





'''simple docstring'''




from dataclasses import dataclass, field
from typing import Optional


@dataclass
class 					SCREAMING_SNAKE_CASE       :
     lowerCAmelCase						     =			field(
         default='''codeparrot/codeparrot'''   ,						metadata={'''help''': '''Model name or path of model to be trained.'''}				)
     lowerCAmelCase						     =			field(
         default='''./'''   ,						metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}				)
     lowerCAmelCase						     =			field(
         default='''codeparrot/codeparrot-clean-train'''   ,						metadata={'''help''': '''Name or path of training dataset.'''}				)
     lowerCAmelCase						     =			field(
         default='''codeparrot/codeparrot-clean-valid'''   ,						metadata={'''help''': '''Name or path of validation dataset.'''}				)
     lowerCAmelCase						     =			field(default=2   ,						metadata={'''help''': '''Batch size for training.'''}				)
     lowerCAmelCase						     =			field(default=2   ,						metadata={'''help''': '''Batch size for evaluation.'''}				)
     lowerCAmelCase						     =			field(default=0.1   ,						metadata={'''help''': '''Value of weight decay.'''}				)
     lowerCAmelCase						     =			field(
         default=1_0000   ,						metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}				)
     lowerCAmelCase						     =			field(default=2E-4   ,						metadata={'''help''': '''Learning rate fo training.'''}				)
     lowerCAmelCase						     =			field(default='''cosine'''   ,						metadata={'''help''': '''Learning rate.'''}				)
     lowerCAmelCase						     =			field(
         default=750   ,						metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}				)
     lowerCAmelCase						     =			field(
         default=16   ,						metadata={'''help''': '''Number of gradient accumulation steps.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}				)
     lowerCAmelCase						     =			field(default=5_0000   ,						metadata={'''help''': '''Maximum number of training steps.'''}				)
     lowerCAmelCase						     =			field(
         default=-1   ,						metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}				)
     lowerCAmelCase						     =			field(default=1024   ,						metadata={'''help''': '''Sequence lengths used for training.'''}				)
     lowerCAmelCase						     =			field(default=1   ,						metadata={'''help''': '''Training seed.'''}				)
     lowerCAmelCase						     =			field(
         default=1024   ,						metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}				)
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''If True the data is pretokenized.'''}				)




@dataclass
class 					SCREAMING_SNAKE_CASE       :
     lowerCAmelCase						     =			field(
         default='''codeparrot/codeparrot'''   ,						metadata={'''help''': '''Model name or path of model to be evaluated.'''}				)
     lowerCAmelCase						     =			field(
         default='''codeparrot/codeparrot-clean-valid'''   ,						metadata={'''help''': '''Name or path of validation dataset.'''}				)
     lowerCAmelCase						     =			field(default=2   ,						metadata={'''help''': '''Batch size used for evaluation.'''}				)
     lowerCAmelCase						     =			field(
         default=-1   ,						metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}				)
     lowerCAmelCase						     =			field(default=1024   ,						metadata={'''help''': '''Length of sequences to be evaluated.'''}				)
     lowerCAmelCase						     =			field(default=1   ,						metadata={'''help''': '''Random seed used for evaluation.'''}				)




