






'''simple docstring'''







def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = 10    ,      UpperCamelCase = 1000    ,      UpperCamelCase = True				)				->	int:
							assert (
							    isinstance(UpperCamelCase    ,      UpperCamelCase				)
							    and isinstance(UpperCamelCase    ,      UpperCamelCase				)
							    and isinstance(UpperCamelCase    ,      UpperCamelCase				)
							), "Invalid type of value(s) specified to function!"

							if min_val > max_val:
														raise ValueError("""Invalid value for min_val or max_val (min_value < max_value)"""				)
							return min_val if option else max_val





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	int:
							return int((number_a + number_a) / 2				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	None:
							assert (
							    isinstance(UpperCamelCase    ,      UpperCamelCase				) and isinstance(UpperCamelCase    ,      UpperCamelCase				) and isinstance(UpperCamelCase    ,      UpperCamelCase				)
							), 'argument values must be type of "int"'

							if lower > higher:
														raise ValueError("""argument value for lower and higher must be(lower > higher)"""				)

							if not lower < to_guess < higher:
														raise ValueError(
														    """guess value must be within the range of lower and higher value"""				)

							def answer(UpperCamelCase				) -> str:
														if number > to_guess:
																					return "high"
														elif number < to_guess:
																					return "low"
														else:
																					return "same"

							print("""started..."""				)

							lowerCamelCase__ :     List[str]	  =	lower
							lowerCamelCase__ :     Optional[int]	  =	higher

							lowerCamelCase__ :     Dict	  =	[]

							while True:
														lowerCamelCase__ :     Any	  =	get_avg(UpperCamelCase    ,      UpperCamelCase				)
														last_numbers.append(UpperCamelCase				)

														if answer(UpperCamelCase				) == "low":
																					lowerCamelCase__ :     int	  =	number
														elif answer(UpperCamelCase				) == "high":
																					lowerCamelCase__ :     Optional[Any]	  =	number
														else:
																					break

							print(f'''guess the number : {last_numbers[-1]}'''				)
							print(f'''details : {last_numbers!s}'''				)





def 			SCREAMING_SNAKE_CASE_	()				->	None:
							lowerCamelCase__ :     List[Any]	  =	int(input("""Enter lower value : """				).strip()				)
							lowerCamelCase__ :     Union[str, Any]	  =	int(input("""Enter high value : """				).strip()				)
							lowerCamelCase__ :     Tuple	  =	int(input("""Enter value to guess : """				).strip()				)
							guess_the_number(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)


if __name__ == "__main__":
			main()









'''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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase=None    ,      UpperCamelCase=None				)				->	Any:
							return field(default_factory=lambda: default    ,      metadata=UpperCamelCase				)

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





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							try:
														int(UpperCamelCase				)
														return True
							except ValueError:
														return False





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							try:
														float(UpperCamelCase				)
														return True
							except ValueError:
														return False

class _lowercase     :




							def __init__(							self:    Tuple      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     int	  =	args
														lowerCamelCase__ :     Optional[int]	  =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

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




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__  ,		lowerCamelCase__ :     Tuple	  =	plt.subplots()
														lowerCamelCase__ :     Any	  =	"""Time usage""" if self.args.is_time else """Memory usage"""
														lowerCamelCase__ :     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()					):
																					lowerCamelCase__ :     Any	  =	sorted(set(self.result_dict[model_name]["""bsz"""]					)					)
																					lowerCamelCase__ :     int	  =	sorted(set(self.result_dict[model_name]["""seq_len"""]					)					)
																					lowerCamelCase__ :     Any	  =	self.result_dict[model_name]["""result"""]

																					((lowerCamelCase__)  ,		(lowerCamelCase__)) :     Dict	  =	(
																					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																					)

																					lowerCamelCase__ :     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:
																																			lowerCamelCase__ :     int	  =	np.asarray(
																																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=UpperCamelCase__      ,							)
																												else:
																																			lowerCamelCase__ :     List[Any]	  =	np.asarray(
																																			    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,							dtype=np.floataa      ,							)

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

																												lowerCamelCase__ :     int	  =	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.'''

														lowerCamelCase__ :     Any	  =	title_str[:-4]
														lowerCamelCase__ :     Optional[int]	  =	"""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 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     str	  =	HfArgumentParser(UpperCamelCase				)
							lowerCamelCase__ :     str	  =	parser.parse_args_into_dataclasses()[0]
							lowerCamelCase__ :     Any	  =	Plot(args=UpperCamelCase				)
							plot.plot()


if __name__ == "__main__":
			main()

