





"""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=SCREAMING_SNAKE_CASE     )






@dataclass
class 							lowercase__			:

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


def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						try:
												int(SCREAMING_SNAKE_CASE     )
												return True
						except ValueError:
												return False

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						try:
												float(SCREAMING_SNAKE_CASE     )
												return True
						except ValueError:
												return False






class 							lowercase__			:

			"""simple docstring"""







			def __init__(      self						,   _A    ):






									'''simple docstring'''







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

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







			def   _a      (      self    ):






									'''simple docstring'''







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

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

															UpperCamelCase				:		int				   =			(
															    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:
																											UpperCamelCase				:		int				   =			np.asarray(
																											    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]						,   dtype=_A						,   )
																					else:
																											UpperCamelCase				:		Optional[int]				   =			np.asarray(
																											    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]						,   dtype=np.floataa						,   )

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

																					UpperCamelCase				:		Optional[Any]				   =			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."""

									UpperCamelCase				:		Union[str, Any]				   =			title_str[:-4]
									UpperCamelCase				:		int				   =			"""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							():
						UpperCamelCase				:		Tuple				   =			HfArgumentParser(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Optional[int]				   =			parser.parse_args_into_dataclasses()[0]
						UpperCamelCase				:		int				   =			Plot(args=SCREAMING_SNAKE_CASE     )
						plot.plot()


if __name__ == "__main__":
		main()





'''simple docstring'''
def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):

	def count_of_possible_combinations(A__						) -> int:
		if target < 0:
			return 0
		if target == 0:
			return 1
		return sum(count_of_possible_combinations(target - item						) for item in array						)

	return count_of_possible_combinations(A__						)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):

	def count_of_possible_combinations_with_dp_array(
	    A__  ,      A__						) -> int:
		if target < 0:
			return 0
		if target == 0:
			return 1
		if dp_array[target] != -1:
			return dp_array[target]
		a_      				=						sum(
		    count_of_possible_combinations_with_dp_array(target - item  ,      A__						)
		    for item in array						)
		a_      				=						answer
		return answer

	a_      				=						[-1] * (target + 1)
	return count_of_possible_combinations_with_dp_array(A__  ,      A__						)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						[0] * (target + 1)
	a_      				=						1

	for i in range(1  ,      target + 1						):
		for j in range(A__						):
			if i - array[j] >= 0:
				dp_array[i] += dp_array[i - array[j]]

	return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		lowercase__											=3
		lowercase__											=5
		lowercase__											=[1, 2, 5]
		print(combination_sum_iv(n, array, target))