@dataclass
class 					SCREAMING_SNAKE_CASE       :
     lowerCAmelCase						     =			field(
         default='''codeparrot/codeparrot'''   ,						metadata={'''help''': '''Model name or path of model to be evaluated.'''}				)
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Number of workers used for code evaluation.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Sample from the language model\'s output distribution.'''}				)
     lowerCAmelCase						     =			field(default=0.2   ,						metadata={'''help''': '''Sampling temperature used for generation.'''}				)
     lowerCAmelCase						     =			field(default=256   ,						metadata={'''help''': '''Maximum number of newly generated tokens.'''}				)
     lowerCAmelCase						     =			field(default=0   ,						metadata={'''help''': '''Top-k parameter used for generation.'''}				)
     lowerCAmelCase						     =			field(default=0.95   ,						metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}				)
     lowerCAmelCase						     =			field(default=10   ,						metadata={'''help''': '''Number of generations to run in parallel.'''}				)
     lowerCAmelCase						     =			field(
         default=200   ,						metadata={'''help''': '''Number of completions to generate for each sample.'''}				)
     lowerCAmelCase						     =			field(default=1   ,						metadata={'''help''': '''Random seed used for evaluation.'''}				)
     lowerCAmelCase						     =			field(
         default='''eval_results.json'''   ,						metadata={'''help''': '''Random seed used for evaluation.'''}				)
     lowerCAmelCase						     =			field(
         default='''0'''   ,						metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}				)
     lowerCAmelCase						     =			field(
         default=-1   ,						metadata={
             '''help''': (
                 '''Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'''
                 ''' number corresponds to which GPU device id to run on.'''
             )
         }   ,						)




@dataclass
class 					SCREAMING_SNAKE_CASE       :
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
         }   ,						)
     lowerCAmelCase						     =			field(
         default='''transformersbook/codeparrot'''   ,						metadata={'''help''': '''Folder or name of dataset to process.'''}				)
     lowerCAmelCase						     =			field(
         default='''codeparrot-clean'''   ,						metadata={'''help''': '''Folder to save processed processed dataset.'''}				)
     lowerCAmelCase						     =			field(
         default=10_0000   ,						metadata={'''help''': '''Number of files to save per JSON output file.'''}				)
     lowerCAmelCase						     =			field(default='''content'''   ,						metadata={'''help''': '''Column containing text data to process.'''}				)
     lowerCAmelCase						     =			field(
         default=1000   ,						metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}				)
     lowerCAmelCase						     =			field(
         default=100   ,						metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}				)
     lowerCAmelCase						     =			field(
         default=0.25   ,						metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}				)
     lowerCAmelCase						     =			field(
         default=1.5   ,						metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}				)
     lowerCAmelCase						     =			field(
         default=0.7   ,						metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}				)
     lowerCAmelCase						     =			field(
         default='''codeparrot/codeparrot'''   ,						metadata={'''help''': '''Name or path to the tokenizer.'''}   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''If True, near-duplicate samples are removed.'''}				)
     lowerCAmelCase						     =			field(
         default=0.85   ,						metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}				)




@dataclass
class 					SCREAMING_SNAKE_CASE       :
     lowerCAmelCase						     =			field(
         default='''gpt2'''   ,						metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}				)
     lowerCAmelCase						     =			field(
         default='''transformersbook/codeparrot-train'''   ,						metadata={'''help''': '''Dataset to train tokenizer on.'''}				)
     lowerCAmelCase						     =			field(default='''content'''   ,						metadata={'''help''': '''Column containing text data to process.'''}				)
     lowerCAmelCase						     =			field(default=20_0000   ,						metadata={'''help''': '''Number of examples to train tokenizer on.'''}				)
     lowerCAmelCase						     =			field(
         default=3_2768   ,						metadata={'''help''': '''Number of examples to train the tokenizer on.'''}				)
     lowerCAmelCase						     =			field(default='''codeparrot'''   ,						metadata={'''help''': '''Name of new tokenizer.'''}				)
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Push saved tokenizer to the hub.'''}				)




@dataclass
class 					SCREAMING_SNAKE_CASE       :
     lowerCAmelCase						     =			field(
         default='''codeparrot/codeparrot'''   ,						metadata={'''help''': '''Name or path to the tokenizer.'''}				)
     lowerCAmelCase						     =			field(
         default='''codeparrot/codeparrot-clean-train'''   ,						metadata={'''help''': '''Name or path to the dataset to pretokenize.'''}				)
     lowerCAmelCase						     =			field(
         default='''tokenized-codeparrot-train'''   ,						metadata={'''help''': '''Repo name of the pretokenized data.'''}				)
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Number of workers used for code evaluation.'''}				)




