





from dataclasses import dataclass, field
from typing import Optional
@dataclass
class        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	Optional[str]				      = field(
       default='codeparrot/codeparrot'    ,			metadata={'help': 'Model name or path of model to be trained.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default='./'    ,			metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default='codeparrot/codeparrot-clean-train'    ,			metadata={'help': 'Name or path of training dataset.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default='codeparrot/codeparrot-clean-valid'    ,			metadata={'help': 'Name or path of validation dataset.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=2    ,			metadata={'help': 'Batch size for training.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=2    ,			metadata={'help': 'Batch size for evaluation.'}   )
   _UpperCamelCase					:	Optional[float]				      = field(default=0.1    ,			metadata={'help': 'Value of weight decay.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(
       default=1_00_00    ,			metadata={'help': 'Size of buffer used to shuffle streaming dataset.'}   )
   _UpperCamelCase					:	Optional[float]				      = field(default=2E-4    ,			metadata={'help': 'Learning rate fo training.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(default='cosine'    ,			metadata={'help': 'Learning rate.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(
       default=7_50    ,			metadata={'help': 'Number of warmup steps in the learning rate schedule.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(
       default=16    ,			metadata={'help': 'Number of gradient accumulation steps.'}   )
   _UpperCamelCase					:	Optional[bool]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=5_00_00    ,			metadata={'help': 'Maximum number of training steps.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(
       default=-1    ,			metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=10_24    ,			metadata={'help': 'Sequence lengths used for training.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=1    ,			metadata={'help': 'Training seed.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(
       default=10_24    ,			metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'}    ,			)
   _UpperCamelCase					:	Optional[str]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'States path if the training should continue from a checkpoint folder.'}   )
   _UpperCamelCase					:	Optional[bool]				      = field(default=UpperCAmelCase    ,			metadata={'help': 'If True the data is pretokenized.'}   )






@dataclass
class        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	Optional[str]				      = field(
       default='codeparrot/codeparrot'    ,			metadata={'help': 'Model name or path of model to be evaluated.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default='codeparrot/codeparrot-clean-valid'    ,			metadata={'help': 'Name or path of validation dataset.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=2    ,			metadata={'help': 'Batch size used for evaluation.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(
       default=-1    ,			metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=10_24    ,			metadata={'help': 'Length of sequences to be evaluated.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=1    ,			metadata={'help': 'Random seed used for evaluation.'}   )






@dataclass
class        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	Optional[str]				      = field(
       default='codeparrot/codeparrot'    ,			metadata={'help': 'Model name or path of model to be evaluated.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=UpperCAmelCase    ,			metadata={'help': 'Number of workers used for code evaluation.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}    ,			)
   _UpperCamelCase					:	Optional[bool]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'Sample from the language model\'s output distribution.'}   )
   _UpperCamelCase					:	Optional[float]				      = field(default=0.2    ,			metadata={'help': 'Sampling temperature used for generation.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=2_56    ,			metadata={'help': 'Maximum number of newly generated tokens.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=0    ,			metadata={'help': 'Top-k parameter used for generation.'}   )
   _UpperCamelCase					:	Optional[float]				      = field(default=0.95    ,			metadata={'help': 'Top-p parameter used for nucleus sampling.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=10    ,			metadata={'help': 'Number of generations to run in parallel.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(
       default=2_00    ,			metadata={'help': 'Number of completions to generate for each sample.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=1    ,			metadata={'help': 'Random seed used for evaluation.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default='eval_results.json'    ,			metadata={'help': 'Random seed used for evaluation.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default='0'    ,			metadata={'help': 'Allow `code_eval` to execute Python code on machine'}   )
   _UpperCamelCase					:	Optional[int]				      = 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     :
   _UpperCamelCase					:	Optional[int]				      = field(
       default=UpperCAmelCase    ,			metadata={
           'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
       }    ,			)
   _UpperCamelCase					:	Optional[str]				      = field(
       default='transformersbook/codeparrot'    ,			metadata={'help': 'Folder or name of dataset to process.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default='codeparrot-clean'    ,			metadata={'help': 'Folder to save processed processed dataset.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(
       default=10_00_00    ,			metadata={'help': 'Number of files to save per JSON output file.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(default='content'    ,			metadata={'help': 'Column containing text data to process.'}   )
   _UpperCamelCase					:	Optional[float]				      = field(
       default=10_00    ,			metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}   )
   _UpperCamelCase					:	Optional[float]				      = field(
       default=1_00    ,			metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}   )
   _UpperCamelCase					:	Optional[float]				      = field(
       default=0.25    ,			metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}   )
   _UpperCamelCase					:	Optional[float]				      = field(
       default=1.5    ,			metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}   )
   _UpperCamelCase					:	Optional[float]				      = field(
       default=0.7    ,			metadata={'help': 'Probability for filtering config, test and uncommon files.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default='codeparrot/codeparrot'    ,			metadata={'help': 'Name or path to the tokenizer.'}    ,			)
   _UpperCamelCase					:	Optional[bool]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'If True, near-duplicate samples are removed.'}   )
   _UpperCamelCase					:	Optional[float]				      = field(
       default=0.85    ,			metadata={'help': 'Jaccard threshold for near-duplicate samples.'}   )






@dataclass
class        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	Optional[str]				      = field(
       default='gpt2'    ,			metadata={'help': 'Base tokenizer to build new tokenizer from.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default='transformersbook/codeparrot-train'    ,			metadata={'help': 'Dataset to train tokenizer on.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(default='content'    ,			metadata={'help': 'Column containing text data to process.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=20_00_00    ,			metadata={'help': 'Number of examples to train tokenizer on.'}   )
   _UpperCamelCase					:	Optional[int]				      = field(
       default=3_27_68    ,			metadata={'help': 'Number of examples to train the tokenizer on.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(default='codeparrot'    ,			metadata={'help': 'Name of new tokenizer.'}   )
   _UpperCamelCase					:	Optional[bool]				      = field(default=UpperCAmelCase    ,			metadata={'help': 'Push saved tokenizer to the hub.'}   )






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






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









from __future__ import annotations

import math
from collections.abc import Callable




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 100	,		)       ->     float:
  lowercase__			    =						x_start
  lowercase__			    =						fnc(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						0.0

  for _ in range(_SCREAMING_SNAKE_CASE	):
    # Approximates curve as a sequence of linear lines and sums their length
    lowercase__			    =						(x_end - x_start) / steps + xa
    lowercase__			    =						fnc(_SCREAMING_SNAKE_CASE	)
    length += math.hypot(xa - xa	,		fxa - fxa	)

    # Increment step
    lowercase__			    =						xa
    lowercase__			    =						fxa

  return length


if __name__ == "__main__":




      def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return math.sin(10 * x	)

      print("""f(x) = sin(10 * x)""")
      print("""The length of the curve from x = -10 to x = 10 is:""")
      lowercase_					     =	10
      while i <= 100_000:
            print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
            i *= 10


