




'''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_(			snake_case							: Dict=None							,       snake_case							: Optional[int]=None  ):

							'''simple docstring'''






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




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





def   UpperCamelCase_(			snake_case							: List[str]  ):

							'''simple docstring'''






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


def   UpperCamelCase_(			snake_case							: List[Any]  ):

							'''simple docstring'''






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




class 				_snake_case	:




			def __init__(      self       ,							a__							)       ->       Optional[Any]:

										'''simple docstring'''




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

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




			def      lowerCAmelCase__       (      self							)       ->       str:

										'''simple docstring'''




										snake_case_      ,   snake_case_			      =   plt.subplots()
										snake_case_			      =   "Time usage" if self.args.is_time else "Memory usage"
										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()							):
																	snake_case_			      =   sorted(set(self.result_dict[model_name]["bsz"]							)							)
																	snake_case_			      =   sorted(set(self.result_dict[model_name]["seq_len"]							)							)
																	snake_case_			      =   self.result_dict[model_name]["result"]

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

																	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:
																															snake_case_			      =   np.asarray(
																															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,							dtype=a__       ,							)
																								else:
																															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       ,							)

																								((snake_case_)      ,   (snake_case_))			      =   (
																								    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
																								)

																								snake_case_			      =   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.'

										snake_case_			      =   title_str[:-4]
										snake_case_			      =   "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   UpperCamelCase_(			):

							'''simple docstring'''






							snake_case_			      =   HfArgumentParser(lowerCamelCase__  )
							snake_case_			      =   parser.parse_args_into_dataclasses()[0]
							snake_case_			      =   Plot(args=lowerCamelCase__  )
							plot.plot()


if __name__ == "__main__":
		main()










'''simple docstring'''





import heapq


def   UpperCamelCase_(			snake_case							: List[Any]							,       snake_case							: List[str]							,       snake_case							: Dict  ):

							'''simple docstring'''






							snake_case_			      =   [(0, start)]  # cost from start node,end node
							snake_case_			      =   set()
							while heap:
														((snake_case_)      ,   (snake_case_))			      =   heapq.heappop(snake_case  )
														if u in visited:
																					continue
														visited.add(snake_case  )
														if u == end:
																					return cost
														for v, c in graph[u]:
																					if v in visited:
																												continue
																					snake_case_			      =   cost + c
																					heapq.heappush(snake_case							,       (next_item, v)  )
							return -1


_SCREAMING_SNAKE_CASE							:     Dict   	=				{
    "A": [["B", 2], ["C", 5]],
    "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
    "C": [["A", 5], ["F", 3]],
    "D": [["B", 3]],
    "E": [["B", 4], ["F", 3]],
    "F": [["C", 3], ["E", 3]],
}
_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["F", 3]],
    "F": [],
}
_SCREAMING_SNAKE_CASE							:     Any   	=				{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}

_SCREAMING_SNAKE_CASE							:     Tuple   	=				dijkstra(G, "E", "C")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

_SCREAMING_SNAKE_CASE							:     str   	=				dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 3 --> F == 3

_SCREAMING_SNAKE_CASE							:     List[Any]   	=				dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
		import doctest

		doctest.testmod()




