import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


__snake_case			      =    logging.get_logger(__name__)

__snake_case			      =    {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__snake_case			      =    {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
}

__snake_case			      =    {
    '''openai-gpt''': 5_12,
}





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






      '''simple docstring'''





      UpperCAmelCase    : Any      =text.strip()
      if not text:
            return []
      UpperCAmelCase    : Dict      =text.split()
      return tokens







class 			__snake_case   (     lowerCamelCase__   ):
 def __init__(  self		,   snake_case__=True		,   snake_case__=None		,   snake_case__=True		,   snake_case__=None		,   snake_case__=True		,   )	->							Optional[int]:
       '''simple docstring'''





       if never_split is None:
             UpperCAmelCase    : Optional[int]      =[]
       UpperCAmelCase    : Optional[int]      =do_lower_case
       UpperCAmelCase    : str      =set(snake_case__						)
       UpperCAmelCase    : Optional[int]      =tokenize_chinese_chars
       UpperCAmelCase    : Optional[int]      =strip_accents
       UpperCAmelCase    : List[str]      =do_split_on_punc
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__=None						)	->							Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase    : Dict      =self.never_split.union(set(snake_case__						)						) if never_split else self.never_split
       UpperCAmelCase    : List[Any]      =self._clean_text(snake_case__						)

       # This was added on November 1st, 2018 for the multilingual and Chinese
       # models. This is also applied to the English models now, but it doesn't
       # matter since the English models were not trained on any Chinese data
       # and generally don't have any Chinese data in them (there are Chinese
       # characters in the vocabulary because Wikipedia does have some Chinese
       # words in the English Wikipedia.).
       if self.tokenize_chinese_chars:
             UpperCAmelCase    : List[str]      =self._tokenize_chinese_chars(snake_case__						)
       # prevents treating the same character with different unicode codepoints as different characters
       UpperCAmelCase    : Dict      =unicodedata.normalize('''NFC'''		,   snake_case__						)
       UpperCAmelCase    : Tuple      =whitespace_tokenize(snake_case__						)
       UpperCAmelCase    : str      =[]
       for token in orig_tokens:
             if token not in never_split:
                   if self.do_lower_case:
                         UpperCAmelCase    : Union[str, Any]      =token.lower()
                         if self.strip_accents is not False:
                               UpperCAmelCase    : Any      =self._run_strip_accents(snake_case__						)
                   elif self.strip_accents:
                         UpperCAmelCase    : str      =self._run_strip_accents(snake_case__						)
             split_tokens.extend(self._run_split_on_punc(snake_case__		,   snake_case__						)						)

       UpperCAmelCase    : Tuple      =whitespace_tokenize(''' '''.join(snake_case__						)						)
       return output_tokens
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =unicodedata.normalize('''NFD'''		,   snake_case__						)
       UpperCAmelCase    : Optional[Any]      =[]
       for char in text:
             UpperCAmelCase    : List[str]      =unicodedata.category(snake_case__						)
             if cat == "Mn":
                   continue
             output.append(snake_case__						)
       return "".join(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__=None						)	->							Any:
       '''simple docstring'''





       if not self.do_split_on_punc or (never_split is not None and text in never_split):
             return [text]
       UpperCAmelCase    : List[str]      =list(snake_case__						)
       UpperCAmelCase    : Any      =0
       UpperCAmelCase    : Dict      =True
       UpperCAmelCase    : Optional[Any]      =[]
       while i < len(snake_case__						):
             UpperCAmelCase    : Tuple      =chars[i]
             if _is_punctuation(snake_case__						):
                   output.append([char]						)
                   UpperCAmelCase    : Tuple      =True
             else:
                   if start_new_word:
                         output.append([]						)
                   UpperCAmelCase    : List[str]      =False
                   output[-1].append(snake_case__						)
             i += 1

       return ["".join(snake_case__						) for x in output]
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Dict:
       '''simple docstring'''





