



'''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


_UpperCamelCase  						=			logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_UpperCamelCase  						=			{
    """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
_UpperCamelCase  						=			{
    """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
}
_UpperCamelCase  						=			sorted(arg_to_scheduler.keys())
_UpperCamelCase  						=			"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class      lowerCamelCase__     (     pl.LightningModule				):






	'''simple docstring'''

	def __init__(  self		:  Any   ,		__A		:  Any   ,		__A		:  str=None   ,		__A		:  List[Any]="base"   ,		__A		:  Tuple=None   ,		__A		:  List[str]=None   ,		__A		:  Union[str, Any]=None   ,		**__A		:  List[Any]   ,		)     ->			Any:




								'''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(__a					)
								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=__a   ,		**__a   ,		)
								else:
															lowerCAmelCase__  =   config

								lowerCAmelCase__  =   ("""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:
															lowerCAmelCase__  =   AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,		cache_dir=__a   ,		)
								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=__a   ,		)
								else:
															lowerCAmelCase__  =   model




	def      lowercase__							(  self		:  List[Any]   ,		*__A		:  Optional[int]   ,		**__A		:  Optional[Any]					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_type.from_pretrained(*__a   ,		**__a					)




	def      lowercase__							(  self		:  Tuple					)     ->			Optional[int]:




								'''simple docstring'''





								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      lowercase__							(  self		:  Optional[int]					)     ->			Optional[Any]:




								'''simple docstring'''





								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(
															    __a   ,		lr=self.hparams.learning_rate   ,		scale_parameter=__a   ,		relative_step=__a					)

								else:
															lowerCAmelCase__  =   AdamW(
															    __a   ,		lr=self.hparams.learning_rate   ,		eps=self.hparams.adam_epsilon					)
								lowerCAmelCase__  =   optimizer

								lowerCAmelCase__  =   self.get_lr_scheduler()

								return [optimizer], [scheduler]




	def      lowercase__							(  self		:  Dict   ,		__A		:  Optional[Any]   ,		__A		:  Union[str, Any]					)     ->			int:




								'''simple docstring'''





								return self.validation_step(__a   ,		__a					)




	def      lowercase__							(  self		:  Optional[Any]   ,		__A		:  List[Any]					)     ->			List[str]:




								'''simple docstring'''





								return self.validation_end(__a					)




	def      lowercase__							(  self		:  int					)     ->			int:




								'''simple docstring'''





								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      lowercase__							(  self		:  int   ,		__A		:  int					)     ->			Union[str, Any]:




								'''simple docstring'''





								if mode == "test":
															lowerCAmelCase__  =   len(self.test_dataloader().dataset					)
								else:
															lowerCAmelCase__  =   self.get_dataloader("""train"""   ,		self.hparams.train_batch_size   ,		shuffle=__a					)
															lowerCAmelCase__  =   len(self.train_dataloader().dataset					)




	def      lowercase__							(  self		:  Optional[int]   ,		__A		:  Union[str, Any]   ,		__A		:  Optional[Any]   ,		__A		:  str = False					)     ->			List[Any]:




								'''simple docstring'''





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




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




								'''simple docstring'''





								return self.train_loader




	def      lowercase__							(  self		:  Optional[int]					)     ->			Optional[int]:




								'''simple docstring'''





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




	def      lowercase__							(  self		:  List[str]					)     ->			str:




								'''simple docstring'''





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




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




								'''simple docstring'''





								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      lowercase__							(  self		:  int   ,		__A		:  int					)     ->			None:




								'''simple docstring'''





								lowerCAmelCase__  =   self.output_dir.joinpath("""best_tfmr"""					)
								lowerCAmelCase__  =   self.step_count
								self.model.save_pretrained(__a					)
								self.tokenizer.save_pretrained(__a					)




	@staticmethod
	def      lowercase__							(  __A		:  Any   ,		__A		:  Any					)     ->			Union[str, Any]:




								'''simple docstring'''





								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=32   ,		type=__a					)
								parser.add_argument("""--eval_batch_size"""   ,		default=32   ,		type=__a					)
								parser.add_argument("""--adafactor"""   ,		action="""store_true"""					)





class      lowerCamelCase__     (     pl.Callback				):






	'''simple docstring'''

	def      lowercase__							(  self		:  Optional[int]   ,		__A		:  int   ,		__A		:  Dict					)     ->			List[Any]:




								'''simple docstring'''





								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(__a					)




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




								'''simple docstring'''





								rank_zero_info("""***** Validation results *****"""					)
								lowerCAmelCase__  =   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      lowercase__							(  self		:  List[Any]   ,		__A		:  str   ,		__A		:  Tuple					)     ->			Optional[Any]:




								'''simple docstring'''





