




"""simple docstring"""





import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase	      =    logging.get_logger(__name__)

_lowercase	      =    {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_lowercase	      =    {
    """vocab_file""": {
        """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json""",
    },
    """merges_file""": {"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"""},
}

_lowercase	      =    {
    """microsoft/biogpt""": 10_24,
}

def 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


						snake_case__										=					set()
						snake_case__										=					word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)		)
												snake_case__										=					char
						return pairs

class       __lowercase       (			__lowerCamelCase	):


				'''simple docstring'''


				_lowerCAmelCase				=VOCAB_FILES_NAMES
				_lowerCAmelCase				=PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase				=["input_ids", "attention_mask"]

				def __init__(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]="<unk>"      ,SCREAMING_SNAKE_CASE_   :     str="<s>"      ,SCREAMING_SNAKE_CASE_   :     List[str]="</s>"      ,SCREAMING_SNAKE_CASE_   :     Dict="</s>"      ,SCREAMING_SNAKE_CASE_   :     str="<pad>"      ,**SCREAMING_SNAKE_CASE_   :     Any      ,):
										super().__init__(
										    bos_token=_lowerCAmelCase      ,eos_token=_lowerCAmelCase      ,sep_token=_lowerCAmelCase      ,unk_token=_lowerCAmelCase      ,pad_token=_lowerCAmelCase      ,**_lowerCAmelCase      ,)

										try:
																import sacremoses
										except ImportError:
																raise ImportError(
																    '''You need to install sacremoses to use BioGptTokenizer. '''
																    '''See https://pypi.org/project/sacremoses/ for installation.'''      )

										snake_case__										=					"""en"""
										snake_case__										=					sacremoses
										# cache of sm.MosesTokenizer instance
										snake_case__										=					{}
										snake_case__										=					{}
										with open(_lowerCAmelCase      ,encoding='''utf-8'''      ) as vocab_handle:
																snake_case__										=					json.load(_lowerCAmelCase      )
										snake_case__										=					{v: k for k, v in self.encoder.items()}
										with open(_lowerCAmelCase      ,encoding='''utf-8'''      ) as merges_handle:
																snake_case__										=					merges_handle.read().split('''\n'''      )[:-1]
										snake_case__										=					[tuple(merge.split()[:2]      ) for merge in merges]
										snake_case__										=					dict(zip(_lowerCAmelCase      ,range(len(_lowerCAmelCase      )      )      )      )
										snake_case__										=					{}

				@property
				def       _lowerCamelCase					(   self   :     Tuple      ):
										return len(self.encoder      )

				def       _lowerCamelCase					(   self   :     List[Any]      ):
										return dict(self.encoder      ,**self.added_tokens_encoder      )

				def       _lowerCamelCase					(   self   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Tuple      ):
										if lang not in self.cache_moses_tokenizer:
																snake_case__										=					self.sm.MosesTokenizer(lang=_lowerCAmelCase      )
																snake_case__										=					moses_tokenizer
										return self.cache_moses_tokenizer[lang].tokenize(
										    _lowerCAmelCase      ,aggressive_dash_splits=_lowerCAmelCase      ,return_str=_lowerCAmelCase      ,escape=_lowerCAmelCase      )

				def       _lowerCamelCase					(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     List[str]      ):
										if lang not in self.cache_moses_detokenizer:
																snake_case__										=					self.sm.MosesDetokenizer(lang=_lowerCAmelCase      )
																snake_case__										=					moses_detokenizer
										return self.cache_moses_detokenizer[lang].detokenize(_lowerCAmelCase      )

