





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
					if is_torch_available():
										import torch
					if is_tf_available():
										import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


UpperCAmelCase_   				=  logging.get_logger(__name__)

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

UpperCAmelCase_   				=  {
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
}

UpperCAmelCase_   				=  {
    """Salesforce/codegen-350M-mono""": 2048,
}





@lru_cache()
def     lowerCamelCase__  (  )      ->			Optional[int]:



					'''simple docstring'''




					_snake_case						=					(
					    list(range(ord('!'    )							,     ord('~'    ) + 1    )    ) + list(range(ord('¡'    )							,     ord('¬'    ) + 1    )    ) + list(range(ord('®'    )							,     ord('ÿ'    ) + 1    )    )
					)
					_snake_case						=					bs[:]
					_snake_case						=					0
					for b in range(2**8    ):
										if b not in bs:
															bs.append(UpperCamelCase__    )
															cs.append(2**8 + n    )
															n += 1
					_snake_case						=					[chr(UpperCamelCase__    ) for n in cs]
					return dict(zip(UpperCamelCase__							,     UpperCamelCase__    )    )





def     lowerCamelCase__  (  UpperCamelCase__      :							Any    )      ->			Tuple:



					'''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       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						VOCAB_FILES_NAMES
					lowerCAmelCase_				=						PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				=						['''input_ids''', '''attention_mask''']




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_="replace"					,    lowerCAmelCase_="<|endoftext|>"					,    lowerCAmelCase_="<|endoftext|>"					,    lowerCAmelCase_="<|endoftext|>"					,    lowerCAmelCase_=None					,    lowerCAmelCase_=False					,    lowerCAmelCase_=False					,    **lowerCAmelCase_					,    )		->				Union[str, Any]:
										_snake_case						=					AddedToken(lowerCAmelCase_					,    lstrip=lowerCAmelCase_					,    rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	) else bos_token
										_snake_case						=					AddedToken(lowerCAmelCase_					,    lstrip=lowerCAmelCase_					,    rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	) else eos_token
										_snake_case						=					AddedToken(lowerCAmelCase_					,    lstrip=lowerCAmelCase_					,    rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	) else unk_token
										_snake_case						=					AddedToken(lowerCAmelCase_					,    lstrip=lowerCAmelCase_					,    rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	) else pad_token
										super().__init__(
										    errors=lowerCAmelCase_					,    unk_token=lowerCAmelCase_					,    bos_token=lowerCAmelCase_					,    eos_token=lowerCAmelCase_					,    pad_token=lowerCAmelCase_					,    add_prefix_space=lowerCAmelCase_					,    add_bos_token=lowerCAmelCase_					,    **lowerCAmelCase_					,    )
										_snake_case						=					add_bos_token

										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()}
										_snake_case						=					errors  # how to handle errors in decoding
										_snake_case						=					bytes_to_unicode()
										_snake_case						=					{v: k for k, v in self.byte_encoder.items()}
										with open(lowerCAmelCase_					,    encoding='utf-8'	) as merges_handle:
															_snake_case						=					merges_handle.read().split('\n'	)[1:-1]
										_snake_case						=					[tuple(merge.split()	) for merge in bpe_merges]
										_snake_case						=					dict(zip(lowerCAmelCase_					,    range(len(lowerCAmelCase_	)	)	)	)
										_snake_case						=					{}
										_snake_case						=					add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										_snake_case						=					re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)




					@property
					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										return len(self.encoder	)




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										return dict(self.encoder					,    **self.added_tokens_encoder	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Any:
										if token in self.cache:
															return self.cache[token]
										_snake_case						=					tuple(lowerCAmelCase_	)
										_snake_case						=					get_pairs(lowerCAmelCase_	)

										if not pairs:
															return token

										while True:
															_snake_case						=					min(lowerCAmelCase_					,    key=lambda lowerCAmelCase_	: self.bpe_ranks.get(lowerCAmelCase_					,    float('inf'	)	)	)
															if bigram not in self.bpe_ranks:
																				break
															_snake_case				,  _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_	)
										_snake_case						=					word
										return word




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_=None	)		->				List[Any]:
										if self.add_bos_token:
															_snake_case						=					[self.bos_token_id]
										else:
															_snake_case						=					[]

