
"""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


SCREAMING_SNAKE_CASE_			      =					logging.getLogger(__name__)

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

SCREAMING_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
SCREAMING_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
}
SCREAMING_SNAKE_CASE_			      =					sorted(arg_to_scheduler.keys())
SCREAMING_SNAKE_CASE_			      =					'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
class      lowerCAmelCase_						(							pl.LightningModule				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_  ,							snake_case_=None  ,							snake_case_="base"  ,							snake_case_=None  ,							snake_case_=None  ,							snake_case_=None  ,							**snake_case_  ,							)   ->       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(snake_case_							)
													__lowerCAmelCase        =       0
													__lowerCAmelCase        =       Path(self.hparams.output_dir							)
													__lowerCAmelCase        =       self.hparams.cache_dir if self.hparams.cache_dir else None
													if config is None:
																			__lowerCAmelCase        =       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:
																			__lowerCAmelCase        =       config

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




							def  A__		(				self  ,							*snake_case_  ,							**snake_case_							)   ->       str:
													__lowerCAmelCase        =       self.model_type.from_pretrained(*snake_case_  ,							**snake_case_							)




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




							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       self.model
													__lowerCAmelCase        =       ["""bias""", """LayerNorm.weight"""]
													__lowerCAmelCase        =       [
													    {
													        """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:
																			__lowerCAmelCase        =       Adafactor(
																			    snake_case_  ,							lr=self.hparams.learning_rate  ,							scale_parameter=snake_case_  ,							relative_step=snake_case_							)

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

													__lowerCAmelCase        =       self.get_lr_scheduler()

													return [optimizer], [scheduler]




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Dict:
													return self.validation_step(snake_case_  ,							snake_case_							)




							def  A__		(				self  ,							snake_case_							)   ->       Union[str, Any]:
													return self.validation_end(snake_case_							)




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




							def  A__		(				self  ,							snake_case_							)   ->       List[Any]:
													if stage == "test":
																			__lowerCAmelCase        =       len(self.test_dataloader().dataset							)
													else:
																			__lowerCAmelCase        =       self.get_dataloader("""train"""  ,							self.hparams.train_batch_size  ,							shuffle=snake_case_							)
																			__lowerCAmelCase        =       len(self.train_dataloader().dataset							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_ = False							)   ->       Any:
													raise NotImplementedError("""You must implement this for your task"""							)




							def  A__		(				self							)   ->       Any:
													return self.train_loader




							def  A__		(				self							)   ->       Union[str, Any]:
													return self.get_dataloader("""dev"""  ,							self.hparams.eval_batch_size  ,							shuffle=snake_case_							)




							def  A__		(				self							)   ->       Optional[int]:
													return self.get_dataloader("""test"""  ,							self.hparams.eval_batch_size  ,							shuffle=snake_case_							)




							def  A__		(				self  ,							snake_case_							)   ->       List[str]:
													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  A__		(				self  ,							snake_case_							)   ->       None:
													__lowerCAmelCase        =       self.output_dir.joinpath("""best_tfmr"""							)
													__lowerCAmelCase        =       self.step_count
													self.model.save_pretrained(snake_case_							)
													self.tokenizer.save_pretrained(snake_case_							)




							@staticmethod
							def  A__		(				snake_case_  ,							snake_case_							)   ->       Union[str, Any]:
													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      lowerCAmelCase_						(							pl.Callback				):



							'''simple docstring'''





							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Dict:
													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      lowerCAmelCase_						(							pl.Callback				):



							'''simple docstring'''





							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Any:
													__lowerCAmelCase        =       trainer.lr_schedulers[0]["""scheduler"""]
													__lowerCAmelCase        =       {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
													pl_module.logger.log_metrics(snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Dict:
													rank_zero_info("""***** Validation results *****"""							)
													__lowerCAmelCase        =       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  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Union[str, Any]:
													rank_zero_info("""***** Test results *****"""							)
													__lowerCAmelCase        =       trainer.callback_metrics
													# Log and save results to file
													__lowerCAmelCase        =       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 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						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 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase=None				,  _lowerCAmelCase=True				,  _lowerCAmelCase=None				,  _lowerCAmelCase=[]				,  _lowerCAmelCase=None				,  _lowerCAmelCase=None				,  **_lowerCAmelCase				,  ):
						pl.seed_everything(args.seed		)

						# init model
						__lowerCAmelCase        =       Path(model.hparams.output_dir		)
						odir.mkdir(exist_ok=_lowerCAmelCase		)

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

						__lowerCAmelCase        =       {}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												__lowerCAmelCase        =       16
												# train_params["amp_level"] = args.fp16_opt_level

						if args.gpus > 1:
												__lowerCAmelCase        =       """auto"""  # "ddp"
												__lowerCAmelCase        =       """ddp"""

						__lowerCAmelCase        =       args.accumulate_grad_batches
						__lowerCAmelCase        =       None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
						__lowerCAmelCase        =       """auto"""