								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(__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 			_lowerCAmelCase(      UpperCAmelCase_      :	int					, UpperCAmelCase_      :	List[Any]						) ->							Dict:
							parser.add_argument(
							    """--output_dir"""					, default=_SCREAMING_SNAKE_CASE					, type=_SCREAMING_SNAKE_CASE					, required=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE						)
							parser.add_argument("""--max_grad_norm"""					, dest="""gradient_clip_val"""					, default=1.0					, type=_SCREAMING_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=_SCREAMING_SNAKE_CASE					, default=1					, help="""Number of updates steps to accumulate before performing a backward/update pass."""					, )
							parser.add_argument("""--seed"""					, type=_SCREAMING_SNAKE_CASE					, default=42					, help="""random seed for initialization"""						)
							parser.add_argument(
							    """--data_dir"""					, default=_SCREAMING_SNAKE_CASE					, type=_SCREAMING_SNAKE_CASE					, required=_SCREAMING_SNAKE_CASE					, help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""					, )






def 			_lowerCAmelCase(      UpperCAmelCase_      :	str					, UpperCAmelCase_      :	Optional[Any]					, UpperCAmelCase_      :	int=None					, UpperCAmelCase_      :	Tuple=True					, UpperCAmelCase_      :	str=[]					, UpperCAmelCase_      :	Union[str, Any]=None					, UpperCAmelCase_      :	Tuple=None					, **UpperCAmelCase_      :	int					, ) ->							Optional[Any]:
							pl.seed_everything(args.seed						)

							# init model
							lowerCAmelCase__  =   Path(model.hparams.output_dir						)
							odir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE						)

							# 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(_SCREAMING_SNAKE_CASE						)
							if logging_callback is None:
														lowerCAmelCase__  =   LoggingCallback()

							lowerCAmelCase__  =   {}

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

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

							lowerCAmelCase__  =   args.accumulate_grad_batches
							lowerCAmelCase__  =   extra_train_kwargs.get("""accelerator"""					, _SCREAMING_SNAKE_CASE						)
							lowerCAmelCase__  =   extra_train_kwargs.get("""profiler"""					, _SCREAMING_SNAKE_CASE						)

							lowerCAmelCase__  =   pl.Trainer.from_argparse_args(
							    _SCREAMING_SNAKE_CASE					, weights_summary=_SCREAMING_SNAKE_CASE					, callbacks=[logging_callback] + extra_callbacks					, logger=_SCREAMING_SNAKE_CASE					, checkpoint_callback=_SCREAMING_SNAKE_CASE					, **_SCREAMING_SNAKE_CASE					, )

							if args.do_train:
														trainer.fit(_SCREAMING_SNAKE_CASE						)

							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    a__       (       _SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None     ):




		"""simple docstring"""


		return field(default_factory=lambda: default	,						metadata=_SCREAMING_SNAKE_CASE     )





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



def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		try:
				int(_SCREAMING_SNAKE_CASE     )
				return True
		except ValueError:
				return False


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		try:
				float(_SCREAMING_SNAKE_CASE     )
				return True
		except ValueError:
				return False





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Tuple:
								UpperCamelCase =						args
								UpperCamelCase =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

								with open(self.args.csv_file ,				newline=""		) as csv_file:
										UpperCamelCase =						csv.DictReader(__a		)
										for row in reader:
												UpperCamelCase =						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
														UpperCamelCase =						int(row["result"]		)
												elif can_convert_to_float(row["result"]		):
														# value is not None
														UpperCamelCase =						float(row["result"]		)




						def      snake_case_ (self		)			-> Optional[int]:
								UpperCamelCase ,					UpperCamelCase =						plt.subplots()
								UpperCamelCase =						"Time usage" if self.args.is_time else "Memory usage"
								UpperCamelCase =						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()		):
										UpperCamelCase =						sorted(set(self.result_dict[model_name]["bsz"]		)		)
										UpperCamelCase =						sorted(set(self.result_dict[model_name]["seq_len"]		)		)
										UpperCamelCase =						self.result_dict[model_name]["result"]

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

										UpperCamelCase =						(
										    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:
														UpperCamelCase =						np.asarray(
														    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,				dtype=__a ,				)
												else:
														UpperCamelCase =						np.asarray(
														    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,				dtype=np.floataa ,				)

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

												UpperCamelCase =						np.asarray(__a ,				__a		)[: len(__a		)]
												plt.scatter(
												    __a ,				__a ,				label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"		)
												plt.plot(__a ,				__a ,				"--"		)

										title_str += F" {label_model_name} vs."

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

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

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



def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						HfArgumentParser(_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						parser.parse_args_into_dataclasses()[0]
		UpperCamelCase =						Plot(args=_SCREAMING_SNAKE_CASE     )
		plot.plot()


if __name__ == "__main__":
						main()






