internal-testing/tiny-random-layoutlmv2"""
__a           =							"""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
__a           =							"""hf-internal-testing/tiny-random-ctrl"""
__a           =							"""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
__a           =							"""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
__a           =							"""hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
__a           =							"""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
__a           =							"""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
__a           =							""""""
# - "luke" Entities classes - no plan to make such example
__a           =							"""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
__a           =							"""hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
__a           =							"""hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
__a           =							"""hf-internal-testing/tiny-random-tapas"""
def   A_    (     _lowercase=False      ):







						'''simple docstring'''


						snake_case_					:List[Any]		=     min(2,   get_gpu_count()      ) if distributed else 1
						snake_case_					:List[Any]		=     os.environ.get("""DS_TEST_PORT""",   _lowercase      )
						return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:Dict		=     f"""{FIXTURE_DIRECTORY}/tests_samples"""
						snake_case_					:Any		=     f"""{data_dir_samples}/wmt_en_ro"""
						snake_case_					:Any		=     f"""{data_dir_samples}/xsum"""
						snake_case_					:Dict		=     "\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        ".split()

						# try to cover as many models as possible once (it's enough to run on one task per model)
						# but need a tiny model for each
						#
						# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
						snake_case_					:Dict		=     {
						    "trans": [
						        "bart",
						        "fsmt",
						        "m2m_100",
						        "marian",
						        "mbart",
						        "t5",
						        "t5_v1",
						        # "mt5", missing model files
						    ],
						    "sum": [
						        "pegasus",
						    ],
						    "clm": [
						        "big_bird",
						        "bigbird_pegasus",
						        "blenderbot",
						        "bloom",
						        "gpt2",
						        "gpt_neo",
						        "gptj",
						        "xlm-roberta",
						        "prophetnet",
						        # "camembert", missing model files
						    ],
						    "mlm": [
						        "albert",
						        "deberta",
						        "deberta-v2",
						        "distilbert",
						        "electra",
						        "flaubert",
						        "funnel",
						        "layoutlm",
						        # "reformer", # multiple issues with either mlm/qa/clas
						    ],
						    "qa": [
						        "led",
						        "longformer",
						        "mobilebert",
						        "mpnet",
						        "roberta",
						        "squeezebert",
						        # "convbert", # missing tokenizer files
						        # "layoutlmv2", missing model files
						    ],
						    "clas": [
						        "bert",
						        "xlnet",
						        # "hubert", # missing tokenizer files
						        # "ibert", # multiple issues with either mlm/qa/clas
						        # "transfo-xl", # tokenizer issues as ctrl
						        # "ctrl", # tokenizer issues
						        # "openai-gpt", missing model files
						        # "tapas", multiple issues
						    ],
						    "img_clas": [
						        "vit",
						    ],
						}

						snake_case_					:Optional[Any]		=     f"""{ROOT_DIRECTORY}/examples/pytorch"""

						snake_case_					:str		=     {
						    "trans": f"""\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        """,
						    "sum": f"""\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        """,
						    "clm": f"""\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        """,
						    "mlm": f"""\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        """,
						    "qa": f"""\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        """,
						    "clas": f"""\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        """,
						    "img_clas": f"""\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        """,
						}

						snake_case_					:Optional[int]		=     get_launcher(distributed=_lowercase      )

						snake_case_					:List[Any]		=     {}
						for task, args in tasks.items():
												snake_case_					:Tuple		=     args.split()
												for model in tasksamodels[task]:
																		snake_case_					:Tuple		=     globals()[f"""{model.upper().replace('-',   '_'      )}_TINY"""]
																		snake_case_					:Any		=     f"""--model_name_or_path {model_name}""".split()
																		snake_case_					:List[Any]		=     launcher + args + args_model + args_main

																		# # generation special case
																		# if task == "gen":
																		#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
																		#     args_model += f"--model_type {model}".split()
																		#     cmds[f"{task}_{model}"] = launcher + args + args_model
																		# else:

						return cmds


__a           =							make_task_cmds()

__a           =							"""zero2"""
__a           =							"""zero3"""

__a           =							[ZEROa, ZEROa]
def   A_    (     _lowercase,   _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:Optional[Any]		=     parameterized.to_safe_name("""_""".join(str(_lowercase      ) for x in param.args      )      )
						return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
__a           =							list(itertools.product(stages, task_cmds.keys()))

