


'''simple docstring'''


import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
				from comet_ml import OfflineExperiment

if is_tensorboard_available():
				import struct

				import tensorboard.compat.proto.event_pba as event_pba

UpperCAmelCase_	:     Union[str, Any]							     =	logging.getLogger(__name__)





@require_tensorboard
class 					lowercase__						(		unittest.TestCase      ):





							'''simple docstring'''


							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Dict  	=   """test_project_with_config"""
											with tempfile.TemporaryDirectory() as dirpath:
															_SCREAMING_SNAKE_CASE				: List[str]  	=   Accelerator(log_with="""tensorboard"""					,      project_dir=__snake_case						)
															_SCREAMING_SNAKE_CASE				: Tuple  	=   {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
															accelerator.init_trackers(__snake_case					,      __snake_case						)
															accelerator.end_training()
															for child in Path(f"""{dirpath}/{project_name}"""						).glob("""*/**"""						):
																			_SCREAMING_SNAKE_CASE				: Optional[int]  	=   list(filter(lambda __snake_case						: x.is_file()					,      child.iterdir()						)						)[0]
															self.assertNotEqual(str(__snake_case						)					,      """"""						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   """test_project_with_log"""
											with tempfile.TemporaryDirectory() as dirpath:
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   Accelerator(log_with="""tensorboard"""					,      project_dir=__snake_case						)
															accelerator.init_trackers(__snake_case						)
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
															accelerator.log(__snake_case					,      step=0						)
															accelerator.end_training()
															# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
															# Names are randomly generated each time
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   list(filter(lambda __snake_case						: x.is_file()					,      Path(f"""{dirpath}/{project_name}"""						).iterdir()						)						)[0]
															self.assertNotEqual(str(__snake_case						)					,      """"""						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: List[str]  	=   """test_project_with_log"""
											with tempfile.TemporaryDirectory() as dirpath:
															_SCREAMING_SNAKE_CASE				: Any  	=   Accelerator(log_with="""tensorboard"""					,      project_dir=__snake_case						)
															accelerator.init_trackers(__snake_case						)
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   {"""tensor""": torch.tensor(1						)}
															accelerator.log(__snake_case					,      step=0						)
															accelerator.end_training()
															# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
															# Names are randomly generated each time
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   list(filter(lambda __snake_case						: x.is_file()					,      Path(f"""{dirpath}/{project_name}"""						).iterdir()						)						)[0]
															# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
															with open(__snake_case					,      """rb"""						) as f:
																			_SCREAMING_SNAKE_CASE				: List[str]  	=   f.read()
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   False
															while data:
																			_SCREAMING_SNAKE_CASE				: int  	=   struct.unpack("""Q"""					,      data[:8]						)

																			_SCREAMING_SNAKE_CASE				: Tuple  	=   data[12 : 12 + int(header[0]						)]  # 8+4
																			_SCREAMING_SNAKE_CASE				: Dict  	=   data[12 + int(header[0]						) + 4 :]
																			_SCREAMING_SNAKE_CASE				: List[str]  	=   event_pba.Event()

																			event.ParseFromString(__snake_case						)
																			if event.HasField("""summary"""						):
																							for value in event.summary.value:
																											if value.simple_value == 1.0 and value.tag == "tensor":
																															_SCREAMING_SNAKE_CASE				: List[Any]  	=   True
															self.assertTrue(__snake_case					,      """Converted tensor was not found in the log file!"""						)






							def UpperCAmelCase_			(					self						):
											with self.assertRaisesRegex(__snake_case					,      """Logging with `tensorboard` requires a `logging_dir`"""						):
															_SCREAMING_SNAKE_CASE				: str  	=   Accelerator(log_with="""tensorboard"""						)
											with tempfile.TemporaryDirectory() as dirpath:
															_SCREAMING_SNAKE_CASE				: Any  	=   Accelerator(log_with="""tensorboard"""					,      project_dir=__snake_case						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: int  	=   ProjectConfiguration(total_limit=30						)
											with tempfile.TemporaryDirectory() as dirpath:
															_SCREAMING_SNAKE_CASE				: Tuple  	=   Accelerator(log_with="""tensorboard"""					,      project_dir=__snake_case					,      project_config=__snake_case						)





@require_wandb
@mock.patch.dict(os.environ  ,  {"""WANDB_MODE""": """offline"""}      )
class 					lowercase__						(		_snake_case  ,  _snake_case      ):