										_snake_case						=					bos_token_ids + token_ids_a

										if token_ids_a is None:
															return output

										return output + bos_token_ids + token_ids_a




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[str]:
										_snake_case						=					[]
										for token in re.findall(self.pat					,    lowerCAmelCase_	):
															_snake_case						=					''.join(
															    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase_	).split(' '	)	)
										return bpe_tokens




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Optional[Any]:
										return self.encoder.get(lowerCAmelCase_					,    self.encoder.get(self.unk_token	)	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[Any]:
										return self.decoder.get(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				int:
										_snake_case						=					''.join(lowerCAmelCase_	)
										_snake_case						=					bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'					,    errors=self.errors	)
										return text




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_ = None	)		->				Tuple[str]:
										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:
															writer.write('#version: 0.2\n'	)
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,    key=lambda lowerCAmelCase_	: 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 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_=False					,    **lowerCAmelCase_	)		->				List[str]:
										_snake_case						=					kwargs.pop('add_prefix_space'					,    self.add_prefix_space	)
										if is_split_into_words or add_prefix_space:
															_snake_case						=					' ' + text
										return (text, kwargs)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_ = False					,    lowerCAmelCase_ = None					,    lowerCAmelCase_ = None					,    **lowerCAmelCase_					,    )		->				str:
										_snake_case						=					super()._decode(
										    token_ids=lowerCAmelCase_					,    skip_special_tokens=lowerCAmelCase_					,    clean_up_tokenization_spaces=lowerCAmelCase_					,    **lowerCAmelCase_					,    )

										if truncate_before_pattern is not None and len(lowerCAmelCase_	) > 0:
															_snake_case						=					self.truncate(lowerCAmelCase_					,    lowerCAmelCase_	)

										return decoded_text






					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Optional[int]:
										def find_re(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	):
															_snake_case						=					pattern.search(lowerCAmelCase_					,    lowerCAmelCase_	)
															return m.start() if m else -1

										_snake_case						=					[re.compile(lowerCAmelCase_					,    re.MULTILINE	) for pattern in truncate_before_pattern]

										_snake_case						=					list(re.finditer('^print'					,    lowerCAmelCase_					,    re.MULTILINE	)	)

										if len(lowerCAmelCase_	) > 1:
															_snake_case						=					completion[: prints[1].start()]

										_snake_case						=					list(re.finditer('^def'					,    lowerCAmelCase_					,    re.MULTILINE	)	)

										if len(lowerCAmelCase_	) > 1:
															_snake_case						=					completion[: defs[1].start()]

										_snake_case						=					0

										_snake_case						=					[
										    pos for pos in [find_re(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	) for terminal in terminals] if pos != -1
										]

										if len(lowerCAmelCase_	) > 0:
															return completion[: min(lowerCAmelCase_	)]
										else:
															return completion










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


UpperCAmelCase_   				=  logging.getLogger(__name__)

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

UpperCAmelCase_   				=  {
    """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
UpperCAmelCase_   				=  {
    """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
}
UpperCAmelCase_   				=  sorted(arg_to_scheduler.keys())
UpperCAmelCase_   				=  """{""" + """, """.join(arg_to_scheduler_choices) + """}"""
class       UpperCamelCase_		(	pl.LightningModule       ):




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_=None					,    lowerCAmelCase_="base"					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    **lowerCAmelCase_					,    )		->				Union[str, Any]:
										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(lowerCAmelCase_	)
										_snake_case						=					0
										_snake_case						=					Path(self.hparams.output_dir	)
										_snake_case						=					self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
															_snake_case						=					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=lowerCAmelCase_					,    **lowerCAmelCase_					,    )
										else:
															_snake_case						=					config

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

										if tokenizer is None:
															_snake_case						=					AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,    cache_dir=lowerCAmelCase_					,    )
										else:
															_snake_case						=					tokenizer
										_snake_case						=					MODEL_MODES[mode]
										if model is None:
															_snake_case						=					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=lowerCAmelCase_					,    )
										else:
															_snake_case						=					model




					def 						lowerCAmelCase      (		self					,    *lowerCAmelCase_					,    **lowerCAmelCase_	)		->				str:
										_snake_case						=					self.model_type.from_pretrained(*lowerCAmelCase_					,    **lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				List[Any]:
										_snake_case						=					arg_to_scheduler[self.hparams.lr_scheduler]
										_snake_case						=					get_schedule_func(
										    self.opt					,    num_warmup_steps=self.hparams.warmup_steps					,    num_training_steps=self.total_steps()	)
										_snake_case						=					{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
										return scheduler




					def 						lowerCAmelCase      (		self	)		->				int:
										_snake_case						=					self.model
										_snake_case						=					['bias', 'LayerNorm.weight']
										_snake_case						=					[
										    {
										        '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						=					Adafactor(
															    lowerCAmelCase_					,    lr=self.hparams.learning_rate					,    scale_parameter=lowerCAmelCase_					,    relative_step=lowerCAmelCase_	)

										else:
															_snake_case						=					AdamW(
															    lowerCAmelCase_					,    lr=self.hparams.learning_rate					,    eps=self.hparams.adam_epsilon	)
										_snake_case						=					optimizer

