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

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       return 0.0





def    _lowerCAmelCase    (     lowerCamelCase__							:							np.ndarray,						lowerCamelCase__							:							int				)  ->    tuple[int | float, int | float]:
  _SCREAMING_SNAKE_CASE   :				Any							    = min([-2_0, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = max([2_0, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
  return lowest, highest





def    _lowerCAmelCase    (     lowerCamelCase__							:							FilterType,						lowerCamelCase__							:							int				)  ->    None:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = 5_1_2
  _SCREAMING_SNAKE_CASE   :				int							    = [1] + [0] * (size - 1)
  _SCREAMING_SNAKE_CASE   :				Any							    = [filter_type.process(lowerCamelCase__				) for item in inputs]

  _SCREAMING_SNAKE_CASE   :				Tuple							    = [0] * (samplerate - size)  # zero-padding
  outputs += filler
  _SCREAMING_SNAKE_CASE   :				int							    = np.abs(np.fft.fft(lowerCamelCase__				)				)
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 2_0 * np.logaa(lowerCamelCase__				)

  # Frequencies on log scale from 24 to nyquist frequency
  plt.xlim(2_4,						samplerate / 2 - 1				)
  plt.xlabel("Frequency (Hz)"				)
  plt.xscale("log"				)

  # Display within reasonable bounds
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = get_bounds(lowerCamelCase__,						lowerCamelCase__				)
  plt.ylim(max([-8_0, bounds[0]]				),						min([8_0, bounds[1]]				)				)
  plt.ylabel("Gain (dB)"				)

  plt.plot(lowerCamelCase__				)
  plt.show()





def    _lowerCAmelCase    (     lowerCamelCase__							:							FilterType,						lowerCamelCase__							:							int				)  ->    None:
  _SCREAMING_SNAKE_CASE   :				Any							    = 5_1_2
  _SCREAMING_SNAKE_CASE   :				Any							    = [1] + [0] * (size - 1)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = [filter_type.process(lowerCamelCase__				) for item in inputs]

  _SCREAMING_SNAKE_CASE   :				Tuple							    = [0] * (samplerate - size)  # zero-padding
  outputs += filler
  _SCREAMING_SNAKE_CASE   :				Any							    = np.angle(np.fft.fft(lowerCamelCase__				)				)

  # Frequencies on log scale from 24 to nyquist frequency
  plt.xlim(2_4,						samplerate / 2 - 1				)
  plt.xlabel("Frequency (Hz)"				)
  plt.xscale("log"				)

  plt.ylim(-2 * pi,						2 * pi				)
  plt.ylabel("Phase shift (Radians)"				)
  plt.plot(np.unwrap(lowerCamelCase__,						-2 * pi				)				)
  plt.show()





"""simple docstring"""
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Any=1_0_2_4				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = [], []

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = list(zip(lowerCamelCase__,						lowerCamelCase__				)				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = sorted_examples[0]

  def is_too_big(lowerCamelCase__							:							List[Any]				):
    return tok(lowerCamelCase__,						return_tensors="pt"				).input_ids.shape[1] > max_tokens

  for src, tgt in tqdm(sorted_examples[1:]				):
    _SCREAMING_SNAKE_CASE   :				int							    = new_src + " " + src
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_tgt + " " + tgt
    if is_too_big(lowerCamelCase__				) or is_too_big(lowerCamelCase__				):  # cant fit, finalize example
      finished_src.append(lowerCamelCase__				)
      finished_tgt.append(lowerCamelCase__				)
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = src, tgt
    else:  # can fit, keep adding
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = cand_src, cand_tgt

    # cleanup
  if new_src:
    assert new_tgt
    finished_src.append(lowerCamelCase__				)
    finished_tgt.append(lowerCamelCase__				)
  return finished_src, finished_tgt





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Path,						lowerCamelCase__							:							Dict,						lowerCamelCase__							:							List[str]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				int							    = Path(lowerCamelCase__				)
  save_path.mkdir(exist_ok=lowerCamelCase__				)
  for split in ["train"]:
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
    _SCREAMING_SNAKE_CASE   :				Any							    = [x.rstrip() for x in Path(lowerCamelCase__				).open().readlines()]
    _SCREAMING_SNAKE_CASE   :				int							    = [x.rstrip() for x in Path(lowerCamelCase__				).open().readlines()]
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = pack_examples(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
    print(f'''packed {split} split from {len(lowerCamelCase__				)} examples -> {len(lowerCamelCase__				)}.'''				)
    Path(save_path / f'''{split}.source'''				).open("w"				).write("\n".join(lowerCamelCase__				)				)
    Path(save_path / f'''{split}.target'''				).open("w"				).write("\n".join(lowerCamelCase__				)				)
  for split in ["val", "test"]:
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
    shutil.copyfile(lowerCamelCase__,						save_path / f'''{split}.source'''				)
    shutil.copyfile(lowerCamelCase__,						save_path / f'''{split}.target'''				)





def    _lowerCAmelCase    (     )  ->    int:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = argparse.ArgumentParser()
  parser.add_argument("--tok_name",						type=lowerCamelCase__,						help="like facebook/bart-large-cnn,t5-base, etc."				)
  parser.add_argument("--max_seq_len",						type=lowerCamelCase__,						default=1_2_8				)
  parser.add_argument("--data_dir",						type=lowerCamelCase__				)
  parser.add_argument("--save_path",						type=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = parser.parse_args()
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoTokenizer.from_pretrained(args.tok_name				)
  return pack_data_dir(lowerCamelCase__,						Path(args.data_dir				),						args.max_seq_len,						args.save_path				)


if __name__ == "__main__":
      packer_cli()