							'''simple docstring'''


							def UpperCAmelCase_			(					self						):
											super().setUp()
											# wandb let's us override where logs are stored to via the WANDB_DIR env var
											self.add_mocks(mock.patch.dict(os.environ					,      {"""WANDB_DIR""": self.tmpdir}						)						)






							@staticmethod
							def UpperCAmelCase_			(					__snake_case					,      __snake_case					,      __snake_case = True						):
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   rf"""{section} ([\S\s]*?)\n\n"""
											if record:
															_SCREAMING_SNAKE_CASE				: Dict  	=   rf"""Record: {pattern}"""
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   re.findall(__snake_case					,      __snake_case						)[0]
											# A config
											if section == "config" or section == "history":
															_SCREAMING_SNAKE_CASE				: int  	=   re.findall(r"""\"([a-zA-Z0-9_.,]+)"""					,      __snake_case						)
															return {key: val for key, val in zip(cleaned_record[0::2]					,      cleaned_record[1::2]						)}
											# Everything else
											else:
															return dict(re.findall(r"""(\w+): \"([^\s]+)\""""					,      __snake_case						)						)






							@skip
							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Any  	=   """test_project_with_config"""
											_SCREAMING_SNAKE_CASE				: int  	=   Accelerator(log_with="""wandb"""						)
											_SCREAMING_SNAKE_CASE				: Any  	=   {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
											_SCREAMING_SNAKE_CASE				: Any  	=   {"""wandb""": {"""tags""": ["""my_tag"""]}}
											accelerator.init_trackers(__snake_case					,      __snake_case					,      __snake_case						)
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
											accelerator.log(__snake_case					,      step=0						)
											accelerator.end_training()
											# The latest offline log is stored at wandb/latest-run/*.wandb
											for child in Path(f"""{self.tmpdir}/wandb/latest-run"""						).glob("""*"""						):
															if child.is_file() and child.suffix == ".wandb":
																			_SCREAMING_SNAKE_CASE				: Dict  	=   subprocess.check_output(
																			    ["""wandb""", """sync""", """--view""", """--verbose""", str(__snake_case						)]					,      env=os.environ.copy()						).decode("""utf8"""					,      """ignore"""						)
																			break

        # Check HPS through careful parsing and cleaning
											_SCREAMING_SNAKE_CASE				: Tuple  	=   self.parse_log(__snake_case					,      """config"""						)
											self.assertEqual(logged_items["""num_iterations"""]					,      """12"""						)
											self.assertEqual(logged_items["""learning_rate"""]					,      """0.01"""						)
											self.assertEqual(logged_items["""some_boolean"""]					,      """false"""						)
											self.assertEqual(logged_items["""some_string"""]					,      """some_value"""						)
											self.assertEqual(logged_items["""some_string"""]					,      """some_value"""						)

											# Run tags
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   self.parse_log(__snake_case					,      """run"""					,      __snake_case						)
											self.assertEqual(logged_items["""tags"""]					,      """my_tag"""						)

											# Actual logging
											_SCREAMING_SNAKE_CASE				: Tuple  	=   self.parse_log(__snake_case					,      """history"""						)
											self.assertEqual(logged_items["""total_loss"""]					,      """0.1"""						)
											self.assertEqual(logged_items["""iteration"""]					,      """1"""						)
											self.assertEqual(logged_items["""my_text"""]					,      """some_value"""						)
											self.assertEqual(logged_items["""_step"""]					,      """0"""						)







def     snake_case_     (						self							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: str  	=   run_name
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   OfflineExperiment(project_name=SCREAMING_SNAKE_CASE__							,   offline_directory=SCREAMING_SNAKE_CASE__  )
				logger.info(f"""Initialized offline CometML project {self.run_name}"""  )
				logger.info("""Make sure to log any initial configurations with `self.store_init_configuration` before training!"""  )





@require_comet_ml
@mock.patch.object(_snake_case  ,  """__init__"""  ,  _snake_case      )
class 					lowercase__						(		unittest.TestCase      ):





							'''simple docstring'''