@dataclass
class 					SCREAMING_SNAKE_CASE       :
     lowerCAmelCase						     =			field(
         default='''gpt2-large'''   ,						metadata={'''help''': '''Configuration to use for model initialization.'''}				)
     lowerCAmelCase						     =			field(
         default='''codeparrot/codeparrot'''   ,						metadata={'''help''': '''Tokenizer attached to model.'''}				)
     lowerCAmelCase						     =			field(default='''codeparrot'''   ,						metadata={'''help''': '''Name of the created model.'''}				)
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Push saved tokenizer to the hub.'''}				)






'''simple docstring'''




from __future__ import annotations

import math


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			size
          # approximate the overall size of segment tree with given value
          __A		:					Optional[Any]						=			[0 for i in range(0		,      4 * size)]
          # create array to store lazy update
          __A		:					Optional[Any]						=			[0 for i in range(0		,      4 * size)]
          __A		:					str						=			[0 for i in range(0		,      4 * size)]  # flag for lazy update


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return idx * 2


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return idx * 2 + 1


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if left_element == right_element:
               __A		:					List[Any]						=			a[left_element - 1]
          else:
               __A		:					List[str]						=			(left_element + right_element) // 2
               self.build(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
               self.build(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase)
               __A		:					Any						=			max(
                   self.segment_tree[self.left(_UpperCAmelCase)]		,      self.segment_tree[self.right(_UpperCAmelCase)])


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if self.flag[idx] is True:
               __A		:					Optional[Any]						=			self.lazy[idx]
               __A		:					Optional[Any]						=			False
               if left_element != right_element:
                    __A		:					List[Any]						=			self.lazy[idx]
                    __A		:					Dict						=			self.lazy[idx]
                    __A		:					Tuple						=			True
                    __A		:					Union[str, Any]						=			True

          if right_element < a or left_element > b:
               return True
          if left_element >= a and right_element <= b:
               __A		:					Optional[int]						=			val
               if left_element != right_element:
                    __A		:					Tuple						=			val
                    __A		:					Any						=			val
                    __A		:					Tuple						=			True
                    __A		:					Union[str, Any]						=			True
               return True
          __A		:					str						=			(left_element + right_element) // 2
          self.update(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          self.update(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					int						=			max(
              self.segment_tree[self.left(_UpperCAmelCase)]		,      self.segment_tree[self.right(_UpperCAmelCase)])
          return True


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if self.flag[idx] is True:
               __A		:					Union[str, Any]						=			self.lazy[idx]
               __A		:					List[str]						=			False
               if left_element != right_element:
                    __A		:					Union[str, Any]						=			self.lazy[idx]
                    __A		:					Optional[int]						=			self.lazy[idx]
                    __A		:					str						=			True
                    __A		:					Union[str, Any]						=			True
          if right_element < a or left_element > b:
               return -math.inf
          if left_element >= a and right_element <= b:
               return self.segment_tree[idx]
          __A		:					Any						=			(left_element + right_element) // 2
          __A		:					int						=			self.query(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					Union[str, Any]						=			self.query(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          return max(_UpperCAmelCase		,      _UpperCAmelCase)


     def __str__(		self):

          '''simple docstring'''






          return str([self.query(1		,      1		,      self.size		,      _UpperCAmelCase		,      _UpperCAmelCase) for i in range(1		,      self.size + 1)])


if __name__ == "__main__":
  lowercase__       : Union[str, Any]         =       [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
  lowercase__       : str         =       15
  lowercase__       : List[Any]         =       SegmentTree(size)
  segt.build(1, 1, size, A)
  print(segt.query(1, 1, size, 4, 6))
  print(segt.query(1, 1, size, 7, 11))
  print(segt.query(1, 1, size, 7, 12))
  segt.update(1, 1, size, 1, 3, 1_11)
  print(segt.query(1, 1, size, 1, 15))
  segt.update(1, 1, size, 7, 8, 2_35)
  print(segt)