@slow
@require_deepspeed
@require_torch_gpu
class 							lowerCamelCase    (     a__							):






					'''simple docstring'''





					def   lowerCAmelCase_							(			self:				Dict		,	snake_case:				str		,	snake_case:				Tuple		)  ->      Optional[Any]:







											if task not in task_cmds:
																	raise ValueError(f"""don\'t know of task {task}, have {task_cmds.keys()}"""		)

											snake_case_					:List[str]		=     task_cmds[task]
											snake_case_					:Optional[Any]		=     f"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

											snake_case_					:List[Any]		=     self.get_auto_remove_tmp_dir()
											snake_case_					:Any		=     f"""--output_dir {output_dir}""".split()

											cmd += args_ds + args_out

											return cmd, output_dir






					@parameterized.expand(__A		,	name_func=__A		)
					def   lowerCAmelCase_							(			self:				List[str]		,	snake_case:				List[Any]		,	snake_case:				Union[str, Any]		)  ->      Optional[Any]:
											snake_case_					:Optional[int]		=     self.get_task_cmd(__A		,	__A		)

											# 1. generate the checkpoint
											cmd += "--save_steps 1".split()
											# keep for quick debug
											# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
											execute_subprocess_async(__A		,	env=self.get_env()		)

											# 2. test that the fp32 weights get reconsolidated
											snake_case_					:str		=     f"""{output_dir}/checkpoint-1"""
											snake_case_					:Any		=     f"""{chkpt_dir}/out.bin"""
											snake_case_					:Optional[Any]		=     f"""{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}"""
											# keep for quick debug
											# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
											subprocess.check_call(__A		,	shell=__A		)
											assert os.path.exists(__A		), f"""{recovered_model_path} was not found"""

											# possibly could also test that the resulting saved model is usable but given that we use
											# random models we won't know if it's any good




'''simple docstring'''




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	:   str   		=					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	:   List[str]   		=					{
    """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	:   Optional[Any]   		=					sorted(arg_to_scheduler.keys())
__lowercase	:   Union[str, Any]   		=					"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""





class _snake_case (			pl.LightningModule ):
							"""simple docstring"""





							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						)
											__lowerCamelCase		:   str           =       0
											__lowerCamelCase		:   int           =       Path(self.hparams.output_dir						)
											__lowerCamelCase		:   Tuple           =       self.hparams.cache_dir if self.hparams.cache_dir else None
											if config is None:
															__lowerCamelCase		:   str           =       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:
															__lowerCamelCase		:   PretrainedConfig           =       config

											__lowerCamelCase		:   Optional[int]           =       ("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:
															__lowerCamelCase		:   Optional[int]           =       AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,					cache_dir=__A      ,					)
											else:
															__lowerCamelCase		:   PreTrainedTokenizer           =       tokenizer
											__lowerCamelCase		:   List[str]           =       MODEL_MODES[mode]
											if model is None:
															__lowerCamelCase		:   int           =       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:
															__lowerCamelCase		:   Tuple           =       model







							def 		__SCREAMING_SNAKE_CASE( self      ,					*__A      ,					**__A						)  ->Any:



											"""simple docstring"""







											__lowerCamelCase		:   str           =       self.model_type.from_pretrained(*__A      ,					**__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







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







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



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       self.model
											__lowerCamelCase		:   List[Any]           =       ["bias", "LayerNorm.weight"]
											__lowerCamelCase		:   int           =       [
											    {
											        "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:
															__lowerCamelCase		:   Any           =       Adafactor(
															    __A      ,					lr=self.hparams.learning_rate      ,					scale_parameter=__A      ,					relative_step=__A						)

											else:
															__lowerCamelCase		:   str           =       AdamW(
															    __A      ,					lr=self.hparams.learning_rate      ,					eps=self.hparams.adam_epsilon						)
											__lowerCamelCase		:   Optional[int]           =       optimizer

											__lowerCamelCase		:   List[str]           =       self.get_lr_scheduler()

											return [optimizer], [scheduler]







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											return self.validation_step(__A      ,					__A						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											return self.validation_end(__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       max(1      ,					self.hparams.gpus						)  # TODO: consider num_tpu_cores
											__lowerCamelCase		:   Optional[Any]           =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
											return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







