






from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	def is_in_circle(lowerCamelCase				,  lowerCamelCase					) -> bool:
		__lowercase             =  sqrt((x**2) + (y**2)					)
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	__lowercase             =  mean(
	    int(is_in_circle(uniform(-1.0				,  1.0					)				,  uniform(-1.0				,  1.0					)					)					)
	    for _ in range(lowerCamelCase					)					)
	# The ratio of the area for circle to square is pi/4.
	__lowercase             =  proportion * 4
	print(F'The estimated value of pi is {pi_estimate}'					)
	print(F'The numpy value of pi is {pi}'					)
	print(F'The total error is {abs(pi - pi_estimate					)}'					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = 0.0				,  lowerCamelCase = 1.0				,  ):




	'''simple docstring'''







	return mean(
	    function_to_integrate(uniform(lowerCamelCase				,  lowerCamelCase					)					) for _ in range(lowerCamelCase					)					) * (max_value - min_value)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase = 0.0				,  lowerCamelCase = 1.0					):




	'''simple docstring'''







	def identity_function(lowerCamelCase					) -> float:
		return x

	__lowercase             =  area_under_curve_estimator(
	    lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
	__lowercase             =  (max_value * max_value - min_value * min_value) / 2

	print("""******************"""					)
	print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'					)
	print(F'Estimated value is {estimated_value}'					)
	print(F'Expected value is {expected_value}'					)
	print(F'Total error is {abs(estimated_value - expected_value					)}'					)
	print("""******************"""					)





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	def function_to_integrate(lowerCamelCase					) -> float:
		return sqrt(4.0 - x * x					)

	__lowercase             =  area_under_curve_estimator(
	    lowerCamelCase				,  lowerCamelCase				,  0.0				,  2.0					)

	print("""******************"""					)
	print("""Estimating pi using area_under_curve_estimator"""					)
	print(F'Estimated value is {estimated_value}'					)
	print(F'Expected value is {pi}'					)
	print(F'Total error is {abs(estimated_value - pi					)}'					)
	print("""******************"""					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()









import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__UpperCamelCase      :      Any     						=						logging.get_logger(__name__)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:str   =       field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()   )}   )
	__snake_case	:str   =       field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}   )
	__snake_case	:int   =       field(
	    default=1_2_8   ,			metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.task_name.lower()


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Optional[int]   =       'train'
	__snake_case	:int   =       'dev'
	__snake_case	:Any   =       'test'


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:GlueDataTrainingArguments
	__snake_case	:str
	__snake_case	:List[InputFeatures]







	def __init__(  self      :							Dict       ,				_lowerCAmelCase      :							GlueDataTrainingArguments       ,				_lowerCAmelCase      :							PreTrainedTokenizerBase       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Union[str, Split] = Split.train       ,				_lowerCAmelCase      :							Optional[str] = None       ,				)		-> List[Any]:
		"""simple docstring"""







		warnings.warn(
		    """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
		    """library. You can have a look at this example script for pointers: """
		    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""       ,				_lowerCAmelCase       ,				)
		__lowercase             =  args
		__lowercase             =  glue_processors[args.task_name]()
		__lowercase             =  glue_output_modes[args.task_name]
		if isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			):
			try:
				__lowercase             =  Split[mode]
			except KeyError:
				raise KeyError("""mode is not a valid split name"""			)
        # Load data features from cache or dataset file
		__lowercase             =  os.path.join(
		    cache_dir if cache_dir is not None else args.data_dir       ,				F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'       ,				)
		__lowercase             =  self.processor.get_labels()
		if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
		    "RobertaTokenizer",
		    "RobertaTokenizerFast",
		    "XLMRobertaTokenizer",
		    "BartTokenizer",
		    "BartTokenizerFast",
		):
			# HACK(label indices are swapped in RoBERTa pretrained model)
			__lowercase       ,						__lowercase             =  label_list[2], label_list[1]
		__lowercase             =  label_list

		# Make sure only the first process in distributed training processes the dataset,
		# and the others will use the cache.
		__lowercase             =  cached_features_file + """.lock"""
		with FileLock(_lowerCAmelCase			):
			if os.path.exists(_lowerCAmelCase			) and not args.overwrite_cache:
				__lowercase             =  time.time()
				__lowercase             =  torch.load(_lowerCAmelCase			)
				logger.info(
				    F'Loading features from cached file {cached_features_file} [took %.3f s]'       ,				time.time() - start			)
			else:
				logger.info(F'Creating features from dataset file at {args.data_dir}'			)

				if mode == Split.dev:
					__lowercase             =  self.processor.get_dev_examples(args.data_dir			)
				elif mode == Split.test:
					__lowercase             =  self.processor.get_test_examples(args.data_dir			)
				else:
					__lowercase             =  self.processor.get_train_examples(args.data_dir			)
				if limit_length is not None:
					__lowercase             =  examples[:limit_length]
				__lowercase             =  glue_convert_examples_to_features(
				    _lowerCAmelCase       ,				_lowerCAmelCase       ,				max_length=args.max_seq_length       ,				label_list=_lowerCAmelCase       ,				output_mode=self.output_mode       ,				)
				__lowercase             =  time.time()
				torch.save(self.features       ,				_lowerCAmelCase			)
				# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
				logger.info(
				    F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'			)







	def __len__(  self      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		return len(self.features			)







	def __getitem__(  self      :							Tuple       ,				_lowerCAmelCase      :							Optional[int]			)		-> InputFeatures:
		"""simple docstring"""







		return self.features[i]







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		return self.label_list

