code: 
  
  
  
  
  
  
  """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:		Dict				,			UpperCamelCase_:		argparse.Namespace				,			UpperCamelCase_:		Tuple=None				,			UpperCamelCase_:		Any="base"				,			UpperCamelCase_:		List[str]=None				,			UpperCamelCase_:		Tuple=None				,			UpperCamelCase_:		Optional[int]=None				,			**UpperCamelCase_:		str				,			):
       super().__init__()
       # TODO: move to self.save_hyperparameters()
       # self.save_hyperparameters()
       # can also expand arguments into trainer signature for easier reading
  
       self.save_hyperparameters(UpperCamelCase_					)
       UpperCamelCase_     =0
       UpperCamelCase_     =Path(self.hparams.output_dir					)
       UpperCamelCase_     =self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
          UpperCamelCase_     =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:
          UpperCamelCase_     =config
  
       UpperCamelCase_     =("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:
          UpperCamelCase_     =AutoTokenizer.from_pretrained(
              self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,			cache_dir=UpperCamelCase_				,			)
       else:
          UpperCamelCase_     =tokenizer
       UpperCamelCase_     =MODEL_MODES[mode]
       if model is None:
          UpperCamelCase_     =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:
          UpperCamelCase_     =model
    def 					UpperCamelCase__   (      self:		Tuple				,			*UpperCamelCase_:		Union[str, Any]				,			**UpperCamelCase_:		List[str]					):
       UpperCamelCase_     =self.model_type.from_pretrained(*UpperCamelCase_				,			**UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		str					):
       UpperCamelCase_     =arg_to_scheduler[self.hparams.lr_scheduler]
       UpperCamelCase_     =get_schedule_func(
           self.opt				,			num_warmup_steps=self.hparams.warmup_steps				,			num_training_steps=self.total_steps()					)
       UpperCamelCase_     ={"scheduler": scheduler, "interval": "step", "frequency": 1}
       return scheduler
    def 					UpperCamelCase__   (      self:		List[str]					):
       UpperCamelCase_     =self.model
       UpperCamelCase_     =["bias", "LayerNorm.weight"]
       UpperCamelCase_     =[
           {
               "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_     =Adafactor(
              UpperCamelCase_				,			lr=self.hparams.learning_rate				,			scale_parameter=UpperCamelCase_				,			relative_step=UpperCamelCase_					)
  
       else:
          UpperCamelCase_     =AdamW(
              UpperCamelCase_				,			lr=self.hparams.learning_rate				,			eps=self.hparams.adam_epsilon					)
       UpperCamelCase_     =optimizer
  
       UpperCamelCase_     =self.get_lr_scheduler()
  
       return [optimizer], [scheduler]
    def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		Union[str, Any]				,			UpperCamelCase_:		str					):
       return self.validation_step(UpperCamelCase_				,			UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Tuple				,			UpperCamelCase_:		Dict					):
       return self.validation_end(UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Optional[Any]					):
       UpperCamelCase_     =max(1				,			self.hparams.gpus					)  # TODO: consider num_tpu_cores
       UpperCamelCase_     =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:		Optional[Any]				,			UpperCamelCase_:		List[str]					):
       if mode == "test":
          UpperCamelCase_     =len(self.test_dataloader().dataset					)
       else:
          UpperCamelCase_     =self.get_dataloader("train"				,			self.hparams.train_batch_size				,			shuffle=UpperCamelCase_					)
          UpperCamelCase_     =len(self.train_dataloader().dataset					)
    def 					UpperCamelCase__   (      self:		Union[str, Any]				,			UpperCamelCase_:		str				,			UpperCamelCase_:		int				,			UpperCamelCase_:		bool = False					):
       raise NotImplementedError("You must implement this for your task"					)
    def 					UpperCamelCase__   (      self:		Dict					):
       return self.train_loader
    def 					UpperCamelCase__   (      self:		str					):
       return self.get_dataloader("dev"				,			self.hparams.eval_batch_size				,			shuffle=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		List[Any]					):
       return self.get_dataloader("test"				,			self.hparams.eval_batch_size				,			shuffle=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Optional[Any]				,			UpperCamelCase_:		Optional[Any]					):
       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 					UpperCamelCase__   (      self:		Optional[Any]				,			UpperCamelCase_:		Dict[str, Any]					):
       UpperCamelCase_     =self.output_dir.joinpath("best_tfmr"					)
       UpperCamelCase_     =self.step_count
       self.model.save_pretrained(UpperCamelCase_					)
       self.tokenizer.save_pretrained(UpperCamelCase_					)
  