       UpperCAmelCase    : str      =[]
       for char in text:
             UpperCAmelCase    : Optional[Any]      =ord(snake_case__						)
             if self._is_chinese_char(snake_case__						):
                   output.append(''' '''						)
                   output.append(snake_case__						)
                   output.append(''' '''						)
             else:
                   output.append(snake_case__						)
       return "".join(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							List[str]:
       '''simple docstring'''





       if (
           (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
           or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
           or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
           or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
           or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
           or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
           or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
           or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
       ):  #
             return True

       return False






 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : int      =[]
       for char in text:
             UpperCAmelCase    : Optional[Any]      =ord(snake_case__						)
             if cp == 0 or cp == 0xF_F_F_D or _is_control(snake_case__						):
                   continue
             if _is_whitespace(snake_case__						):
                   output.append(''' '''						)
             else:
                   output.append(snake_case__						)
       return "".join(snake_case__						)





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  List[str]:






      '''simple docstring'''





      UpperCAmelCase    : int      =set()
      UpperCAmelCase    : Tuple      =word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)						)
            UpperCAmelCase    : List[Any]      =char
      return pairs





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  Dict:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      =text.replace('''—'''		,					'''-'''						)
      UpperCAmelCase    : str      =text.replace('''–'''		,					'''-'''						)
      UpperCAmelCase    : Dict      =text.replace('''―'''		,					'''-'''						)
      UpperCAmelCase    : Optional[int]      =text.replace('''…'''		,					'''...'''						)
      UpperCAmelCase    : int      =text.replace('''´'''		,					'''\''''						)
      UpperCAmelCase    : Optional[int]      =re.sub(R'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'''		,					R''' \1 '''		,					__lowerCAmelCase						)
      UpperCAmelCase    : List[Any]      =re.sub(R'''\s*\n\s*'''		,					''' \n '''		,					__lowerCAmelCase						)
      UpperCAmelCase    : Union[str, Any]      =re.sub(R'''[^\S\n]+'''		,					''' '''		,					__lowerCAmelCase						)
      return text.strip()







class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   Dict     =      VOCAB_FILES_NAMES
 __lowerCamelCase    :   Union[str, Any]     =      PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :   Tuple     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :   Dict     =      ["""input_ids""", """attention_mask"""]
 def __init__(  self		,   snake_case__		,   snake_case__		,   snake_case__="<unk>"		,   **snake_case__						)	->							Any:
       '''simple docstring'''





       super().__init__(unk_token=snake_case__		,   **snake_case__						)

       try:
             import ftfy
             from spacy.lang.en import English

             UpperCAmelCase    : Dict      =English()
             UpperCAmelCase    : str      =_nlp.tokenizer
             UpperCAmelCase    : int      =ftfy.fix_text
       except ImportError:
             logger.warning('''ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'''						)
             UpperCAmelCase    : Union[str, Any]      =BasicTokenizer(do_lower_case=snake_case__						)
             UpperCAmelCase    : Any      =None

       with open(snake_case__		,   encoding='''utf-8'''						) as vocab_handle:
             UpperCAmelCase    : Dict      =json.load(snake_case__						)
       UpperCAmelCase    : int      ={v: k for k, v in self.encoder.items()}
       with open(snake_case__		,   encoding='''utf-8'''						) as merges_handle:
             UpperCAmelCase    : Union[str, Any]      =merges_handle.read().split('''\n'''						)[1:-1]
       UpperCAmelCase    : Optional[int]      =[tuple(merge.split()						) for merge in merges]
       UpperCAmelCase    : Dict      =dict(zip(snake_case__		,   range(len(snake_case__						)						)						)						)
       UpperCAmelCase    : int      ={}
 @property
 def 	UpperCAmelCase__       (  self						)	->							Optional[int]:
       '''simple docstring'''





       return True
 @property
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       return len(self.encoder						)
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       return dict(self.encoder		,   **self.added_tokens_encoder						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =tuple(token[:-1]						) + (token[-1] + '''</w>''',)
       if token in self.cache:
             return self.cache[token]
       UpperCAmelCase    : Optional[Any]      =get_pairs(snake_case__						)

