






'''simple docstring'''

import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


SCREAMING_SNAKE_CASE__           =  logging.getLogger(__name__)






@dataclass
class 							a_		(     __lowercase							):
  lowercase							= field(
      default=0.0      ,			metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}							)
  lowercase							= field(default=__lowercase      ,			metadata={"""help""": """Whether to SortishSamler or not."""}							)
  lowercase							= field(
      default=__lowercase      ,			metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}							)
  lowercase							= field(default=__lowercase      ,			metadata={"""help""": """whether to use adafactor"""}							)
  lowercase							= field(
      default=__lowercase      ,			metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}							)
  lowercase							= field(
      default=__lowercase      ,			metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}							)
  lowercase							= field(default=__lowercase      ,			metadata={"""help""": """Dropout probability. Goes into model.config."""}							)
  lowercase							= field(
      default=__lowercase      ,			metadata={"""help""": """Attention dropout probability. Goes into model.config."""}							)
  lowercase							= field(
      default="""linear"""      ,			metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''}      ,			)


"""simple docstring"""




from __future__ import annotations

from collections.abc import Callable



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a = 100,				):
		SCREAMING_SNAKE_CASE_    			=			x_start
		SCREAMING_SNAKE_CASE_    			=			fnc(__a     )
		SCREAMING_SNAKE_CASE_    			=			0.0
		for _ in range(__a     ):
				# Approximates small segments of curve as linear and solve
				# for trapezoidal area
				SCREAMING_SNAKE_CASE_    			=			(x_end - x_start) / steps + xa
				SCREAMING_SNAKE_CASE_    			=			fnc(__a     )
				area += abs(fxa + fxa     ) * (xa - xa) / 2
				# Increment step
				SCREAMING_SNAKE_CASE_    			=			xa
				SCREAMING_SNAKE_CASE_    			=			fxa
		return area


if __name__ == "__main__":



						def 							_lowerCamelCase			(			__a     ):
								return x**3 + x**2

						print('f(x) = x^3 + x^2')
						print('The area between the curve, x = -5, x = 5 and the x axis is:')
						lowerCAmelCase__				=					10
						while i <= 100000:
												print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
												i *= 10