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ):
										snake_case__										=					tuple(token[:-1]      ) + (token[-1] + """</w>""",)
										if token in self.cache:
																return self.cache[token]
										snake_case__										=					get_pairs(_lowerCAmelCase      )

										if not pairs:
																return token + "</w>"

										while True:
																snake_case__										=					min(_lowerCAmelCase      ,key=lambda SCREAMING_SNAKE_CASE_      : self.bpe_ranks.get(_lowerCAmelCase      ,float('''inf'''      )      )      )
																if bigram not in self.bpe_ranks:
																						break
																snake_case__										=					bigram
																snake_case__										=					[]
																snake_case__										=					0
																while i < len(_lowerCAmelCase      ):
																						try:
																												snake_case__										=					word.index(_lowerCAmelCase      ,_lowerCAmelCase      )
																						except ValueError:
																												new_word.extend(word[i:]      )
																												break
																						else:
																												new_word.extend(word[i:j]      )
																												snake_case__										=					j

																						if word[i] == first and i < len(_lowerCAmelCase      ) - 1 and word[i + 1] == second:
																												new_word.append(first + second      )
																												i += 2
																						else:
																												new_word.append(word[i]      )
																												i += 1
																snake_case__										=					tuple(_lowerCAmelCase      )
																snake_case__										=					new_word
																if len(_lowerCAmelCase      ) == 1:
																						break
																else:
																						snake_case__										=					get_pairs(_lowerCAmelCase      )
										snake_case__										=					""" """.join(_lowerCAmelCase      )
										if word == "\n  </w>":
																snake_case__										=					"""\n</w>"""
										snake_case__										=					word
										return word

				def       _lowerCamelCase					(   self   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     Dict=False      ):
										if bypass_tokenizer:
																snake_case__										=					text.split()
										else:
																snake_case__										=					self.moses_tokenize(_lowerCAmelCase      ,self.lang      )

										snake_case__										=					[]
										for token in text:
																if token:
																						split_tokens.extend(list(self.bpe(_lowerCAmelCase      ).split(''' '''      )      )      )

										return split_tokens

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Any      ):
										return self.encoder.get(_lowerCAmelCase      ,self.encoder.get(self.unk_token      )      )

				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ):
										return self.decoder.get(_lowerCAmelCase      ,self.unk_token      )

				def       _lowerCamelCase					(   self   :     Any      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ):
										snake_case__										=					[t.replace(''' '''      ,''''''      ).replace('''</w>'''      ,''' '''      ) for t in tokens]
										snake_case__										=					"""""".join(_lowerCAmelCase      ).split()
										# detokenize
										snake_case__										=					self.moses_detokenize(_lowerCAmelCase      ,self.lang      )
										return text

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     List[Any] = None      ):
										if token_ids_a is None:
																return [self.sep_token_id] + token_ids_a
										snake_case__										=					[self.sep_token_id]
										return sep + token_ids_a + sep + token_ids_a

				def       _lowerCamelCase					(   self   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any] = None      ,SCREAMING_SNAKE_CASE_   :     str = False      ):
										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_lowerCAmelCase      ,token_ids_a=_lowerCAmelCase      ,already_has_special_tokens=_lowerCAmelCase      )
										# no bos used in fairseq
										if token_ids_a is not None:
																return [1] + ([0] * len(_lowerCAmelCase      )) + [1] + ([0] * len(_lowerCAmelCase      ))
										return [1] + ([0] * len(_lowerCAmelCase      ))

				def       _lowerCamelCase					(   self   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Dict = None      ):
										snake_case__										=					[self.sep_token_id]

										# no bos used in fairseq
										if token_ids_a is None:
																return len(token_ids_a + sep      ) * [0]
										return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]

				def       _lowerCamelCase					(   self   :     Any      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     List[str] = None      ):
										if not os.path.isdir(_lowerCAmelCase      ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
																return
										snake_case__										=					os.path.join(
										    _lowerCAmelCase      ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
										snake_case__										=					os.path.join(
										    _lowerCAmelCase      ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

										with open(_lowerCAmelCase      ,'''w'''      ,encoding='''utf-8'''      ) as f:
																f.write(json.dumps(self.encoder      ,indent=2      ,sort_keys=_lowerCAmelCase      ,ensure_ascii=_lowerCAmelCase      ) + '''\n'''      )

