



"""simple docstring"""






import json
import os
from typing import Optional, Tuple

import regex as re

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


__snake_case							:     Union[str, Any]   =					logging.get_logger(__name__)

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

__snake_case							:     List[Any]   =					{
    """vocab_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"""},
    """merges_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"""},
}

__snake_case							:     Optional[int]   =					{
    """ctrl""": 256,
}

__snake_case							:     Any   =					{
    """Pregnancy""": 168_629,
    """Christianity""": 7_675,
    """Explain""": 106_423,
    """Fitness""": 63_440,
    """Saving""": 63_163,
    """Ask""": 27_171,
    """Ass""": 95_985,
    """Joke""": 163_509,
    """Questions""": 45_622,
    """Thoughts""": 49_605,
    """Retail""": 52_342,
    """Feminism""": 164_338,
    """Writing""": 11_992,
    """Atheism""": 192_263,
    """Netflix""": 48_616,
    """Computing""": 39_639,
    """Opinion""": 43_213,
    """Alone""": 44_967,
    """Funny""": 58_917,
    """Gaming""": 40_358,
    """Human""": 4_088,
    """India""": 1_331,
    """Joker""": 77_138,
    """Diet""": 36_206,
    """Legal""": 11_859,
    """Norman""": 4_939,
    """Tip""": 72_689,
    """Weight""": 52_343,
    """Movies""": 46_273,
    """Running""": 23_425,
    """Science""": 2_090,
    """Horror""": 37_793,
    """Confession""": 60_572,
    """Finance""": 12_250,
    """Politics""": 16_360,
    """Scary""": 191_985,
    """Support""": 12_654,
    """Technologies""": 32_516,
    """Teenage""": 66_160,
    """Event""": 32_769,
    """Learned""": 67_460,
    """Notion""": 182_770,
    """Wikipedia""": 37_583,
    """Books""": 6_665,
    """Extract""": 76_050,
    """Confessions""": 102_701,
    """Conspiracy""": 75_932,
    """Links""": 63_674,
    """Narcissus""": 150_425,
    """Relationship""": 54_766,
    """Relationships""": 134_796,
    """Reviews""": 41_671,
    """News""": 4_256,
    """Translation""": 26_820,
    """multilingual""": 128_406,
}


def     _lowercase       (						__snake_case			)		->     List[str]:
   __lowerCAmelCase   :						List[str]				= set()
   __lowerCAmelCase   :						List[Any]				= word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)			)
      __lowerCAmelCase   :						List[str]				= char

   __lowerCAmelCase   :						Optional[int]				= set(snake_case_			)
   return pairs





class     A__     (						UpperCamelCase_							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE					   =			PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE					   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE					   =			CONTROL_CODES

 def __init__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Dict="<unk>" ,    **_SCREAMING_SNAKE_CASE:	int)       -> List[str]:

    """simple docstring"""

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

    with open(__A ,    encoding="utf-8") as vocab_handle:
       __lowerCAmelCase   :						List[str]				= json.load(__A)
    __lowerCAmelCase   :						Optional[Any]				= {v: k for k, v in self.encoder.items()}
    with open(__A ,    encoding="utf-8") as merges_handle:
       __lowerCAmelCase   :						Optional[Any]				= merges_handle.read().split("\n")[1:-1]
    __lowerCAmelCase   :						Optional[int]				= [tuple(merge.split()) for merge in merges]
    __lowerCAmelCase   :						List[Any]				= dict(zip(__A ,    range(len(__A))))
    __lowerCAmelCase   :						str				= {}

 @property
 def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Any:

    """simple docstring"""

    return len(self.encoder)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Optional[int]:

    """simple docstring"""

    return dict(self.encoder ,    **self.added_tokens_encoder)

 def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	str)       -> Optional[Any]:

    """simple docstring"""

    if token in self.cache:
       return self.cache[token]
    __lowerCAmelCase   :						Optional[Any]				= tuple(__A)
    __lowerCAmelCase   :						List[Any]				= tuple(list(word[:-1]) + [word[-1] + "</w>"])
    __lowerCAmelCase   :						str				= get_pairs(__A)

    if not pairs:
       return token

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

          if word[i] == first and i < len(__A) - 1 and word[i + 1] == second:
             new_word.append(first + second)
             i += 2
          else:
             new_word.append(word[i])
             i += 1
       __lowerCAmelCase   :						Any				= tuple(__A)
       __lowerCAmelCase   :						Union[str, Any]				= new_word
       if len(__A) == 1:
          break
       else:
          __lowerCAmelCase   :						int				= get_pairs(__A)
    __lowerCAmelCase   :						List[str]				= "@@ ".join(__A)
    __lowerCAmelCase   :						Tuple				= word[:-4]
    __lowerCAmelCase   :						Union[str, Any]				= word
    return word

 def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	Optional[int])       -> str:

