
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  ):





     def __init__(self      :    Tuple				,    _A      :    argparse.Namespace				,    _A      :    Union[str, Any]=None				,    _A      :    Tuple="base"				,    _A      :    Tuple=None				,    _A      :    str=None				,    _A      :    str=None				,    **_A      :    Dict				,    )						->						Optional[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(_A   )
         snake_case      =					0
         snake_case      =					Path(self.hparams.output_dir   )
         snake_case      =					self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
             snake_case      =					AutoConfig.from_pretrained(
                 self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path				,    **({"num_labels": num_labels} if num_labels is not None else {})				,    cache_dir=_A				,    **_A				,    )
         else:
             snake_case      =					config

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

         if tokenizer is None:
             snake_case      =					AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,    cache_dir=_A				,    )
         else:
             snake_case      =					tokenizer
         snake_case      =					MODEL_MODES[mode]
         if model is None:
             snake_case      =					self.model_type.from_pretrained(
                 self.hparams.model_name_or_path				,    from_tf=bool(".ckpt" in self.hparams.model_name_or_path   )				,    config=self.config				,    cache_dir=_A				,    )
         else:
             snake_case      =					model





     def 		UpperCAmelCase(self      :    str				,    *_A      :    Dict				,    **_A      :    Union[str, Any]   )						->						Optional[Any]:
         snake_case      =					self.model_type.from_pretrained(*_A				,    **_A   )





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





     def 		UpperCAmelCase(self      :    List[Any]   )						->						Tuple:
         snake_case      =					self.model
         snake_case      =					["bias", "LayerNorm.weight"]
         snake_case      =					[
             {
                 "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay   )],
                 "weight_decay": self.hparams.weight_decay,
             },
             {
                 "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay   )],
                 "weight_decay": 0.0,
             },
         ]
         if self.hparams.adafactor:
             snake_case      =					Adafactor(
                 _A				,    lr=self.hparams.learning_rate				,    scale_parameter=_A				,    relative_step=_A   )

         else:
             snake_case      =					AdamW(
                 _A				,    lr=self.hparams.learning_rate				,    eps=self.hparams.adam_epsilon   )
         snake_case      =					optimizer

         snake_case      =					self.get_lr_scheduler()

         return [optimizer], [scheduler]





     def 		UpperCAmelCase(self      :    str				,    _A      :    Tuple				,    _A      :    Union[str, Any]   )						->						List[str]:
         return self.validation_step(_A				,    _A   )





     def 		UpperCAmelCase(self      :    Optional[int]				,    _A      :    int   )						->						Optional[Any]:
         return self.validation_end(_A   )





     def 		UpperCAmelCase(self      :    int   )						->						int:
         snake_case      =					max(1				,    self.hparams.gpus   )  # TODO: consider num_tpu_cores
         snake_case      =					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





     def 		UpperCAmelCase(self      :    Tuple				,    _A      :    List[str]   )						->						List[str]:
         if mode == "test":
             snake_case      =					len(self.test_dataloader().dataset   )
         else:
             snake_case      =					self.get_dataloader("train"				,    self.hparams.train_batch_size				,    shuffle=_A   )
             snake_case      =					len(self.train_dataloader().dataset   )





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    str				,    _A      :    int				,    _A      :    bool = False   )						->						str:
         raise NotImplementedError("You must implement this for your task"   )





     def 		UpperCAmelCase(self      :    Union[str, Any]   )						->						List[Any]:
         return self.train_loader





     def 		UpperCAmelCase(self      :    Optional[int]   )						->						int:
         return self.get_dataloader("dev"				,    self.hparams.eval_batch_size				,    shuffle=_A   )





     def 		UpperCAmelCase(self      :    Tuple   )						->						Optional[Any]:
         return self.get_dataloader("test"				,    self.hparams.eval_batch_size				,    shuffle=_A   )