										snake_case__										=					0
										with open(_lowerCAmelCase      ,'''w'''      ,encoding='''utf-8'''      ) as writer:
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,key=lambda SCREAMING_SNAKE_CASE_      : kv[1]      ):
																						if index != token_index:
																												logger.warning(
																												    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																												    ''' Please check that the tokenizer is not corrupted!'''      )
																												snake_case__										=					token_index
																						writer.write(''' '''.join(_lowerCAmelCase      ) + '''\n'''      )
																						index += 1

										return vocab_file, merge_file

				def __getstate__(   self   :     Any      ):
										snake_case__										=					self.__dict__.copy()
										snake_case__										=					None
										return state





				def __setstate__(   self   :     int      ,SCREAMING_SNAKE_CASE_   :     Tuple      ):
										snake_case__										=					d

										try:
																import sacremoses
										except ImportError:
																raise ImportError(
																    '''You need to install sacremoses to use XLMTokenizer. '''
																    '''See https://pypi.org/project/sacremoses/ for installation.'''      )

										snake_case__										=					sacremoses








"""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	      =    logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_lowercase	      =    {
    """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	      =    {
    """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	      =    sorted(arg_to_scheduler.keys())
_lowercase	      =    """{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class       __lowercase       (			pl.LightningModule	):


				'''simple docstring'''


				def __init__(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     argparse.Namespace      ,SCREAMING_SNAKE_CASE_   :     Tuple=None      ,SCREAMING_SNAKE_CASE_   :     Optional[int]="base"      ,SCREAMING_SNAKE_CASE_   :     str=None      ,SCREAMING_SNAKE_CASE_   :     Optional[int]=None      ,SCREAMING_SNAKE_CASE_   :     Tuple=None      ,**SCREAMING_SNAKE_CASE_   :     List[str]      ,):
										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(SCREAMING_SNAKE_CASE_      )
										snake_case__       :					Any										=					0
										snake_case__       :					int										=					Path(self.hparams.output_dir      )
										snake_case__       :					Any										=					self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																snake_case__       :					Tuple										=					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=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
										else:
																snake_case__       :					PretrainedConfig										=					config

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

										if tokenizer is None:
																snake_case__       :					Optional[int]										=					AutoTokenizer.from_pretrained(
																    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,cache_dir=SCREAMING_SNAKE_CASE_      ,)
										else:
																snake_case__       :					PreTrainedTokenizer										=					tokenizer
										snake_case__       :					Union[str, Any]										=					MODEL_MODES[mode]
										if model is None:
																snake_case__       :					str										=					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=SCREAMING_SNAKE_CASE_      ,)
										else:
																snake_case__       :					Union[str, Any]										=					model

				def       _lowerCamelCase					(   self   :     Any      ,*SCREAMING_SNAKE_CASE_   :     int      ,**SCREAMING_SNAKE_CASE_   :     str      ):
										snake_case__       :					Any										=					self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     List[str]      ):
										snake_case__       :					List[str]										=					arg_to_scheduler[self.hparams.lr_scheduler]
										snake_case__       :					Dict										=					get_schedule_func(
										    self.opt      ,num_warmup_steps=self.hparams.warmup_steps      ,num_training_steps=self.total_steps()      )
										snake_case__       :					str										=					{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
										return scheduler

				def       _lowerCamelCase					(   self   :     Any      ):
										snake_case__       :					Tuple										=					self.model
										snake_case__       :					Optional[int]										=					['''bias''', '''LayerNorm.weight''']
										snake_case__       :					Any										=					[
										    {
										        '''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:
																snake_case__       :					str										=					Adafactor(
																    SCREAMING_SNAKE_CASE_      ,lr=self.hparams.learning_rate      ,scale_parameter=SCREAMING_SNAKE_CASE_      ,relative_step=SCREAMING_SNAKE_CASE_      )

										else:
																snake_case__       :					List[str]										=					AdamW(
																    SCREAMING_SNAKE_CASE_      ,lr=self.hparams.learning_rate      ,eps=self.hparams.adam_epsilon      )
										snake_case__       :					str										=					optimizer

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

										return [optimizer], [scheduler]

