





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					(  SCREAMING_SNAKE_CASE__			:  Any=None ,      SCREAMING_SNAKE_CASE__			:  Optional[int]=None   )       ->				int:

							'''simple docstring'''






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


@dataclass
class 						lowerCamelCase   :







							"""simple docstring"""




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




def _a					(  SCREAMING_SNAKE_CASE__			:  List[Any]   )       ->				Union[str, Any]:

							'''simple docstring'''






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




def _a					(  SCREAMING_SNAKE_CASE__			:  str   )       ->				int:

							'''simple docstring'''






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


class 						lowerCamelCase   :







							"""simple docstring"""




							def __init__(					self	:     str, _UpperCAmelCase	:     Tuple						)      -> Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			int      =						args
														SCREAMING_SNAKE_CASE__   :			int      =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

														with open(self.args.csv_file, newline=""						) as csv_file:
																					SCREAMING_SNAKE_CASE__   :			List[str]      =						csv.DictReader(snake_case__						)
																					for row in reader:
																												SCREAMING_SNAKE_CASE__   :			str      =						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__   :			str      =						int(row["result"]						)
																												elif can_convert_to_float(row["result"]						):
																																			# value is not None
																																			SCREAMING_SNAKE_CASE__   :			Tuple      =						float(row["result"]						)






							def 					A_	(					self	:     List[str]						)      -> List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Tuple      =						plt.subplots()
														SCREAMING_SNAKE_CASE__   :			int      =						"Time usage" if self.args.is_time else "Memory usage"
														SCREAMING_SNAKE_CASE__   :			List[str]      =						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__   :			Dict      =						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__   :			Tuple      =						self.result_dict[model_name]["result"]

																					(SCREAMING_SNAKE_CASE__)   :			Any      =						(
																					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																					)

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

																												SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						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__   :			Optional[Any]      =						title_str[:-4]
														SCREAMING_SNAKE_CASE__   :			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 _a					(  )       ->				Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			List[str]      =						HfArgumentParser(SCREAMING_SNAKE_CASE__   )
							SCREAMING_SNAKE_CASE__   :			Tuple      =						parser.parse_args_into_dataclasses()[0]
							SCREAMING_SNAKE_CASE__   :			Tuple      =						Plot(args=SCREAMING_SNAKE_CASE__   )
							plot.plot()


if __name__ == "__main__":
		main()














"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  Tuple				=				word.upper()
     UpperCAmelCase__		:  Union[str, Any]				=				0
     UpperCAmelCase__		:  Optional[Any]				=				{}
     UpperCAmelCase__		:  List[Any]				=				[]

     for letter in word:
          if letter not in letter_nums:
               UpperCAmelCase__		:  Tuple				=				str(snake_case							)
               next_num += 1
          word_pattern.append(letter_nums[letter]							)
     return ".".join(snake_case							)


if __name__ == "__main__":
 import pprint
 import time

 _lowerCAmelCase     :     Optional[Any]            =	time.time()
 with open("""dictionary.txt""") as in_file:
  _lowerCAmelCase     :     Optional[int]            =	in_file.read().splitlines()

 _lowerCAmelCase     :     dict            =	{}
 for word in word_list:
  _lowerCAmelCase     :     int            =	get_word_pattern(word)
  if pattern in all_patterns:
   all_patterns[pattern].append(word)
  else:
   _lowerCAmelCase     :     Any            =	[word]

 with open("""word_patterns.txt""", """w""") as out_file:
  out_file.write(pprint.pformat(all_patterns))

 _lowerCAmelCase     :     Union[str, Any]            =	round(time.time() - start_time, 2)
 print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
 # Done!  9,581 word patterns found in 0.58 seconds.


