






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    (      __lowerCAmelCase=None ,		__lowerCAmelCase=None	)->  Dict:





						"""simple docstring"""



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





@dataclass
class __lowerCamelCase :





	"""simple docstring"""






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



def __A    (      __lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



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


def __A    (      __lowerCAmelCase	)->  Optional[int]:





						"""simple docstring"""



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





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







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

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

													_UpperCAmelCase									=			(
													    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									=			np.asarray(
																									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,    dtype=UpperCAmelCase       ,    )
																			else:
																									_UpperCAmelCase									=			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))									=			(
																			    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
																			)

																			_UpperCAmelCase									=			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."""

							_UpperCAmelCase									=			title_str[:-4]
							_UpperCAmelCase									=			'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 __A    (      )->  List[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			HfArgumentParser(__lowerCAmelCase	)
						_UpperCAmelCase									=			parser.parse_args_into_dataclasses()[0]
						_UpperCAmelCase									=			Plot(args=__lowerCAmelCase	)
						plot.plot()


if __name__ == "__main__":
		main()


# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase			:     int           =   subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__lowerCAmelCase			:     Any           =   (
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode('utf-8').split()
)

__lowerCAmelCase			:     str           =   '|'.join(sys.argv[1:])
__lowerCAmelCase			:     Tuple           =   re.compile(RF'''^({joined_dirs}).*?\.py$''')

__lowerCAmelCase			:     Union[str, Any]           =   [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')

