

"""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


_a				   =							logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

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




class       _lowerCAmelCase						(			pl.LightningModule     ):







 """simple docstring"""







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

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

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

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


 def       _lowercase				(    self							:    Tuple,      *UpperCAmelCase__							:    Optional[int],      **UpperCAmelCase__							:    Tuple			):
    __lowercase			=		self.model_type.from_pretrained(*UpperCAmelCase__,      **UpperCAmelCase__			)


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


 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		self.model
    __lowercase			=		["bias", "LayerNorm.weight"]
    __lowercase			=		[
        {
            "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:
       __lowercase			=		Adafactor(
           UpperCAmelCase__,      lr=self.hparams.learning_rate,      scale_parameter=UpperCAmelCase__,      relative_step=UpperCAmelCase__			)

    else:
       __lowercase			=		AdamW(
           UpperCAmelCase__,      lr=self.hparams.learning_rate,      eps=self.hparams.adam_epsilon			)
    __lowercase			=		optimizer

    __lowercase			=		self.get_lr_scheduler()

    return [optimizer], [scheduler]


 def       _lowercase				(    self							:    List[Any],      UpperCAmelCase__							:    Any,      UpperCAmelCase__							:    str			):
    return self.validation_step(UpperCAmelCase__,      UpperCAmelCase__			)


 def       _lowercase				(    self							:    Union[str, Any],      UpperCAmelCase__							:    Optional[Any]			):
    return self.validation_end(UpperCAmelCase__			)


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


 def       _lowercase				(    self							:    str,      UpperCAmelCase__							:    List[Any]			):
    if mode == "test":
       __lowercase			=		len(self.test_dataloader().dataset			)
    else:
       __lowercase			=		self.get_dataloader("train",      self.hparams.train_batch_size,      shuffle=UpperCAmelCase__			)
       __lowercase			=		len(self.train_dataloader().dataset			)


 def       _lowercase				(    self							:    Optional[int],      UpperCAmelCase__							:    str,      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    bool = False			):
    raise NotImplementedError("You must implement this for your task"			)


 def       _lowercase				(    self							:    List[str]			):
    return self.train_loader


 def       _lowercase				(    self							:    str			):
    return self.get_dataloader("dev",      self.hparams.eval_batch_size,      shuffle=UpperCAmelCase__			)


 def       _lowercase				(    self							:    Optional[int]			):
    return self.get_dataloader("test",      self.hparams.eval_batch_size,      shuffle=UpperCAmelCase__			)


 def       _lowercase				(    self							:    str,      UpperCAmelCase__							:    Tuple			):
    return os.path.join(
        self.hparams.data_dir,      "cached_{}_{}_{}".format(
            UpperCAmelCase__,      list(filter(UpperCAmelCase__,      self.hparams.model_name_or_path.split("/"			)			)			).pop(),      str(self.hparams.max_seq_length			),      ),      )


 @pl.utilities.rank_zero_only
 def       _lowercase				(    self							:    str,      UpperCAmelCase__							:    Dict[str, Any]			):
    __lowercase			=		self.output_dir.joinpath("best_tfmr"			)
    __lowercase			=		self.step_count
    self.model.save_pretrained(UpperCAmelCase__			)
    self.tokenizer.save_pretrained(UpperCAmelCase__			)



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







class       _lowerCAmelCase						(			pl.Callback     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Any,      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    int			):
    __lowercase			=		trainer.lr_schedulers[0]["scheduler"]
    __lowercase			=		{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
    pl_module.logger.log_metrics(UpperCAmelCase__			)


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



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







def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : Dict)			->		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 						_A						(						UpperCamelCase_  : BaseTransformer,    UpperCamelCase_  : argparse.Namespace,    UpperCamelCase_  : Optional[Any]=None,    UpperCamelCase_  : List[str]=True,    UpperCamelCase_  : Optional[int]=[],    UpperCamelCase_  : str=None,    UpperCamelCase_  : Optional[int]=None,    **UpperCamelCase_  : int,    )			->		Optional[Any]:
   '''simple docstring'''


   pl.seed_everything(args.seed)

   # init model
   __lowercase			=		Path(model.hparams.output_dir)
   odir.mkdir(exist_ok=UpperCamelCase_)

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

   __lowercase			=		{}

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

   if args.gpus > 1:
      __lowercase			=		"ddp"

   __lowercase			=		args.accumulate_grad_batches
   __lowercase			=		extra_train_kwargs.get("accelerator",    UpperCamelCase_)
   __lowercase			=		extra_train_kwargs.get("profiler",    UpperCamelCase_)

   __lowercase			=		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







"""simple docstring"""





import shutil

import requests




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : bool = False,    UpperCamelCase_  : str = ".")			->		dict:
   '''simple docstring'''


   __lowercase			=		"https://api.nasa.gov/planetary/apod"
   return requests.get(UpperCamelCase_,    params={"api_key": api_key}).json()




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : str = ".")			->		dict:
   '''simple docstring'''


   __lowercase			=		get_apod_data(UpperCamelCase_)
   __lowercase			=		apod_data["url"]
   __lowercase			=		img_url.split("/")[-1]
   __lowercase			=		requests.get(UpperCamelCase_,    stream=UpperCamelCase_)

   with open(F"""{path}/{img_name}""",    "wb+") as img_file:
      shutil.copyfileobj(response.raw,    UpperCamelCase_)
   del response
   return apod_data




def 						_A						(						UpperCamelCase_  : str)			->		dict:
   '''simple docstring'''


   __lowercase			=		"https://images-api.nasa.gov/search"
   return requests.get(UpperCamelCase_,    params={"q": query}).json()


if __name__ == "__main__":
   print(save_apod('YOUR API KEY'))
   _a				   =							get_archive_data('apollo 2011')['collection']['items']
   print(apollo_aaaa_items[0]['data'][0]['description'])