							@staticmethod
							def UpperCAmelCase_			(					__snake_case					,      __snake_case					,      __snake_case = False						):
											for log in log_list:
															_SCREAMING_SNAKE_CASE				: List[str]  	=   json.loads(__snake_case						)["""payload"""]
															if is_param and "param" in j.keys():
																			if j["param"]["paramName"] == key:
																							return j["param"]["paramValue"]
															if "log_other" in j.keys():
																			if j["log_other"]["key"] == key:
																							return j["log_other"]["val"]
															if "metric" in j.keys():
																			if j["metric"]["metricName"] == key:
																							return j["metric"]["metricValue"]






							def UpperCAmelCase_			(					self						):
											with tempfile.TemporaryDirectory() as d:
															_SCREAMING_SNAKE_CASE				: Any  	=   CometMLTracker("""test_project_with_config"""					,      __snake_case						)
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   Accelerator(log_with=__snake_case						)
															_SCREAMING_SNAKE_CASE				: int  	=   {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
															accelerator.init_trackers(__snake_case					,      __snake_case						)
															accelerator.end_training()
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   os.listdir(__snake_case						)[0]  # Comet is nice, it's just a zip file here
															# We parse the raw logs
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   os.path.join(__snake_case					,      __snake_case						)
															_SCREAMING_SNAKE_CASE				: Any  	=   zipfile.ZipFile(__snake_case					,      """r"""						)
															_SCREAMING_SNAKE_CASE				: List[str]  	=   archive.open("""messages.json"""						).read().decode("""utf-8"""						)
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   log.split("""\n"""						)[:-1]
											self.assertEqual(self.get_value_from_key(__snake_case					,      """num_iterations"""					,      __snake_case						)					,      12						)
											self.assertEqual(self.get_value_from_key(__snake_case					,      """learning_rate"""					,      __snake_case						)					,      0.01						)
											self.assertEqual(self.get_value_from_key(__snake_case					,      """some_boolean"""					,      __snake_case						)					,      __snake_case						)
											self.assertEqual(self.get_value_from_key(__snake_case					,      """some_string"""					,      __snake_case						)					,      """some_value"""						)






							def UpperCAmelCase_			(					self						):
											with tempfile.TemporaryDirectory() as d:
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   CometMLTracker("""test_project_with_config"""					,      __snake_case						)
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   Accelerator(log_with=__snake_case						)
															accelerator.init_trackers(__snake_case						)
															_SCREAMING_SNAKE_CASE				: Dict  	=   {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
															accelerator.log(__snake_case					,      step=0						)
															accelerator.end_training()
															_SCREAMING_SNAKE_CASE				: Dict  	=   os.listdir(__snake_case						)[0]  # Comet is nice, it's just a zip file here
															# We parse the raw logs
															_SCREAMING_SNAKE_CASE				: Dict  	=   os.path.join(__snake_case					,      __snake_case						)
															_SCREAMING_SNAKE_CASE				: Dict  	=   zipfile.ZipFile(__snake_case					,      """r"""						)
															_SCREAMING_SNAKE_CASE				: Tuple  	=   archive.open("""messages.json"""						).read().decode("""utf-8"""						)
											_SCREAMING_SNAKE_CASE				: Any  	=   log.split("""\n"""						)[:-1]
											self.assertEqual(self.get_value_from_key(__snake_case					,      """curr_step"""					,      __snake_case						)					,      0						)
											self.assertEqual(self.get_value_from_key(__snake_case					,      """total_loss"""						)					,      0.1						)
											self.assertEqual(self.get_value_from_key(__snake_case					,      """iteration"""						)					,      1						)
											self.assertEqual(self.get_value_from_key(__snake_case					,      """my_text"""						)					,      """some_value"""						)





class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


							A_							:   Tuple   			=						[
							    """total_loss""",
							    """iteration""",
							    """my_text""",
							    """learning_rate""",
							    """num_iterations""",
							    """some_boolean""",
							    """some_string""",
							]

							A_							:   List[Any]   			=						"""my_custom_tracker"""
							A_							:   int   			=						False






							def __init__(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   open(f"""{dir}/log.csv"""					,      """w+"""						)
											_SCREAMING_SNAKE_CASE				: Any  	=   csv.DictWriter(self.f					,      fieldnames=self._col_names						)
											self.writer.writeheader()






							@property
							def UpperCAmelCase_			(					self						):
											return self.writer






							def UpperCAmelCase_			(					self					,      __snake_case						):
											logger.info("""Call init"""						)
											self.writer.writerow(__snake_case						)