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





     @pl.utilities.rank_zero_only
     def 		UpperCAmelCase(self      :    List[str]				,    _A      :    Dict[str, Any]   )						->						None:
         snake_case      =					self.output_dir.joinpath("best_tfmr"   )
         snake_case      =					self.step_count
         self.model.save_pretrained(_A   )
         self.tokenizer.save_pretrained(_A   )





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







class      lowerCamelCase		(  pl.Callback  ):





     def 		UpperCAmelCase(self      :    int				,    _A      :    List[str]				,    _A      :    Union[str, Any]   )						->						str:
         snake_case      =					trainer.lr_schedulers[0]["scheduler"]
         snake_case      =					{f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
         pl_module.logger.log_metrics(_A   )





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





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



def 						lowercase_			(   A__ ,		A__		)							->							None:
    """simple docstring"""

    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 						lowercase_			(   A__ ,		A__ ,		A__=None ,		A__=True ,		A__=[] ,		A__=None ,		A__=None ,		**A__ ,		)							->							Union[str, Any]:
    """simple docstring"""

    pl.seed_everything(args.seed		)

    # init model
    snake_case      =					Path(model.hparams.output_dir		)
    odir.mkdir(exist_ok=A__		)

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

    snake_case      =					{}

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

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

    snake_case      =					args.accumulate_grad_batches
    snake_case      =					extra_train_kwargs.get("accelerator" ,		A__		)
    snake_case      =					extra_train_kwargs.get("profiler" ,		A__		)

    snake_case      =					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



from typing import Any

import numpy as np







class      lowerCamelCase		:





     def __init__(self      :    Optional[Any]				,    _A      :    np.ndarray				,    _A      :    int   )						->						None:
         # Check if RHS is negative
         if np.any(tableau[:, -1]				,    where=tableau[:, -1] < 0   ):
             raise ValueError("RHS must be > 0"   )

         snake_case      =					tableau
         snake_case     ,							snake_case      =					tableau.shape

         # Number of decision variables x1, x2, x3...
         snake_case      =					n_vars

         # Number of artificial variables to be minimised
         snake_case      =					len(np.where(tableau[self.n_vars : -1] == -1   )[0]   )

         # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
         snake_case      =					(self.n_art_vars > 0) + 1

         # Number of slack variables added to make inequalities into equalities
         snake_case      =					self.n_rows - self.n_stages

         # Objectives for each stage
         snake_case      =					["max"]

         # In two stage simplex, first minimise then maximise
         if self.n_art_vars:
             self.objectives.append("min"   )

         snake_case      =					[""]

         # Index of current pivot row and column
         snake_case      =					None
         snake_case      =					None

         # Does objective row only contain (non)-negative values?
         snake_case      =					False





     @staticmethod
     def 		UpperCAmelCase(*_A      :    int   )						->						list[str]:
         if len(_A   ) != 3:
             raise ValueError("Must provide n_vars, n_slack, and n_art_vars"   )

         if not all(x >= 0 and isinstance(_A				,    _A   ) for x in args   ):
             raise ValueError("All arguments must be non-negative integers"   )

         # decision | slack | artificial
         snake_case      =					["x", "s", "a"]
         snake_case      =					[]
         for i in range(3   ):
             for j in range(args[i]   ):
                 titles.append(string_starts[i] + str(j + 1   )   )
         titles.append("RHS"   )
         return titles





     def 		UpperCAmelCase(self      :    int				,    _A      :    np.ndarray   )						->						tuple[Any, Any]:
         snake_case      =					self.objectives[-1]

         # Find entries of highest magnitude in objective rows
         snake_case      =					(objective == "min") - (objective == "max")
         snake_case      =					np.argmax(sign * tableau[0, : self.n_vars]   )

         # Choice is only valid if below 0 for maximise, and above for minimise
         if sign * self.tableau[0, col_idx] <= 0:
             snake_case      =					True
             return 0, 0

