






"""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__=None , snake_case__=None       ):
	return field(default_factory=lambda: default , metadata=snake_case__       )



@dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	_A							:					str 			=					field(
	    metadata={"""help""": """The csv file to plot."""}							,							)
	_A							:					bool 			=					field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}							,							)
	_A							:					bool 			=					field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}							,							)
	_A							:					bool 			=					field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Disable logarithmic scale when plotting"""}							,							)
	_A							:					bool 			=					field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={
	        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
	    }							,							)
	_A							:					Optional[str] 			=					field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}							,							)
	_A							:					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						(		snake_case__       ):
	try:
		int(snake_case__       )
		return True
	except ValueError:
		return False






def       __UpperCamelCase						(		snake_case__       ):
	try:
		float(snake_case__       )
		return True
	except ValueError:
		return False



class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_				):
		A_		:    Optional[int]						     = args
		A_		:    Union[str, Any]						     = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

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





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

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

			A_		:    List[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:
					A_		:    List[str]						     = np.asarray(
					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,  dtype=lowerCAmelCase_  ,  )
				else:
					A_		:    Union[str, Any]						     = np.asarray(
					    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]  ,  dtype=np.floataa  ,  )

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

				A_		:    List[str]						     = np.asarray(lowerCAmelCase_  ,  lowerCAmelCase_				)[: len(lowerCAmelCase_				)]
				plt.scatter(
				    lowerCAmelCase_  ,  lowerCAmelCase_  ,  label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""				)
				plt.plot(lowerCAmelCase_  ,  lowerCAmelCase_  ,  """--"""				)

			title_str += f""" {label_model_name} vs."""

		A_		:    Dict						     = title_str[:-4]
		A_		:    Tuple						     = """Time in s""" if self.args.is_time else """Memory in MB"""

		# plot
		plt.title(lowerCAmelCase_				)
		plt.xlabel(lowerCAmelCase_				)
		plt.ylabel(lowerCAmelCase_				)
		plt.legend()

		if self.args.figure_png_file is not None:
			plt.savefig(self.args.figure_png_file				)
		else:
			plt.show()






def       __UpperCamelCase						(		):
	A_		:    int						     = HfArgumentParser(snake_case__       )
	A_		:    str						     = parser.parse_args_into_dataclasses()[0]
	A_		:    List[Any]						     = Plot(args=snake_case__       )
	plot.plot()


if __name__ == "__main__":
							main()









"""simple docstring"""
from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ = 10**-10       ):
	A_		:    Tuple						     = a
	while True:
		A_		:    List[str]						     = Decimal(snake_case__       ) - (
		    Decimal(eval(snake_case__       )       ) / Decimal(eval(str(diff(snake_case__       )       )       )       )  # noqa: S307
		)
		# This number dictates the accuracy of the answer
		if abs(eval(snake_case__       )       ) < precision:  # noqa: S307
			return float(snake_case__       )


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
							# Find root of polynomial
							print(F'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
							# Find Square Root of 5
							print(F'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
							# Exponential Roots
							print(F'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')

