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      (       _lowercase=None						,    _lowercase=None		):
						return field(default_factory=lambda: default						,    metadata=_lowercase		)





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






def  A      (       _lowercase		):
						try:
												int(_lowercase		)
												return True
						except ValueError:
												return False
def  A      (       _lowercase		):
						try:
												float(_lowercase		)
												return True
						except ValueError:
												return False





class    lowercase__		:


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       int     							=				args
												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

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



						def 			__A			(			self      :							Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Dict     							=				plt.subplots()
												SCREAMING_SNAKE_CASE				:       int     							=				'''Time usage''' if self.args.is_time else '''Memory usage'''
												SCREAMING_SNAKE_CASE				:       Optional[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				:       List[Any]     							=				sorted(set(self.result_dict[model_name]['''bsz''']		)		)
																		SCREAMING_SNAKE_CASE				:       int     							=				sorted(set(self.result_dict[model_name]['''seq_len''']		)		)
																		SCREAMING_SNAKE_CASE				:       Optional[int]     							=				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				:       str     							=				(
																		    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				:       Any     							=				np.asarray(
																														    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,						dtype=_lowercase       ,						)
																								else:
																														SCREAMING_SNAKE_CASE				:       List[str]     							=				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)				:       Dict     							=				(
																								    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																								)

																								SCREAMING_SNAKE_CASE				:       Tuple     							=				np.asarray(_lowercase       ,						_lowercase		)[: len(_lowercase		)]
																								plt.scatter(
																								    _lowercase       ,						_lowercase       ,						label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""		)
																								plt.plot(_lowercase       ,						_lowercase       ,						'''--'''		)

																		title_str += f""" {label_model_name} vs."""

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

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

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






def  A      (       ):
						SCREAMING_SNAKE_CASE				:       int     							=				HfArgumentParser(_lowercase		)
						SCREAMING_SNAKE_CASE				:       Optional[int]     							=				parser.parse_args_into_dataclasses()[0]
						SCREAMING_SNAKE_CASE				:       Dict     							=				Plot(args=_lowercase		)
						plot.plot()


if __name__ == "__main__":
					main()






import random




def    lowerCAmelCase_  (		lowercase:				int	,       lowercase:				float	,       lowercase:				bool = False		)     ->					dict:







 '''simple docstring'''
 _UpperCamelCase:  dict    						=			{i: [] for i in range(lowercase		)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(lowercase		)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(lowercase		):
  for j in range(i + 1	,       lowercase		):
   if random.random() < probability:
    graph[i].append(lowercase		)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(lowercase		)
 return graph




def    lowerCAmelCase_  (		lowercase:				int		)     ->					dict:







 '''simple docstring'''
 return {
     i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
 }


if __name__ == "__main__":
  import doctest

  doctest.testmod()