






'''simple docstring'''







import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


_A     :      Any   					=logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm
			import torch_xla.debug.metrics as met

class _lowercase     (    _lowercase		):




							def __init__(							self:    Optional[Any]      ,							*UpperCamelCase__:    int      ,							UpperCamelCase__:    Union[str, Any]=None      ,							UpperCamelCase__:    Any=None      ,							UpperCamelCase__:    Union[str, Any]=None      ,							**UpperCamelCase__:    Dict					):
														super().__init__(*UpperCamelCase__      ,							**UpperCamelCase__					)
														lowerCamelCase__ :     Tuple	  =	eval_examples
														lowerCamelCase__ :     str	  =	post_process_function
														lowerCamelCase__ :     List[Any]	  =	quant_trainer_args
														lowerCamelCase__ :     Union[str, Any]	  =	128  # default number of calibration samples




							def 		lowerCamelCase_    (							self:    Dict      ,							UpperCamelCase__:    List[Any]=None					):
														if calib_dataset is None and self.calib_dataset is None:
																					raise ValueError("""Trainer: calibration requires an calib_dataset."""					)
														lowerCamelCase__ :     Optional[Any]	  =	calib_dataset if calib_dataset is not None else self.calib_dataset

														lowerCamelCase__ :     Dict	  =	self._remove_unused_columns(UpperCamelCase__      ,							description="""Calibration"""					)

														return DataLoader(
														    UpperCamelCase__      ,							batch_size=self.args.eval_batch_size      ,							collate_fn=self.data_collator      ,							drop_last=self.args.dataloader_drop_last      ,							num_workers=self.args.dataloader_num_workers      ,							pin_memory=self.args.dataloader_pin_memory      ,							shuffle=UpperCamelCase__      ,							)




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    List[str]=None					):
														lowerCamelCase__ :     List[Any]	  =	self.train_dataset if calib_dataset is None else calib_dataset
														lowerCamelCase__ :     List[Any]	  =	self.get_calib_dataloader(UpperCamelCase__					)

														lowerCamelCase__ :     str	  =	self.model
														quant_trainer.configure_model(UpperCamelCase__      ,							self.quant_trainer_args      ,							calib=UpperCamelCase__					)
														model.eval()
														quant_trainer.enable_calibration(UpperCamelCase__					)

														logger.info("""***** Running calibration *****"""					)
														logger.info(F'''  Num examples = {self.calib_num}'''					)
														logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''					)

														for step, inputs in enumerate(UpperCamelCase__					):
																					# Prediction step
																					lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Union[str, Any]	  =	self.prediction_step(UpperCamelCase__      ,							UpperCamelCase__      ,							prediction_loss_only=UpperCamelCase__					)
																					if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
																												break

														quant_trainer.finish_calibration(UpperCamelCase__      ,							self.quant_trainer_args					)
														lowerCamelCase__ :     int	  =	model




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Tuple=None      ,							UpperCamelCase__:    Union[str, Any]=None      ,							UpperCamelCase__:    Optional[int]=None      ,							UpperCamelCase__:    str = "eval"					):
														lowerCamelCase__ :     Union[str, Any]	  =	self.eval_dataset if eval_dataset is None else eval_dataset
														lowerCamelCase__ :     List[str]	  =	self.get_eval_dataloader(UpperCamelCase__					)
														lowerCamelCase__ :     Dict	  =	self.eval_examples if eval_examples is None else eval_examples

														# Temporarily disable metric computation, we will do it in the loop here.
														lowerCamelCase__ :     List[str]	  =	self.compute_metrics
														lowerCamelCase__ :     List[str]	  =	None
														lowerCamelCase__ :     Any	  =	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
														try:
																					lowerCamelCase__ :     Optional[int]	  =	eval_loop(
																					    UpperCamelCase__      ,							description="""Evaluation"""      ,							prediction_loss_only=True if compute_metrics is None else None      ,							ignore_keys=UpperCamelCase__      ,							)
														finally:
																					lowerCamelCase__ :     Optional[Any]	  =	compute_metrics

														if self.post_process_function is not None and self.compute_metrics is not None:
																					lowerCamelCase__ :     int	  =	self.post_process_function(UpperCamelCase__      ,							UpperCamelCase__      ,							output.predictions					)
																					lowerCamelCase__ :     List[str]	  =	self.compute_metrics(UpperCamelCase__					)

																					# Prefix all keys with metric_key_prefix + '_'
																					for key in list(metrics.keys()					):
																												if not key.startswith(F'''{metric_key_prefix}_'''					):
																																			lowerCamelCase__ :     int	  =	metrics.pop(UpperCamelCase__					)

																					self.log(UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     str	  =	{}

														if self.args.tpu_metrics_debug or self.args.debug:
																					# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																					xm.master_print(met.metrics_report()					)

														lowerCamelCase__ :     str	  =	self.callback_handler.on_evaluate(self.args      ,							self.state      ,							self.control      ,							UpperCamelCase__					)
														return metrics




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    Any=None      ,							UpperCamelCase__:    str = "test"					):
														lowerCamelCase__ :     List[Any]	  =	self.get_test_dataloader(UpperCamelCase__					)

														# Temporarily disable metric computation, we will do it in the loop here.
														lowerCamelCase__ :     int	  =	self.compute_metrics
														lowerCamelCase__ :     Optional[Any]	  =	None
														lowerCamelCase__ :     Union[str, Any]	  =	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
														try:
																					lowerCamelCase__ :     int	  =	eval_loop(
																					    UpperCamelCase__      ,							description="""Prediction"""      ,							prediction_loss_only=True if compute_metrics is None else None      ,							ignore_keys=UpperCamelCase__      ,							)
														finally:
																					lowerCamelCase__ :     Tuple	  =	compute_metrics

														if self.post_process_function is None or self.compute_metrics is None:
																					return output

														lowerCamelCase__ :     List[str]	  =	self.post_process_function(UpperCamelCase__      ,							UpperCamelCase__      ,							output.predictions      ,							"""predict"""					)
														lowerCamelCase__ :     Dict	  =	self.compute_metrics(UpperCamelCase__					)