						__lowerCAmelCase        =       pl.Trainer.from_argparse_args(
						    _lowerCAmelCase				,  weights_summary=_lowerCAmelCase				,  callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]				,  logger=_lowerCAmelCase				,  **_lowerCAmelCase				,  )

						if args.do_train:
												trainer.fit(_lowerCAmelCase		)

						return trainer


"""simple docstring"""
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def 				lowercase      (_lowerCAmelCase=None				,  _lowerCAmelCase=None		):
						return field(default_factory=lambda: default				,  metadata=_lowerCAmelCase		)
@dataclass
class      lowerCAmelCase_						:



							'''simple docstring'''





							_snake_case		     =						field(
							    metadata={'''help''': '''The csv file to plot.'''}							,							)
							_snake_case		     =						field(
							    default=A__							,							metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}							,							)
							_snake_case		     =						field(
							    default=A__							,							metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}							,							)
							_snake_case		     =						field(
							    default=A__							,							metadata={'''help''': '''Disable logarithmic scale when plotting'''}							,							)
							_snake_case		     =						field(
							    default=A__							,							metadata={
							        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
							    }							,							)
							_snake_case		     =						field(
							    default=A__							,							metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}							,							)
							_snake_case		     =						list_field(
							    default=A__							,							metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}				)

def 				lowercase      (_lowerCAmelCase		):
						try:
												int(_lowerCAmelCase		)
												return True
						except ValueError:
												return False

def 				lowercase      (_lowerCAmelCase		):
						try:
												float(_lowerCAmelCase		)
												return True
						except ValueError:
												return False
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_							)   ->       List[Any]:
													__lowerCAmelCase        =       args
													__lowerCAmelCase        =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}							)

													with open(self.args.csv_file  ,							newline=""""""							) as csv_file:
																			__lowerCAmelCase        =       csv.DictReader(snake_case_							)
																			for row in reader:
																									__lowerCAmelCase        =       row["""model"""]
																									self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]							)							)
																									self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]							)							)
																									if can_convert_to_int(row["""result"""]							):
																															# value is not None
																															__lowerCAmelCase        =       int(row["""result"""]							)
																									elif can_convert_to_float(row["""result"""]							):
																															# value is not None
																															__lowerCAmelCase        =       float(row["""result"""]							)




							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase ,      __lowerCAmelCase        =       plt.subplots()
													__lowerCAmelCase        =       """Time usage""" if self.args.is_time else """Memory usage"""
													__lowerCAmelCase        =       title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

													if not self.args.no_log_scale:
																			# set logarithm scales
																			ax.set_xscale("""log"""							)
																			ax.set_yscale("""log"""							)

													for axis in [ax.xaxis, ax.yaxis]:
																			axis.set_major_formatter(ScalarFormatter()							)

													for model_name_idx, model_name in enumerate(self.result_dict.keys()							):
																			__lowerCAmelCase        =       sorted(set(self.result_dict[model_name]["""bsz"""]							)							)
																			__lowerCAmelCase        =       sorted(set(self.result_dict[model_name]["""seq_len"""]							)							)
																			__lowerCAmelCase        =       self.result_dict[model_name]["""result"""]

																			((__lowerCAmelCase) ,      (__lowerCAmelCase))        =       (
																			    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																			)

																			__lowerCAmelCase        =       (
																			    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
																			)

																			for inner_loop_value in inner_loop_array:
																									if self.args.plot_along_batch:
																															__lowerCAmelCase        =       np.asarray(
																															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,							dtype=snake_case_  ,							)
																									else:
																															__lowerCAmelCase        =       np.asarray(
																															    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]  ,							dtype=np.floataa  ,							)

																									((__lowerCAmelCase) ,      (__lowerCAmelCase))        =       (
																									    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																									)

																									__lowerCAmelCase        =       np.asarray(snake_case_  ,							snake_case_							)[: len(snake_case_							)]
																									plt.scatter(
																									    snake_case_  ,							snake_case_  ,							label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""							)
																									plt.plot(snake_case_  ,							snake_case_  ,							"""--"""							)

																			title_str += f""" {label_model_name} vs."""

													__lowerCAmelCase        =       title_str[:-4]
													__lowerCAmelCase        =       """Time in s""" if self.args.is_time else """Memory in MB"""

													# plot
													plt.title(snake_case_							)
													plt.xlabel(snake_case_							)
													plt.ylabel(snake_case_							)
													plt.legend()

													if self.args.figure_png_file is not None:
																			plt.savefig(self.args.figure_png_file							)
													else:
																			plt.show()

def 				lowercase      ():
						__lowerCAmelCase        =       HfArgumentParser(_lowerCAmelCase		)
						__lowerCAmelCase        =       parser.parse_args_into_dataclasses()[0]
						__lowerCAmelCase        =       Plot(args=_lowerCAmelCase		)
						plot.plot()


if __name__ == "__main__":
			main()