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ):
										return self.validation_step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ):
										return self.validation_end(SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     List[Any]      ):
										snake_case__       :					Any										=					max(1      ,self.hparams.gpus      )  # TODO: consider num_tpu_cores
										snake_case__       :					int										=					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   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ):
										if mode == "test":
																snake_case__       :					Dict										=					len(self.test_dataloader().dataset      )
										else:
																snake_case__       :					Optional[Any]										=					self.get_dataloader('''train'''      ,self.hparams.train_batch_size      ,shuffle=SCREAMING_SNAKE_CASE_      )
																snake_case__       :					Tuple										=					len(self.train_dataloader().dataset      )

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     bool = False      ):
										raise NotImplementedError('''You must implement this for your task'''      )

				def       _lowerCamelCase					(   self   :     str      ):
										return self.train_loader

				def       _lowerCamelCase					(   self   :     Dict      ):
										return self.get_dataloader('''dev'''      ,self.hparams.eval_batch_size      ,shuffle=SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Tuple      ):
										return self.get_dataloader('''test'''      ,self.hparams.eval_batch_size      ,shuffle=SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ):
										return os.path.join(
										    self.hparams.data_dir      ,'''cached_{}_{}_{}'''.format(
										        SCREAMING_SNAKE_CASE_      ,list(filter(SCREAMING_SNAKE_CASE_      ,self.hparams.model_name_or_path.split('''/'''      )      )      ).pop()      ,str(self.hparams.max_seq_length      )      ,)      ,)

				@pl.utilities.rank_zero_only
				def       _lowerCamelCase					(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Dict[str, Any]      ):
										snake_case__       :					Optional[int]										=					self.output_dir.joinpath('''best_tfmr'''      )
										snake_case__       :					List[Any]										=					self.step_count
										self.model.save_pretrained(SCREAMING_SNAKE_CASE_      )
										self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_      )





				@staticmethod
				def       _lowerCamelCase					(   SCREAMING_SNAKE_CASE_   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ):
										parser.add_argument(
										    '''--model_name_or_path'''      ,default=SCREAMING_SNAKE_CASE_      ,type=SCREAMING_SNAKE_CASE_      ,required=SCREAMING_SNAKE_CASE_      ,help='''Path to pretrained model or model identifier from huggingface.co/models'''      ,)
										parser.add_argument(
										    '''--config_name'''      ,default=''''''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Pretrained config name or path if not the same as model_name'''      )
										parser.add_argument(
										    '''--tokenizer_name'''      ,default=SCREAMING_SNAKE_CASE_      ,type=SCREAMING_SNAKE_CASE_      ,help='''Pretrained tokenizer name or path if not the same as model_name'''      ,)
										parser.add_argument(
										    '''--cache_dir'''      ,default=''''''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''      ,)
										parser.add_argument(
										    '''--encoder_layerdrop'''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Encoder layer dropout probability (Optional). Goes into model.config'''      ,)
										parser.add_argument(
										    '''--decoder_layerdrop'''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Decoder layer dropout probability (Optional). Goes into model.config'''      ,)
										parser.add_argument(
										    '''--dropout'''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Dropout probability (Optional). Goes into model.config'''      ,)
										parser.add_argument(
										    '''--attention_dropout'''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Attention dropout probability (Optional). Goes into model.config'''      ,)
										parser.add_argument('''--learning_rate'''      ,default=5E-5      ,type=SCREAMING_SNAKE_CASE_      ,help='''The initial learning rate for Adam.'''      )
										parser.add_argument(
										    '''--lr_scheduler'''      ,default='''linear'''      ,choices=SCREAMING_SNAKE_CASE_      ,metavar=SCREAMING_SNAKE_CASE_      ,type=SCREAMING_SNAKE_CASE_      ,help='''Learning rate scheduler'''      ,)
										parser.add_argument('''--weight_decay'''      ,default=0.0      ,type=SCREAMING_SNAKE_CASE_      ,help='''Weight decay if we apply some.'''      )
										parser.add_argument('''--adam_epsilon'''      ,default=1E-8      ,type=SCREAMING_SNAKE_CASE_      ,help='''Epsilon for Adam optimizer.'''      )
										parser.add_argument('''--warmup_steps'''      ,default=0      ,type=SCREAMING_SNAKE_CASE_      ,help='''Linear warmup over warmup_steps.'''      )
										parser.add_argument('''--num_workers'''      ,default=4      ,type=SCREAMING_SNAKE_CASE_      ,help='''kwarg passed to DataLoader'''      )
										parser.add_argument('''--num_train_epochs'''      ,dest='''max_epochs'''      ,default=3      ,type=SCREAMING_SNAKE_CASE_      )
										parser.add_argument('''--train_batch_size'''      ,default=3_2      ,type=SCREAMING_SNAKE_CASE_      )
										parser.add_argument('''--eval_batch_size'''      ,default=3_2      ,type=SCREAMING_SNAKE_CASE_      )
										parser.add_argument('''--adafactor'''      ,action='''store_true'''      )