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

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

                   if word[i] == first and i < len(snake_case__						) - 1 and word[i + 1] == second:
                         new_word.append(first + second						)
                         i += 2
                   else:
                         new_word.append(word[i]						)
                         i += 1
             UpperCAmelCase    : Dict      =tuple(snake_case__						)
             UpperCAmelCase    : Any      =new_word
             if len(snake_case__						) == 1:
                   break
             else:
                   UpperCAmelCase    : Union[str, Any]      =get_pairs(snake_case__						)
       UpperCAmelCase    : Union[str, Any]      =''' '''.join(snake_case__						)
       if word == "\n  </w>":
             UpperCAmelCase    : str      ='''\n</w>'''
       UpperCAmelCase    : List[Any]      =word
       return word
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       UpperCAmelCase    : Dict      =[]
       if self.fix_text is None:
             # Using BERT's BasicTokenizer
             UpperCAmelCase    : Optional[Any]      =self.nlp.tokenize(snake_case__						)
             for token in text:
                   split_tokens.extend(list(self.bpe(snake_case__						).split(''' '''						)						)						)
       else:
             # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
             UpperCAmelCase    : List[Any]      =self.nlp(text_standardize(self.fix_text(snake_case__						)						)						)
             for token in text:
                   split_tokens.extend(list(self.bpe(token.text.lower()						).split(''' '''						)						)						)
       return split_tokens
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Union[str, Any]:
       '''simple docstring'''





       return self.encoder.get(snake_case__		,   self.encoder.get(self.unk_token						)						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Any:
       '''simple docstring'''





       return self.decoder.get(snake_case__		,   self.unk_token						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Dict:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =''''''.join(snake_case__						).replace('''</w>'''		,   ''' '''						).strip()
       return out_string






 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__ = None						)	->							Tuple[str]:
       '''simple docstring'''





       if not os.path.isdir(snake_case__						):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
             return
       UpperCAmelCase    : Tuple      =os.path.join(
           snake_case__		,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)
       UpperCAmelCase    : Tuple      =os.path.join(
           snake_case__		,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']						)

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

       UpperCAmelCase    : List[Any]      =0
       with open(snake_case__		,   '''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 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!'''						)
                         UpperCAmelCase    : str      =token_index
                   writer.write(''' '''.join(snake_case__						) + '''\n'''						)
                   index += 1

       return vocab_file, merge_file

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


__snake_case			      =    logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

__snake_case			      =    {
    '''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
__snake_case			      =    {
    '''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
}
__snake_case			      =    sorted(arg_to_scheduler.keys())
__snake_case			      =    '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''







class 			__snake_case   (     pl.LightningModule   ):
 def __init__(  self		,   snake_case__		,   snake_case__=None		,   snake_case__="base"		,   snake_case__=None		,   snake_case__=None		,   snake_case__=None		,   **snake_case__		,   )	->							List[str]:
       '''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(snake_case__						)
       UpperCAmelCase    : Union[str, Any]      =0
       UpperCAmelCase    : List[Any]      =Path(self.hparams.output_dir						)
       UpperCAmelCase    : List[str]      =self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
             UpperCAmelCase    : Union[str, Any]      =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=snake_case__		,   **snake_case__		,   )
       else:
             UpperCAmelCase    : PretrainedConfig      =config

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

       if tokenizer is None:
             UpperCAmelCase    : Dict      =AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,   cache_dir=snake_case__		,   )
       else:
             UpperCAmelCase    : PreTrainedTokenizer      =tokenizer
       UpperCAmelCase    : Dict      =MODEL_MODES[mode]
       if model is None:
             UpperCAmelCase    : 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=snake_case__		,   )
       else:
             UpperCAmelCase    : Union[str, Any]      =model
 def 	UpperCAmelCase__       (  self		,   *snake_case__		,   **snake_case__						)	->							str:
       '''simple docstring'''