         # Pivot row is chosen as having the lowest quotient when elements of
         # the pivot column divide the right-hand side

         # Slice excluding the objective rows
         snake_case      =					slice(self.n_stages				,    self.n_rows   )

         # RHS
         snake_case      =					tableau[s, -1]

         # Elements of pivot column within slice
         snake_case      =					tableau[s, col_idx]

         # Array filled with nans
         snake_case      =					np.full(self.n_rows - self.n_stages				,    np.nan   )

         # If element in pivot column is greater than zeron_stages, return
         # quotient or nan otherwise
         snake_case      =					np.divide(_A				,    _A				,    out=_A				,    where=divisor > 0   )

         # Arg of minimum quotient excluding the nan values. n_stages is added
         # to compensate for earlier exclusion of objective columns
         snake_case      =					np.nanargmin(_A   ) + self.n_stages
         return row_idx, col_idx





     def 		UpperCAmelCase(self      :    int				,    _A      :    np.ndarray				,    _A      :    int				,    _A      :    int   )						->						np.ndarray:
         snake_case      =					tableau[row_idx].copy()

         snake_case      =					piv_row[col_idx]

         # Entry becomes 1
         piv_row *= 1 / piv_val

         # Variable in pivot column becomes basic, ie the only non-zero entry
         for idx, coeff in enumerate(tableau[:, col_idx]   ):
             tableau[idx] += -coeff * piv_row
         snake_case      =					piv_row
         return tableau





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    np.ndarray   )						->						np.ndarray:
         self.objectives.pop()

         if not self.objectives:
             return tableau

         # Slice containing ids for artificial columns
         snake_case      =					slice(-self.n_art_vars - 1				,    -1   )

         # Delete the artificial variable columns
         snake_case      =					np.delete(_A				,    _A				,    axis=1   )

         # Delete the objective row of the first stage
         snake_case      =					np.delete(_A				,    0				,    axis=0   )

         snake_case      =					1
         self.n_rows -= 1
         snake_case      =					0
         snake_case      =					False
         return tableau





     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						dict[Any, Any]:
         # Stop simplex algorithm from cycling.
         for _ in range(1_0_0   ):
             # Completion of each stage removes an objective. If both stages
             # are complete, then no objectives are left
             if not self.objectives:
                 snake_case      =					self.generate_col_titles(
                     self.n_vars				,    self.n_slack				,    self.n_art_vars   )

                 # Find the values of each variable at optimal solution
                 return self.interpret_tableau(self.tableau				,    self.col_titles   )

             snake_case     ,							snake_case      =					self.find_pivot(self.tableau   )

             # If there are no more negative values in objective row
             if self.stop_iter:
                 # Delete artificial variable columns and rows. Update attributes
                 snake_case      =					self.change_stage(self.tableau   )
             else:
                 snake_case      =					self.pivot(self.tableau				,    _A				,    _A   )
         return {}





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    np.ndarray				,    _A      :    list[str]   )						->						dict[str, float]:
         snake_case      =					{"P": abs(tableau[0, -1]   )}

         for i in range(self.n_vars   ):
             # Gives ids of nonzero entries in the ith column
             snake_case      =					np.nonzero(tableau[:, i]   )
             snake_case      =					len(nonzero[0]   )

             # First entry in the nonzero ids
             snake_case      =					nonzero[0][0]
             snake_case      =					tableau[nonzero_rowidx, i]

             # If there is only one nonzero value in column, which is one
             if n_nonzero == nonzero_val == 1:
                 snake_case      =					tableau[nonzero_rowidx, -1]
                 snake_case      =					rhs_val

        # Check for basic variables
         for title in col_titles:
             # Don't add RHS or slack variables to output dict
             if title[0] not in "R-s-a":
                 output_dict.setdefault(_A				,    0   )
         return output_dict


if __name__ == "__main__":
   import doctest

   doctest.testmod()