class       __lowercase       (			pl.Callback	):


				'''simple docstring'''


				def       _lowerCamelCase					(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Any      ):
										snake_case__       :					int										=					trainer.lr_schedulers[0]['''scheduler''']
										snake_case__       :					Union[str, Any]										=					{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
										pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     pl.Trainer      ,SCREAMING_SNAKE_CASE_   :     pl.LightningModule      ):
										rank_zero_info('''***** Validation results *****'''      )
										snake_case__       :					Optional[Any]										=					trainer.callback_metrics
										# Log results
										for key in sorted(SCREAMING_SNAKE_CASE_      ):
																if key not in ["log", "progress_bar"]:
																						rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_      ,str(metrics[key]      )      )      )





				def       _lowerCamelCase					(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     pl.Trainer      ,SCREAMING_SNAKE_CASE_   :     pl.LightningModule      ):
										rank_zero_info('''***** Test results *****'''      )
										snake_case__       :					int										=					trainer.callback_metrics
										# Log and save results to file
										snake_case__       :					List[str]										=					os.path.join(pl_module.hparams.output_dir      ,'''test_results.txt'''      )
										with open(SCREAMING_SNAKE_CASE_      ,'''w'''      ) as writer:
																for key in sorted(SCREAMING_SNAKE_CASE_      ):
																						if key not in ["log", "progress_bar"]:
																												rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_      ,str(metrics[key]      )      )      )
																												writer.write('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_      ,str(metrics[key]      )      )      )







def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


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

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase=None      ,			lowerCamelCase=True      ,			lowerCamelCase=[]      ,			lowerCamelCase=None      ,			lowerCamelCase=None      ,			**lowerCamelCase      ,			):
						'''simple docstring'''


						pl.seed_everything(args.seed		)

						# init model
						snake_case__       :					List[str]										=					Path(model.hparams.output_dir		)
						odir.mkdir(exist_ok=lowerCamelCase		)

						# add custom checkpoints
						if checkpoint_callback is None:
												snake_case__       :					List[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(lowerCamelCase		)
						if logging_callback is None:
												snake_case__       :					Dict										=					LoggingCallback()

						snake_case__       :					List[Any]										=					{}

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

						if args.gpus > 1:
												snake_case__       :					Tuple										=					'''ddp'''

						snake_case__       :					Tuple										=					args.accumulate_grad_batches
						snake_case__       :					Union[str, Any]										=					extra_train_kwargs.get('''accelerator'''      ,			lowerCamelCase		)
						snake_case__       :					int										=					extra_train_kwargs.get('''profiler'''      ,			lowerCamelCase		)

						snake_case__       :					List[str]										=					pl.Trainer.from_argparse_args(
						    lowerCamelCase      ,			weights_summary=lowerCamelCase      ,			callbacks=[logging_callback] + extra_callbacks      ,			logger=lowerCamelCase      ,			checkpoint_callback=lowerCamelCase      ,			**lowerCamelCase      ,			)

						if args.do_train:
												trainer.fit(lowerCamelCase		)

						return trainer