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



											"""simple docstring"""







											if mode == "test":
															__lowerCamelCase		:   str           =       len(self.test_dataloader().dataset						)
											else:
															__lowerCamelCase		:   List[str]           =       self.get_dataloader("train"      ,					self.hparams.train_batch_size      ,					shuffle=__A						)
															__lowerCamelCase		:   List[str]           =       len(self.train_dataloader().dataset						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A = False						)  ->Any:



											"""simple docstring"""







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







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



											"""simple docstring"""







											return self.train_loader







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



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Dict:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Dict:



											"""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 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->None:



											"""simple docstring"""







											__lowerCamelCase		:   int           =       self.output_dir.joinpath("best_tfmr"						)
											__lowerCamelCase		:   str           =       self.step_count
											self.model.save_pretrained(__A						)
											self.tokenizer.save_pretrained(__A						)







							@staticmethod
							def 		__SCREAMING_SNAKE_CASE( __A      ,					__A						)  ->str:



											"""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 _snake_case (			pl.Callback ):
							"""simple docstring"""





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



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       trainer.lr_schedulers[0]["scheduler"]
											__lowerCamelCase		:   str           =       {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
											pl_module.logger.log_metrics(__A						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->List[Any]:



											"""simple docstring"""







											rank_zero_info("***** Validation results *****"						)
											__lowerCamelCase		:   Dict           =       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 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Optional[Any]:



											"""simple docstring"""







											rank_zero_info("***** Test results *****"						)
											__lowerCamelCase		:   str           =       trainer.callback_metrics
											# Log and save results to file
											__lowerCamelCase		:   Union[str, Any]           =       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 				a_							(    lowercase				,    lowercase     )      ->None:






				'''simple docstring'''

				parser.add_argument(
				    "--output_dir"				,    default=lowercase				,    type=lowercase				,    required=lowercase				,    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=lowercase				,    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=lowercase     )
				parser.add_argument("--max_grad_norm"				,    dest="gradient_clip_val"				,    default=1.0				,    type=lowercase				,    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=lowercase				,    default=1				,    help="Number of updates steps to accumulate before performing a backward/update pass."				,    )
				parser.add_argument("--seed"				,    type=lowercase				,    default=42				,    help="random seed for initialization"     )
				parser.add_argument(
				    "--data_dir"				,    default=lowercase				,    type=lowercase				,    required=lowercase				,    help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."				,    )





def 				a_							(    lowercase				,    lowercase				,    lowercase=None				,    lowercase=True				,    lowercase=[]				,    lowercase=None				,    lowercase=None				,    **lowercase				,    )      ->List[Any]:






				'''simple docstring'''

				pl.seed_everything(args.seed     )

				# init model
				__lowerCamelCase		:   str           =       Path(model.hparams.output_dir     )
				odir.mkdir(exist_ok=lowercase     )

				# add custom checkpoints
				if checkpoint_callback is None:
								__lowerCamelCase		:   Optional[Any]           =       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(lowercase     )
				if logging_callback is None:
								__lowerCamelCase		:   List[Any]           =       LoggingCallback()

				__lowerCamelCase		:   int           =       {}

				# TODO: remove with PyTorch 1.6 since pl uses native amp
				if args.fpaa:
								__lowerCamelCase		:   List[Any]           =       16
								__lowerCamelCase		:   Optional[int]           =       args.fpaa_opt_level

				if args.gpus > 1:
								__lowerCamelCase		:   Tuple           =       "ddp"

				__lowerCamelCase		:   str           =       args.accumulate_grad_batches
				__lowerCamelCase		:   Any           =       extra_train_kwargs.get("accelerator"				,    lowercase     )
				__lowerCamelCase		:   Tuple           =       extra_train_kwargs.get("profiler"				,    lowercase     )

				__lowerCamelCase		:   Any           =       pl.Trainer.from_argparse_args(
				    lowercase				,    weights_summary=lowercase				,    callbacks=[logging_callback] + extra_callbacks				,    logger=lowercase				,    checkpoint_callback=lowercase				,    **lowercase				,    )

				if args.do_train:
								trainer.fit(lowercase     )

				return trainer





