






"""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    (__a=None					,       __a=None							)				-> int:
    """simple docstring"""






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




@dataclass
class 							lowerCAmelCase__       :






      '''simple docstring'''

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



def       _A    (__a							)				-> Dict:
    """simple docstring"""






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



def       _A    (__a							)				-> Any:
    """simple docstring"""






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




class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						int		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	args
          SCREAMING_SNAKE_CASE_   :						List[str]							=	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

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



      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	plt.subplots()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	"Time usage" if self.args.is_time else "Memory usage"
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	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_   :						Optional[Any]							=	sorted(set(self.result_dict[model_name]['''bsz''']))
              SCREAMING_SNAKE_CASE_   :						Dict							=	sorted(set(self.result_dict[model_name]['''seq_len''']))
              SCREAMING_SNAKE_CASE_   :						Any							=	self.result_dict[model_name]["result"]

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

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

                  SCREAMING_SNAKE_CASE_   :						str							=	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.'

          SCREAMING_SNAKE_CASE_   :						Tuple							=	title_str[:-4]
          SCREAMING_SNAKE_CASE_   :						int							=	"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    ()				-> Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	HfArgumentParser(lowercase_							)
    SCREAMING_SNAKE_CASE_   :						List[str]							=	parser.parse_args_into_dataclasses()[0]
    SCREAMING_SNAKE_CASE_   :						Tuple							=	Plot(args=lowercase_							)
    plot.plot()


if __name__ == "__main__":
  main()










"""simple docstring"""






from __future__ import annotations

import requests



def       _A    (__a							)				-> dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
    return requests.get(__a							).json()



def       _A    (__a = 10							)				-> list[dict]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	'''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
    SCREAMING_SNAKE_CASE_   :						Dict							=	requests.get(__a							).json()[:max_stories]
    return [get_hackernews_story(__a							) for story_id in story_ids]



def       _A    (__a = 10							)				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Tuple							=	hackernews_top_stories(__a							)
    return "\n".join('''* [{title}]({url})'''.format(**__a							) for story in stories							)


if __name__ == "__main__":
  print(hackernews_top_stories_as_markdown())