       UpperCAmelCase    : Optional[int]      =self.model_type.from_pretrained(*snake_case__		,   **snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =arg_to_scheduler[self.hparams.lr_scheduler]
       UpperCAmelCase    : Dict      =get_schedule_func(
           self.opt		,   num_warmup_steps=self.hparams.warmup_steps		,   num_training_steps=self.total_steps()						)
       UpperCAmelCase    : int      ={'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
       return scheduler
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =self.model
       UpperCAmelCase    : Dict      =['''bias''', '''LayerNorm.weight''']
       UpperCAmelCase    : Dict      =[
           {
               '''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:
             UpperCAmelCase    : Any      =Adafactor(
                 snake_case__		,   lr=self.hparams.learning_rate		,   scale_parameter=snake_case__		,   relative_step=snake_case__						)

       else:
             UpperCAmelCase    : Optional[int]      =AdamW(
                 snake_case__		,   lr=self.hparams.learning_rate		,   eps=self.hparams.adam_epsilon						)
       UpperCAmelCase    : int      =optimizer

       UpperCAmelCase    : Union[str, Any]      =self.get_lr_scheduler()

       return [optimizer], [scheduler]
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__						)	->							Tuple:
       '''simple docstring'''





       return self.validation_step(snake_case__		,   snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Optional[int]:
       '''simple docstring'''





       return self.validation_end(snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       UpperCAmelCase    : str      =max(1		,   self.hparams.gpus						)  # TODO: consider num_tpu_cores
       UpperCAmelCase    : Dict      =self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
       return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Union[str, Any]:
       '''simple docstring'''





       if mode == "test":
             UpperCAmelCase    : str      =len(self.test_dataloader().dataset						)
       else:
             UpperCAmelCase    : Union[str, Any]      =self.get_dataloader('''train'''		,   self.hparams.train_batch_size		,   shuffle=snake_case__						)
             UpperCAmelCase    : str      =len(self.train_dataloader().dataset						)
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__ = False						)	->							List[str]:
       '''simple docstring'''





       raise NotImplementedError('''You must implement this for your task'''						)
 def 	UpperCAmelCase__       (  self						)	->							Union[str, Any]:
       '''simple docstring'''





       return self.train_loader
 def 	UpperCAmelCase__       (  self						)	->							List[Any]:
       '''simple docstring'''





       return self.get_dataloader('''dev'''		,   self.hparams.eval_batch_size		,   shuffle=snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       return self.get_dataloader('''test'''		,   self.hparams.eval_batch_size		,   shuffle=snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Tuple:
       '''simple docstring'''





       return os.path.join(
           self.hparams.data_dir		,   '''cached_{}_{}_{}'''.format(
               snake_case__		,   list(filter(snake_case__		,   self.hparams.model_name_or_path.split('''/'''						)						)						).pop()		,   str(self.hparams.max_seq_length						)		,   )		,   )
 @pl.utilities.rank_zero_only
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							None:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =self.output_dir.joinpath('''best_tfmr'''						)
       UpperCAmelCase    : int      =self.step_count
       self.model.save_pretrained(snake_case__						)
       self.tokenizer.save_pretrained(snake_case__						)






 @staticmethod
 def 	UpperCAmelCase__       (  snake_case__		,   snake_case__						)	->							Any:
       '''simple docstring'''





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







class 			__snake_case   (     pl.Callback   ):
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__						)	->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Tuple      =trainer.lr_schedulers[0]['''scheduler''']
       UpperCAmelCase    : Dict      ={f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
       pl_module.logger.log_metrics(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__						)	->							Optional[int]:
       '''simple docstring'''





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






 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__						)	->							Tuple:
       '''simple docstring'''





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





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  None:






      '''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		,					)->  Optional[int]:






      '''simple docstring'''





      pl.seed_everything(args.seed						)

      # init model
      UpperCAmelCase    : List[Any]      =Path(model.hparams.output_dir						)
      odir.mkdir(exist_ok=__lowerCAmelCase						)

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

      UpperCAmelCase    : Any      ={}

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

      if args.gpus > 1:
            UpperCAmelCase    : str      ='''ddp'''

      UpperCAmelCase    : str      =args.accumulate_grad_batches
      UpperCAmelCase    : List[Any]      =extra_train_kwargs.get('''accelerator'''		,					__lowerCAmelCase						)
      UpperCAmelCase    : Optional[int]      =extra_train_kwargs.get('''profiler'''		,					__lowerCAmelCase						)

      UpperCAmelCase    : 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