    """simple docstring"""

    __lowerCAmelCase   :						Optional[int]				= []

    __lowerCAmelCase   :						Optional[int]				= re.findall(r"\S+\n?" ,    __A)

    for token in words:
       split_tokens.extend(list(self.bpe(__A).split(" ")))
    return split_tokens

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Dict)       -> Optional[Any]:

    """simple docstring"""

    return self.encoder.get(__A ,    self.encoder.get(self.unk_token))

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Union[str, Any])       -> int:

    """simple docstring"""

    return self.decoder.get(__A ,    self.unk_token)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int])       -> Dict:

    """simple docstring"""

    __lowerCAmelCase   :						int				= " ".join(__A).replace("@@ " ,    "").strip()
    return out_string







 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Optional[str] = None)       -> Optional[Any]:

    """simple docstring"""

    if not os.path.isdir(__A):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
       return
    __lowerCAmelCase   :						Any				= os.path.join(
        __A ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
    __lowerCAmelCase   :						Any				= os.path.join(
        __A ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])

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

    __lowerCAmelCase   :						str				= 0
    with open(__A ,    "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 _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!")
             __lowerCAmelCase   :						Tuple				= token_index
          writer.write(" ".join(__A) + "\n")
          index += 1

    return vocab_file, merge_file

 # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
 #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
 #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
 #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
 #     return ''.join(tokens_generated_so_far)




"""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


__snake_case							:     Optional[Any]   =					logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__snake_case							:     List[Any]   =					{
    '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							:     Dict   =					{
    '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							:     int   =					sorted(arg_to_scheduler.keys())
__snake_case							:     Optional[Any]   =					'{' + ', '.join(arg_to_scheduler_choices) + '}'



class     A__     (						pl.LightningModule							):




	'''simple docstring'''





	def __init__(  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	argparse.Namespace ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=None ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]="base" ,    _SCREAMING_SNAKE_CASE:	Optional[int]=None ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=None ,    _SCREAMING_SNAKE_CASE:	Any=None ,    **_SCREAMING_SNAKE_CASE:	int ,    )       -> int:

				"""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(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Optional[Any]				= 0
				__lowerCAmelCase   :						int				= Path(self.hparams.output_dir)
				__lowerCAmelCase   :						List[str]				= self.hparams.cache_dir if self.hparams.cache_dir else None
				if config is None:
							__lowerCAmelCase   :						Optional[int]				= 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:
							__lowerCAmelCase   :						PretrainedConfig				= config

				__lowerCAmelCase   :						Dict				= ("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:
							__lowerCAmelCase   :						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:
							__lowerCAmelCase   :						PreTrainedTokenizer				= tokenizer
				__lowerCAmelCase   :						int				= MODEL_MODES[mode]
				if model is None:
							__lowerCAmelCase   :						Any				= 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:
							__lowerCAmelCase   :						Union[str, Any]				= model

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    *_SCREAMING_SNAKE_CASE:	Union[str, Any] ,    **_SCREAMING_SNAKE_CASE:	Dict)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Optional[int]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.model
				__lowerCAmelCase   :						Tuple				= ["bias", "LayerNorm.weight"]
				__lowerCAmelCase   :						Optional[Any]				= [
				    {
				        "params": [
				            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)
				        ],  # check this named paramters
				        "weight_decay": self.hparams.weight_decay,
				    },
				    {
				        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
				        "weight_decay": 0.0,
				    },
				]
				if self.hparams.adafactor:
							__lowerCAmelCase   :						Dict				= Adafactor(
							    _SCREAMING_SNAKE_CASE ,    lr=self.hparams.learning_rate ,    scale_parameter=_SCREAMING_SNAKE_CASE ,    relative_step=_SCREAMING_SNAKE_CASE)

				else:
							__lowerCAmelCase   :						Dict				= AdamW(
							    _SCREAMING_SNAKE_CASE ,    lr=self.hparams.learning_rate ,    eps=self.hparams.adam_epsilon)
				__lowerCAmelCase   :						int				= optimizer

				__lowerCAmelCase   :						Optional[int]				= self.get_lr_scheduler()

				return [optimizer], [scheduler]

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	Any)       -> Union[str, Any]:

				"""simple docstring"""

				return self.validation_step(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Any)       -> List[Any]:

				"""simple docstring"""

				return self.validation_end(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> int:

