




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  UpperCAmelCase_							(				__UpperCAmelCase						:     str=None      ,      __UpperCAmelCase						:     Optional[int]=None							) ->			int:
      return field(default_factory=lambda: default      ,      metadata=__UpperCAmelCase							)





@dataclass
class 	lowerCamelCase_							:


 '''simple docstring'''







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


def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			Union[str, Any]:
      try:
            int(__UpperCAmelCase							)
            return True
      except ValueError:
            return False

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Union[str, Any]							) ->			Tuple:
      try:
            float(__UpperCAmelCase							)
            return True
      except ValueError:
            return False





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Dict    ,    _lowerCAmelCase  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      args
       SCREAMING_SNAKE_CASE_     						=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

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




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

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

             SCREAMING_SNAKE_CASE_     						=      (
                 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_     						=      np.asarray(
                             [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,    dtype=_lowerCAmelCase    ,    )
                   else:
                         SCREAMING_SNAKE_CASE_     						=      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_))     						=      (
                       ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                   )

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

             title_str += F" {label_model_name} vs."

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

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

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


def  UpperCAmelCase_							(				) ->			Optional[int]:
      SCREAMING_SNAKE_CASE_     						=      HfArgumentParser(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      parser.parse_args_into_dataclasses()[0]
      SCREAMING_SNAKE_CASE_     						=      Plot(args=__UpperCAmelCase							)
      plot.plot()


if __name__ == "__main__":
   main()





from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str = "isbn/0140328726"							) ->			dict:
      SCREAMING_SNAKE_CASE_     						=      olid.strip().strip('/'							)  # Remove leading/trailing whitespace & slashes
      if new_olid.count('/'							) != 1:
            SCREAMING_SNAKE_CASE_     						=      f"{olid} is not a valid Open Library olid"
            raise ValueError(__UpperCAmelCase							)
      return requests.get(f"https://openlibrary.org/{new_olid}.json"							).json()

def  UpperCAmelCase_							(				__UpperCAmelCase						:     dict							) ->			dict:
      SCREAMING_SNAKE_CASE_     						=      {
          'title': 'Title',
          'publish_date': 'Publish date',
          'authors': 'Authors',
          'number_of_pages': 'Number of pages:',
          'first_sentence': 'First sentence',
          'isbn_10': 'ISBN (10)',
          'isbn_13': 'ISBN (13)',
      }
      SCREAMING_SNAKE_CASE_     						=      {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
      SCREAMING_SNAKE_CASE_     						=      [
          get_openlibrary_data(author['key']							)['name'] for author in data['Authors']
      ]
      SCREAMING_SNAKE_CASE_     						=      data['First sentence']['value']
      for key, value in data.items():
            if isinstance(__UpperCAmelCase      ,      __UpperCAmelCase							):
                  SCREAMING_SNAKE_CASE_     						=      ', '.join(__UpperCAmelCase							)
      return data


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   while True:
      lowerCamelCase__			:       Optional[Any]         =							input('\nEnter the ISBN code to search (or \'quit\' to stop): ').strip()
      if isbn.lower() in ("", "q", "quit", "exit", "stop"):
         break

      if len(isbn) not in (10, 13) or not isbn.isdigit():
         print(f'''Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.''')
         continue

      print(f'''\nSearching Open Library for ISBN: {isbn}...\n''')

      try:
         lowerCamelCase__			:       Union[str, Any]         =							summarize_book(get_openlibrary_data(f'''isbn/{isbn}'''))
         print('\n'.join(f'''{key}: {value}''' for key, value in book_summary.items()))
      except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
         print(f'''Sorry, there are no results for ISBN: {isbn}.''')