										_snake_case						=					self.get_lr_scheduler()

										return [optimizer], [scheduler]




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				List[str]:
										return self.validation_step(lowerCAmelCase_					,    lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Optional[int]:
										return self.validation_end(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				int:
										_snake_case						=					max(1					,    self.hparams.gpus	)  # TODO: consider num_tpu_cores
										_snake_case						=					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					,    lowerCAmelCase_	)		->				Tuple:
										if mode == "test":
															_snake_case						=					len(self.test_dataloader().dataset	)
										else:
															_snake_case						=					self.get_dataloader('train'					,    self.hparams.train_batch_size					,    shuffle=lowerCAmelCase_	)
															_snake_case						=					len(self.train_dataloader().dataset	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_ = False	)		->				Dict:
										raise NotImplementedError('You must implement this for your task'	)




					def 						lowerCAmelCase      (		self	)		->				Any:
										return self.train_loader




					def 						lowerCAmelCase      (		self	)		->				int:
										return self.get_dataloader('dev'					,    self.hparams.eval_batch_size					,    shuffle=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Tuple:
										return self.get_dataloader('test'					,    self.hparams.eval_batch_size					,    shuffle=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				int:
										return os.path.join(
										    self.hparams.data_dir					,    'cached_{}_{}_{}'.format(
										        lowerCAmelCase_					,    list(filter(lowerCAmelCase_					,    self.hparams.model_name_or_path.split('/'	)	)	).pop()					,    str(self.hparams.max_seq_length	)					,    )					,    )




					@pl.utilities.rank_zero_only
					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				None:
										_snake_case						=					self.output_dir.joinpath('best_tfmr'	)
										_snake_case						=					self.step_count
										self.model.save_pretrained(lowerCAmelCase_	)
										self.tokenizer.save_pretrained(lowerCAmelCase_	)






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





class       UpperCamelCase_		(	pl.Callback       ):




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				int:
										_snake_case						=					trainer.lr_schedulers[0]['scheduler']
										_snake_case						=					{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
										pl_module.logger.log_metrics(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				str:
										rank_zero_info('***** Validation results *****'	)
										_snake_case						=					trainer.callback_metrics
										# Log results
										for key in sorted(lowerCAmelCase_	):
															if key not in ["log", "progress_bar"]:
																				rank_zero_info('{} = {}\n'.format(lowerCAmelCase_					,    str(metrics[key]	)	)	)






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





def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Union[str, Any]    )      ->			None:



					'''simple docstring'''




					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     lowerCamelCase__  (  UpperCamelCase__      :							BaseTransformer							,     UpperCamelCase__      :							argparse.Namespace							,     UpperCamelCase__      :							Optional[Any]=None							,     UpperCamelCase__      :							Union[str, Any]=True							,     UpperCamelCase__      :							Optional[Any]=[]							,     UpperCamelCase__      :							int=None							,     UpperCamelCase__      :							Tuple=None							,     **UpperCamelCase__      :							List[Any]							,     )      ->			Any:



					'''simple docstring'''




					pl.seed_everything(args.seed    )

					# init model
					_snake_case						=					Path(model.hparams.output_dir    )
					odir.mkdir(exist_ok=UpperCamelCase__    )

					# add custom checkpoints
					if checkpoint_callback is None:
										_snake_case						=					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:
										_snake_case						=					LoggingCallback()

					_snake_case						=					{}

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

					if args.gpus > 1:
										_snake_case						=					'ddp'

					_snake_case						=					args.accumulate_grad_batches
					_snake_case						=					extra_train_kwargs.get('accelerator'							,     UpperCamelCase__    )
					_snake_case						=					extra_train_kwargs.get('profiler'							,     UpperCamelCase__    )

					_snake_case						=					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