														# Prefix all keys with metric_key_prefix + '_'
														for key in list(metrics.keys()					):
																					if not key.startswith(F'''{metric_key_prefix}_'''					):
																												lowerCamelCase__ :     List[Any]	  =	metrics.pop(UpperCamelCase__					)

														return PredictionOutput(predictions=predictions.predictions      ,							label_ids=predictions.label_ids      ,							metrics=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Tuple="./"					):
														lowerCamelCase__ :     Any	  =	self.eval_dataset
														lowerCamelCase__ :     Union[str, Any]	  =	self.get_eval_dataloader(UpperCamelCase__					)

														lowerCamelCase__ :     Dict	  =	next(iter(UpperCamelCase__					)					)

														# saving device - to make it consistent
														lowerCamelCase__ :     List[Any]	  =	torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""					)

														# convert to tuple
														lowerCamelCase__ :     Tuple	  =	tuple(v.to(UpperCamelCase__					) for k, v in batch.items()					)

														logger.info("""Converting model to be onnx compatible"""					)
														from pytorch_quantization.nn import TensorQuantizer

														lowerCamelCase__ :     Dict	  =	True

														lowerCamelCase__ :     Optional[int]	  =	self.model.to(UpperCamelCase__					)

														model.eval()
														model.float()

														lowerCamelCase__ :     List[Any]	  =	model.module if hasattr(UpperCamelCase__      ,							"""module"""					) else model
														quant_trainer.configure_model(UpperCamelCase__      ,							self.quant_trainer_args					)

														lowerCamelCase__ :     int	  =	os.path.join(UpperCamelCase__      ,							"""model.onnx"""					)
														logger.info(F'''exporting model to {output_model_file}'''					)

														lowerCamelCase__ :     int	  =	{0: """batch_size""", 1: """seq_len"""}

														torch.onnx.export(
														    UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							export_params=UpperCamelCase__      ,							opset_version=13      ,							do_constant_folding=UpperCamelCase__      ,							input_names=["""input_ids""", """attention_mask""", """token_type_ids"""]      ,							output_names=["""output_start_logits""", """output_end_logits"""]      ,							dynamic_axes={
														        """input_ids""": axes,
														        """attention_mask""": axes,
														        """token_type_ids""": axes,
														        """output_start_logits""": axes,
														        """output_end_logits""": axes,
														    }      ,							verbose=UpperCamelCase__      ,							)
														logger.info("""onnx export finished"""					)









'''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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase=None    ,      UpperCamelCase=None				)				->	Any:
							return field(default_factory=lambda: default    ,      metadata=UpperCamelCase				)

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





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							try:
														int(UpperCamelCase				)
														return True
							except ValueError:
														return False





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							try:
														float(UpperCamelCase				)
														return True
							except ValueError:
														return False

class _lowercase     :




							def __init__(							self:    Tuple      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     int	  =	args
														lowerCamelCase__ :     Optional[int]	  =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

														with open(self.args.csv_file      ,							newline=""""""					) as csv_file:
																					lowerCamelCase__ :     str	  =	csv.DictReader(UpperCamelCase__					)
																					for row in reader:
																												lowerCamelCase__ :     Optional[int]	  =	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__ :     Tuple	  =	int(row["""result"""]					)
																												elif can_convert_to_float(row["""result"""]					):
																																			# value is not None
																																			lowerCamelCase__ :     Any	  =	float(row["""result"""]					)




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__  ,		lowerCamelCase__ :     Tuple	  =	plt.subplots()
														lowerCamelCase__ :     Any	  =	"""Time usage""" if self.args.is_time else """Memory usage"""
														lowerCamelCase__ :     List[str]	  =	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__ :     Any	  =	sorted(set(self.result_dict[model_name]["""bsz"""]					)					)
																					lowerCamelCase__ :     int	  =	sorted(set(self.result_dict[model_name]["""seq_len"""]					)					)
																					lowerCamelCase__ :     Any	  =	self.result_dict[model_name]["""result"""]

																					((lowerCamelCase__)  ,		(lowerCamelCase__)) :     Dict	  =	(
																					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																					)

																					lowerCamelCase__ :     Any	  =	(
																					    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__ :     int	  =	np.asarray(
																																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=UpperCamelCase__      ,							)
																												else:
																																			lowerCamelCase__ :     List[Any]	  =	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__)) :     List[str]	  =	(
																												    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																												)

																												lowerCamelCase__ :     int	  =	np.asarray(UpperCamelCase__      ,							UpperCamelCase__					)[: len(UpperCamelCase__					)]
																												plt.scatter(
																												    UpperCamelCase__      ,							UpperCamelCase__      ,							label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''					)
																												plt.plot(UpperCamelCase__      ,							UpperCamelCase__      ,							"""--"""					)

																					title_str += F''' {label_model_name} vs.'''

														lowerCamelCase__ :     Any	  =	title_str[:-4]
														lowerCamelCase__ :     Optional[int]	  =	"""Time in s""" if self.args.is_time else """Memory in MB"""

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

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





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     str	  =	HfArgumentParser(UpperCamelCase				)
							lowerCamelCase__ :     str	  =	parser.parse_args_into_dataclasses()[0]
							lowerCamelCase__ :     Any	  =	Plot(args=UpperCamelCase				)
							plot.plot()


if __name__ == "__main__":
			main()