  
  
  
  
    @staticmethod
    def 					UpperCamelCase__   (      UpperCamelCase_:		int				,			UpperCamelCase_:		str					):
       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=32				,			type=UpperCamelCase_					)
       parser.add_argument("--eval_batch_size"				,			default=32				,			type=UpperCamelCase_					)
       parser.add_argument("--adafactor"				,			action="store_true"					)
  
  
  
  
  class 						__lowerCAmelCase   (			pl.Callback       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def 					UpperCamelCase__   (      self:		int				,			UpperCamelCase_:		Tuple				,			UpperCamelCase_:		Dict					):
       UpperCamelCase_     =trainer.lr_schedulers[0]["scheduler"]
       UpperCamelCase_     ={f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
       pl_module.logger.log_metrics(UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		List[Any]				,			UpperCamelCase_:		pl.Trainer				,			UpperCamelCase_:		pl.LightningModule					):
       rank_zero_info("***** Validation results *****"					)
       UpperCamelCase_     =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 					UpperCamelCase__   (      self:		Dict				,			UpperCamelCase_:		pl.Trainer				,			UpperCamelCase_:		pl.LightningModule					):
       rank_zero_info("***** Test results *****"					)
       UpperCamelCase_     =trainer.callback_metrics
       # Log and save results to file
       UpperCamelCase_     =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 						_UpperCamelCase			(  A    ,       A			):
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         "--output_dir"    ,       default=A    ,       type=A    ,       required=A    ,       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=A    ,       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=A			)
     parser.add_argument("--max_grad_norm"    ,       dest="gradient_clip_val"    ,       default=1.0    ,       type=A    ,       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=A    ,       default=1    ,       help="Number of updates steps to accumulate before performing a backward/update pass."    ,       )
     parser.add_argument("--seed"    ,       type=A    ,       default=42    ,       help="random seed for initialization"			)
     parser.add_argument(
         "--data_dir"    ,       default=A    ,       type=A    ,       required=A    ,       help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."    ,       )
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A    ,       A=None    ,       A=True    ,       A=[]    ,       A=None    ,       A=None    ,       **A    ,       ):
     pl.seed_everything(args.seed			)
  
     # init model
     UpperCamelCase_     =Path(model.hparams.output_dir			)
     odir.mkdir(exist_ok=A			)
  
     # add custom checkpoints
     if checkpoint_callback is None:
        UpperCamelCase_     =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(A			)
     if logging_callback is None:
        UpperCamelCase_     =LoggingCallback()
  
     UpperCamelCase_     ={}
  
     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
        UpperCamelCase_     =16
        UpperCamelCase_     =args.fpaa_opt_level
  
     if args.gpus > 1:
        UpperCamelCase_     ="ddp"
  
     UpperCamelCase_     =args.accumulate_grad_batches
     UpperCamelCase_     =extra_train_kwargs.get("accelerator"    ,       A			)
     UpperCamelCase_     =extra_train_kwargs.get("profiler"    ,       A			)
  
     UpperCamelCase_     =pl.Trainer.from_argparse_args(
         A    ,       weights_summary=A    ,       callbacks=[logging_callback] + extra_callbacks    ,       logger=A    ,       checkpoint_callback=A    ,       **A    ,       )
  
     if args.do_train:
        trainer.fit(A			)
  
     return trainer
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from datetime import date
  
  import requests
  
  A_			  =    "https://api.carbonintensity.org.uk/intensity"
  
  
  
  
  def 						_UpperCamelCase			(  ):
     UpperCamelCase_     =requests.get(A			).json()["data"][0]
     return last_half_hour["intensity"]["actual"]
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A			):
     return requests.get(f"""{BASE_URL}/{start}/{end}"""			).json()["data"]
  
  
  if __name__ == "__main__":
      for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
          print("from {from} to {to}: {intensity[actual]}".format(**entry))
      print(f'''{fetch_last_half_hour() = }''')
  