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case						):
											logger.info("""Call log"""						)
											self.writer.writerow(__snake_case						)






							def UpperCAmelCase_			(					self						):
											self.f.close()





class 					lowercase__						(		unittest.TestCase      ):





							'''simple docstring'''


							def UpperCAmelCase_			(					self						):
											with tempfile.TemporaryDirectory() as d:
															_SCREAMING_SNAKE_CASE				: List[str]  	=   MyCustomTracker(__snake_case						)
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   Accelerator(log_with=__snake_case						)
															_SCREAMING_SNAKE_CASE				: List[str]  	=   {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
															accelerator.init_trackers("""Some name"""					,      __snake_case						)
															accelerator.end_training()
															with open(f"""{d}/log.csv"""					,      """r"""						) as f:
																			_SCREAMING_SNAKE_CASE				: str  	=   csv.DictReader(__snake_case						)
																			_SCREAMING_SNAKE_CASE				: int  	=   next(__snake_case						)
																			_SCREAMING_SNAKE_CASE				: Optional[int]  	=   {
																			    """total_loss""": """""",
																			    """iteration""": """""",
																			    """my_text""": """""",
																			    """learning_rate""": """0.01""",
																			    """num_iterations""": """12""",
																			    """some_boolean""": """False""",
																			    """some_string""": """some_value""",
																			}
																			self.assertDictEqual(__snake_case					,      __snake_case						)






							def UpperCAmelCase_			(					self						):
											with tempfile.TemporaryDirectory() as d:
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   MyCustomTracker(__snake_case						)
															_SCREAMING_SNAKE_CASE				: Dict  	=   Accelerator(log_with=__snake_case						)
															accelerator.init_trackers("""Some name"""						)
															_SCREAMING_SNAKE_CASE				: Tuple  	=   {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
															accelerator.log(__snake_case					,      step=0						)
															accelerator.end_training()
															with open(f"""{d}/log.csv"""					,      """r"""						) as f:
																			_SCREAMING_SNAKE_CASE				: Optional[int]  	=   csv.DictReader(__snake_case						)
																			_SCREAMING_SNAKE_CASE				: int  	=   next(__snake_case						)
																			_SCREAMING_SNAKE_CASE				: Tuple  	=   {
																			    """total_loss""": """0.1""",
																			    """iteration""": """1""",
																			    """my_text""": """some_value""",
																			    """learning_rate""": """""",
																			    """num_iterations""": """""",
																			    """some_boolean""": """""",
																			    """some_string""": """""",
																			}
																			self.assertDictEqual(__snake_case					,      __snake_case						)








'''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     snake_case_     (						SCREAMING_SNAKE_CASE__=None							,   SCREAMING_SNAKE_CASE__=None  ):







				"""simple docstring"""





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





@dataclass
class 					lowercase__						:





							'''simple docstring'''


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







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args
											_SCREAMING_SNAKE_CASE				: Dict  	=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

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






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: List[Any]  	=   plt.subplots()
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   """Time usage""" if self.args.is_time else """Memory usage"""
											_SCREAMING_SNAKE_CASE				: int  	=   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()						):
															_SCREAMING_SNAKE_CASE				: str  	=   sorted(set(self.result_dict[model_name]["""bsz"""]						)						)
															_SCREAMING_SNAKE_CASE				: List[str]  	=   sorted(set(self.result_dict[model_name]["""seq_len"""]						)						)
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.result_dict[model_name]["""result"""]

															((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: Tuple  	=   (
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															_SCREAMING_SNAKE_CASE				: 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:
																							_SCREAMING_SNAKE_CASE				: Any  	=   np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,      dtype=__snake_case					,      )
																			else:
																							_SCREAMING_SNAKE_CASE				: Dict  	=   np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,      dtype=np.floataa					,      )

																			((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: List[Any]  	=   (
																			    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																			)

																			_SCREAMING_SNAKE_CASE				: str  	=   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."""

											_SCREAMING_SNAKE_CASE				: str  	=   title_str[:-4]
											_SCREAMING_SNAKE_CASE				: int  	=   """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     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   HfArgumentParser(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   parser.parse_args_into_dataclasses()[0]
				_SCREAMING_SNAKE_CASE				: int  	=   Plot(args=SCREAMING_SNAKE_CASE__  )
				plot.plot()


if __name__ == "__main__":
				main()




