



'''simple docstring'''






# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  a						: List[Any]					       =					multiprocessing.Manager()
  a						: List[str]					       =					manager.list()

  a						: Dict					       =					multiprocessing.Process(target=_A			,      args=(check_program, result, timeout)    )
  p.start()
  p.join(timeout=timeout + 1    )
  if p.is_alive():
    p.kill()

  if not result:
    result.append('timed out'    )

  return {
      "task_id": task_id,
      "passed": result[0] == "passed",
      "result": result[0],
      "completion_id": completion_id,
  }
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  with create_tempdir():
    # These system calls are needed when cleaning up tempdir.
    import os
    import shutil

    a						: List[str]					       =					shutil.rmtree
    a						: Dict					       =					os.rmdir
    a						: Tuple					       =					os.chdir

    # Disable functionalities that can make destructive changes to the test.
    reliability_guard()

    # Run program.
    try:
      a						: Tuple					       =					{}
      with swallow_io():
        with time_limit(_A    ):
          exec(_A			,      _A    )
      result.append('passed'    )
    except TimeoutException:
      result.append('timed out'    )
    except BaseException as e:
      result.append(f"""failed: {e}"""    )

    # Needed for cleaning up.
    a						: List[str]					       =					rmtree
    a						: Dict					       =					rmdir
    a						: int					       =					chdir
@contextlib.contextmanager
def 				lowerCamelCase__	(						_A    ):
  def signal_handler(_A			,      _A    ):
    raise TimeoutException('Timed out!'    )

  signal.setitimer(signal.ITIMER_REAL			,      _A    )
  signal.signal(signal.SIGALRM			,      _A    )
  try:
    yield
  finally:
    signal.setitimer(signal.ITIMER_REAL			,      0    )
@contextlib.contextmanager
def 				lowerCamelCase__	(						):
  a						: Tuple					       =					WriteOnlyStringIO()
  with contextlib.redirect_stdout(_A    ):
    with contextlib.redirect_stderr(_A    ):
      with redirect_stdin(_A    ):
        yield
@contextlib.contextmanager
def 				lowerCamelCase__	(						):
  with tempfile.TemporaryDirectory() as dirname:
    with chdir(_A    ):
      yield dirname

class 							a__( lowerCamelCase__     ):
    pass

class 							a__( io.StringIO     ):







    def       lowercase_	(				self			:  List[str]						,      *__snake_case			:  Dict						,      **__snake_case			:  Union[str, Any]     ):
      raise OSError







    def       lowercase_	(				self			:  List[str]						,      *__snake_case			:  Optional[Any]						,      **__snake_case			:  Any     ):
      raise OSError







    def       lowercase_	(				self			:  int						,      *__snake_case			:  Optional[Any]						,      **__snake_case			:  Optional[int]     ):
      raise OSError







    def       lowercase_	(				self			:  Union[str, Any]						,      *__snake_case			:  Any						,      **__snake_case			:  str     ):
      return False

class 							a__( contextlib._RedirectStream     ):  # type: ignore
    lowercase__			=   """stdin"""
@contextlib.contextmanager
def 				lowerCamelCase__	(						_A    ):
  if root == ".":
    yield
    return
  a						: Optional[int]					       =					os.getcwd()
  os.chdir(_A    )
  try:
    yield
  except BaseException as exc:
    raise exc
  finally:
    os.chdir(_A    )

def 				lowerCamelCase__	(						_A=None    ):

  if maximum_memory_bytes is not None:
    import resource

    resource.setrlimit(resource.RLIMIT_AS			,      (maximum_memory_bytes, maximum_memory_bytes)    )
    resource.setrlimit(resource.RLIMIT_DATA			,      (maximum_memory_bytes, maximum_memory_bytes)    )
    if not platform.uname().system == "Darwin":
      resource.setrlimit(resource.RLIMIT_STACK			,      (maximum_memory_bytes, maximum_memory_bytes)    )

  faulthandler.disable()

  import builtins

  a						: List[str]					       =					None
  a						: List[Any]					       =					None

  import os

  a						: Optional[int]					       =					'1'

  a						: Union[str, Any]					       =					None
  a						: Optional[int]					       =					None
  a						: List[str]					       =					None
  a						: List[str]					       =					None
  a						: Optional[int]					       =					None
  a						: List[str]					       =					None
  a						: Union[str, Any]					       =					None
  a						: Any					       =					None
  a						: List[Any]					       =					None
  a						: Any					       =					None
  a						: Union[str, Any]					       =					None
  a						: Union[str, Any]					       =					None
  a						: Optional[Any]					       =					None
  a						: Any					       =					None
  a						: Union[str, Any]					       =					None
  a						: Optional[Any]					       =					None
  a						: List[Any]					       =					None
  a						: int					       =					None
  a						: List[str]					       =					None
  a						: int					       =					None
  a						: Dict					       =					None
  a						: Optional[int]					       =					None
  a						: List[str]					       =					None
  a						: int					       =					None
  a						: str					       =					None
  a						: Tuple					       =					None
  a						: List[str]					       =					None

  import shutil

  a						: Tuple					       =					None
  a						: Optional[Any]					       =					None
  a						: int					       =					None

  import subprocess

  a						: str					       =					None  # type: ignore

  a						: Optional[int]					       =					None

  import sys

  a						: Tuple					       =					None
  a						: Any					       =					None
  a						: str					       =					None
  a						: Optional[Any]					       =					None
  a						: Union[str, Any]					       =					None




'''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 				lowerCamelCase__	(						_A=None			,      _A=None    ):
  return field(default_factory=lambda: default			,      metadata=_A    )

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

class 							a__:







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

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







    def       lowercase_	(				self			:  int     ):
      a       ,     a						: Dict					       =					plt.subplots()
      a						: int					       =					'Time usage' if self.args.is_time else 'Memory usage'
      a						: 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()     ):
        a						: Dict					       =					sorted(set(self.result_dict[model_name]['bsz']     )     )
        a						: Any					       =					sorted(set(self.result_dict[model_name]['seq_len']     )     )
        a						: Any					       =					self.result_dict[model_name]['result']

        ((a)       ,     (a))						: Optional[Any]					       =					(
            (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
        )

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

          ((a)       ,     (a))						: Optional[int]					       =					(
              ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
          )

          a						: List[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."""

      a						: List[Any]					       =					title_str[:-4]
      a						: Optional[Any]					       =					'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 				lowerCamelCase__	(						):
  a						: Dict					       =					HfArgumentParser(_A    )
  a						: List[str]					       =					parser.parse_args_into_dataclasses()[0]
  a						: Dict					       =					Plot(args=_A    )
  plot.plot()


if __name__ == "__main__":
     main()