

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






@dataclass
class _UpperCamelCase							:
			'''simple docstring'''







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

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

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






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Optional[int] , a							:	List[Any]		)							->    Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    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				: Optional[int]			       =    csv.DictReader(a		)
					for row in reader:
						SCREAMING_SNAKE_CASE				: 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				: Any			       =    int(row["result"]		)
						elif can_convert_to_float(row["result"]		):
							# value is not None
							SCREAMING_SNAKE_CASE				: Optional[Any]			       =    float(row["result"]		)






			def 							__UpperCamelCase	(     self							:	int		)							->    List[str]:

				"""simple docstring"""

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

					((SCREAMING_SNAKE_CASE)       ,(SCREAMING_SNAKE_CASE))				: Dict			       =    (
					    (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=a , )
						else:
							SCREAMING_SNAKE_CASE				: Any			       =    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)       ,(SCREAMING_SNAKE_CASE))				: Union[str, Any]			       =    (
						    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
						)

						SCREAMING_SNAKE_CASE				: Dict			       =    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."

				SCREAMING_SNAKE_CASE				: Tuple			       =    title_str[:-4]
				SCREAMING_SNAKE_CASE				: str			       =    "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 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: str			       =    HfArgumentParser(_a)
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    parser.parse_args_into_dataclasses()[0]
	SCREAMING_SNAKE_CASE				: List[str]			       =    Plot(args=_a)
	plot.plot()


if __name__ == "__main__":
			main()


from __future__ import annotations

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	if len(_a) == 0:
		raise ValueError("find_min() arg is an empty sequence")
	if (
	    left >= len(_a)
	    or left < -len(_a)
	    or right >= len(_a)
	    or right < -len(_a)
	):
		raise IndexError("list index out of range")
	if left == right:
		return nums[left]
	SCREAMING_SNAKE_CASE				: List[str]			       =    (left + right) >> 1  # the middle
	SCREAMING_SNAKE_CASE				: Tuple			       =    find_min(_a							,     _a							,     _a)  # find min in range[left, mid]
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    find_min(_a							,     mid + 1							,     _a)  # find min in range[mid + 1, right]

	return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)