				"""simple docstring"""

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

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

				"""simple docstring"""

				if stage == "test":
							__lowerCAmelCase   :						List[str]				= len(self.test_dataloader().dataset)
				else:
							__lowerCAmelCase   :						Tuple				= self.get_dataloader("train" ,    self.hparams.train_batch_size ,    shuffle=_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Tuple				= len(self.train_dataloader().dataset)

	def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	bool = False)       -> int:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> List[str]:

				"""simple docstring"""

				return self.train_loader

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Tuple:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Optional[int]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	Any)       -> int:

				"""simple docstring"""

				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 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Dict[str, Any])       -> None:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.output_dir.joinpath("best_tfmr")
				__lowerCAmelCase   :						str				= self.step_count
				self.model.save_pretrained(_SCREAMING_SNAKE_CASE)
				self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE)







	@staticmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Dict)       -> Optional[Any]:

				"""simple docstring"""

				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=str(Path(_SCREAMING_SNAKE_CASE).parent / "test_run" / "cache") ,    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=32 ,    type=_SCREAMING_SNAKE_CASE)
				parser.add_argument("--eval_batch_size" ,    default=32 ,    type=_SCREAMING_SNAKE_CASE)
				parser.add_argument("--adafactor" ,    action="store_true")






class     A__     (						pl.Callback							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Tuple)       -> Any:

				"""simple docstring"""

				if (
				    trainer.is_global_zero and trainer.global_rank == 0
				):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
							pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class     A__     (						pl.Callback							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Tuple)       -> Optional[int]:

				"""simple docstring"""

				for name, param in pl_module.model.rag.named_parameters():
							if param.grad is None:
										print(_SCREAMING_SNAKE_CASE)






class     A__     (						pl.Callback							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	int)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= trainer.lr_schedulers[0]["scheduler"]
				__lowerCAmelCase   :						str				= {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr())}
				pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	pl.Trainer ,    _SCREAMING_SNAKE_CASE:	pl.LightningModule)       -> str:

				"""simple docstring"""

				rank_zero_info("***** Validation results *****")
				__lowerCAmelCase   :						Tuple				= 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 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	pl.Trainer ,    _SCREAMING_SNAKE_CASE:	pl.LightningModule)       -> List[Any]:

				"""simple docstring"""

				rank_zero_info("***** Test results *****")
				__lowerCAmelCase   :						Optional[int]				= trainer.callback_metrics
				# Log and save results to file
				__lowerCAmelCase   :						List[Any]				= 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     _lowercase       (						__snake_case      ,__snake_case			)		->     None:
			#  To allow all pl args uncomment the following line
			#  parser = pl.Trainer.add_argparse_args(parser)
			parser.add_argument(
			    "--output_dir"      ,default=str(Path(__snake_case			).parent / "test_run" / "model_checkpoints"			)      ,type=__snake_case      ,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=__snake_case      ,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=__snake_case			)
			parser.add_argument("--max_grad_norm"      ,dest="gradient_clip_val"      ,default=1.0      ,type=__snake_case      ,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=__snake_case      ,default=1      ,help="Number of updates steps to accumulate before performing a backward/update pass."      ,)
			parser.add_argument("--seed"      ,type=__snake_case      ,default=42      ,help="random seed for initialization"			)
			parser.add_argument(
			    "--data_dir"      ,default=str(Path(__snake_case			).parent / "test_run" / "dummy-train-data"			)      ,type=__snake_case      ,help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."      ,)





def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=None      ,__snake_case=True      ,__snake_case=[]      ,__snake_case=None      ,__snake_case=None      ,**__snake_case      ,)		->     Tuple:
			pl.seed_everything(args.seed			)

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

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

			__lowerCAmelCase   :						int				= {}

			if args.fpaa:
						__lowerCAmelCase   :						Optional[int]				= 16

			if args.gpus > 1:
						__lowerCAmelCase   :						int				= "auto"
						__lowerCAmelCase   :						List[Any]				= "ddp"

			__lowerCAmelCase   :						Optional[int]				= args.accumulate_grad_batches
			__lowerCAmelCase   :						int				= None
			__lowerCAmelCase   :						Any				= "auto"

			__lowerCAmelCase   :						Optional[Any]				= pl.Trainer.from_argparse_args(
			    __snake_case      ,weights_summary=__snake_case      ,callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]      ,logger=__snake_case      ,val_check_interval=1      ,num_sanity_val_steps=2      ,**__snake_case      ,)

			if args.do_train:
						trainer.fit(__snake_case			)

			else:
						print("RAG modeling tests with new set functions successfuly executed!"			)
			return trainer