'''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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_=None  ,       SCREAMING_SNAKE_CASE_=None							):
     return field(default_factory=lambda: default  ,       metadata=a_							)


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



def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     try:
          int(a_							)
          return True
     except ValueError:
          return False

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     try:
          float(a_							)
          return True
     except ValueError:
          return False


class       UpperCAmelCase__  :
   def __init__(self       ,				_a				)    ->     Optional[Any]:
        lowercase_					:       Union[str, Any]     				=					args
        lowercase_					:       Optional[int]     				=					defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

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





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

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

             lowercase_					:       Optional[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:
                       lowercase_					:       List[Any]     				=					np.asarray(
                           [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,				dtype=_UpperCAmelCase       ,				)
                  else:
                       lowercase_					:       Optional[Any]     				=					np.asarray(
                           [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,				dtype=np.floataa       ,				)

                  (lowercase_)					:       List[str]     				=					(
                      ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
                  )

                  lowercase_					:       Optional[Any]     				=					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.'''

        lowercase_					:       Optional[int]     				=					title_str[:-4]
        lowercase_					:       Optional[Any]     				=					'''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 _UpperCamelCase   (       ):
     lowercase_					:       List[str]     				=					HfArgumentParser(a_							)
     lowercase_					:       str     				=					parser.parse_args_into_dataclasses()[0]
     lowercase_					:       Tuple     				=					Plot(args=a_							)
     plot.plot()


if __name__ == "__main__":
 main()





'''simple docstring'''




import math
import os
import sys

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					''
     try:
          with open(SCREAMING_SNAKE_CASE_  ,       'rb'							) as binary_file:
               lowercase_					:       Dict     				=					binary_file.read()
          for dat in data:
               lowercase_					:       Union[str, Any]     				=					f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('File not accessible'							)
          sys.exit()

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lexicon.pop(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					last_match_id

     if math.loga(SCREAMING_SNAKE_CASE_							).is_integer():
          for curr_key in lexicon:
               lowercase_					:       Tuple     				=					'0' + lexicon[curr_key]

     lowercase_					:       Any     				=					bin(SCREAMING_SNAKE_CASE_							)[2:]

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Dict     				=					{'0': '0', '1': '1'}
     lowercase_				,lowercase_					:       Any     				=					'', ''
     lowercase_					:       List[Any]     				=					len(SCREAMING_SNAKE_CASE_							)

     for i in range(len(SCREAMING_SNAKE_CASE_							)							):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          lowercase_					:       str     				=					lexicon[curr_string]
          result += last_match_id
          add_key_to_lexicon(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
          index += 1
          lowercase_					:       List[Any]     				=					''

     while curr_string != "" and curr_string not in lexicon:
          curr_string += "0"

     if curr_string != "":
          lowercase_					:       Optional[Any]     				=					lexicon[curr_string]
          result += last_match_id

     return result

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					os.path.getsize(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					bin(SCREAMING_SNAKE_CASE_							)[2:]
     lowercase_					:       Optional[Any]     				=					len(SCREAMING_SNAKE_CASE_							)

     return "0" * (length_length - 1) + file_length_binary + compressed

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					8
     try:
          with open(SCREAMING_SNAKE_CASE_  ,       'wb'							) as opened_file:
               lowercase_					:       List[Any]     				=					[
                   to_write[i : i + byte_length]
                   for i in range(0  ,       len(SCREAMING_SNAKE_CASE_							)  ,       SCREAMING_SNAKE_CASE_							)
               ]

               if len(result_byte_array[-1]							) % byte_length == 0:
                    result_byte_array.append('10000000'							)
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1]							) - 1
                    )

               for elem in result_byte_array:
                    opened_file.write(int(SCREAMING_SNAKE_CASE_  ,       2							).to_bytes(1  ,       byteorder='big'							)							)
     except OSError:
          print('File not accessible'							)
          sys.exit()

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					read_file_binary(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[str]     				=					compress_data(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Union[str, Any]     				=					add_file_length(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     write_file_binary(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 compress(sys.argv[1], sys.argv[2])




