



"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger



class    SCREAMING_SNAKE_CASE__						(      _UpperCAmelCase				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			params

    UpperCAmelCase			:      Optional[int]			=			np.array(lowercase__		)
    UpperCAmelCase			:      Any			=			np.array([len(lowercase__		) for t in data]		)

    self.check()
    self.remove_long_sequences()
    self.remove_empty_sequences()
    self.remove_unknown_sequences()
    self.check()
    self.print_statistics()

   def __getitem__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   int:





    '''simple docstring'''




    return (self.token_ids[index], self.lengths[index])

   def __len__(     self		)						->   List[Any]:





    '''simple docstring'''




    return len(self.lengths		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    assert len(self.token_ids		) == len(self.lengths		)
    assert all(self.lengths[i] == len(self.token_ids[i]		) for i in range(len(self.lengths		)		)		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			self.params.max_model_input_size
    UpperCAmelCase			:      List[str]			=			self.lengths > max_len
    logger.info(F"Splitting {sum(lowercase__		)} too long sequences."		)

    def divide_chunks(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
     return [l[i : i + n] for i in range(0   ,  len(lowercase__		)   ,  lowercase__		)]

    UpperCAmelCase			:      Optional[Any]			=			[]
    UpperCAmelCase			:      Union[str, Any]			=			[]
    if self.params.mlm:
     UpperCAmelCase			:      Tuple			=			self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
    else:
     UpperCAmelCase			:      List[str]			=			self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

    for seq_, len_ in zip(self.token_ids   ,  self.lengths		):
     assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
     if len_ <= max_len:
      new_tok_ids.append(seq_		)
      new_lengths.append(len_		)
     else:
      UpperCAmelCase			:      int			=			[]
      for sub_s in divide_chunks(seq_   ,  max_len - 2		):
       if sub_s[0] != cls_id:
        UpperCAmelCase			:      List[Any]			=			np.insert(lowercase__   ,  0   ,  lowercase__		)
       if sub_s[-1] != sep_id:
        UpperCAmelCase			:      Optional[int]			=			np.insert(lowercase__   ,  len(lowercase__		)   ,  lowercase__		)
       assert len(lowercase__		) <= max_len
       assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
       sub_seqs.append(lowercase__		)

      new_tok_ids.extend(lowercase__		)
      new_lengths.extend([len(lowercase__		) for l in sub_seqs]		)

    UpperCAmelCase			:      Union[str, Any]			=			np.array(lowercase__		)
    UpperCAmelCase			:      Optional[Any]			=			np.array(lowercase__		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			len(self		)
    UpperCAmelCase			:      Optional[int]			=			self.lengths > 11
    UpperCAmelCase			:      int			=			self.token_ids[indices]
    UpperCAmelCase			:      int			=			self.lengths[indices]
    UpperCAmelCase			:      Union[str, Any]			=			len(self		)
    logger.info(F"Remove {init_size - new_size} too short (<=11 tokens) sequences."		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    if "unk_token" not in self.params.special_tok_ids:
     return
    else:
     UpperCAmelCase			:      Optional[Any]			=			self.params.special_tok_ids["unk_token"]
    UpperCAmelCase			:      Optional[Any]			=			len(self		)
    UpperCAmelCase			:      int			=			np.array([np.count_nonzero(a == unk_token_id		) for a in self.token_ids]		)
    UpperCAmelCase			:      List[Any]			=			(unk_occs / self.lengths) < 0.5
    UpperCAmelCase			:      int			=			self.token_ids[indices]
    UpperCAmelCase			:      Union[str, Any]			=			self.lengths[indices]
    UpperCAmelCase			:      Any			=			len(self		)
    logger.info(F"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    if not self.params.is_master:
     return
    logger.info(F"{len(self		)} sequences"		)
    # data_len = sum(self.lengths)
    # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
    # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

    # unk_idx = self.params.special_tok_ids['unk_token']
    # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
    # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			[t[0] for t in batch]
    UpperCAmelCase			:      Dict			=			[t[1] for t in batch]
    assert len(lowercase__		) == len(lowercase__		)

    # Max for paddings
    UpperCAmelCase			:      List[Any]			=			max(lowercase__		)

    # Pad token ids
    if self.params.mlm:
     UpperCAmelCase			:      Any			=			self.params.special_tok_ids["pad_token"]
    else:
     UpperCAmelCase			:      Union[str, Any]			=			self.params.special_tok_ids["unk_token"]
    UpperCAmelCase			:      Any			=			[list(t.astype(lowercase__		)		) + [pad_idx] * (max_seq_len_ - len(lowercase__		)) for t in token_ids]
    assert len(tk_		) == len(lowercase__		)
    assert all(len(lowercase__		) == max_seq_len_ for t in tk_		)

    UpperCAmelCase			:      List[str]			=			torch.tensor(tk_		)  # (bs, max_seq_len_)
    UpperCAmelCase			:      str			=			torch.tensor(lowercase__		)  # (bs)
    return tk_t, lg_t










"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np

def    _snake_case		(		UpperCamelCase		:	bytes    ,	UpperCamelCase		:	int		):
 UpperCAmelCase			:      Tuple			=			F"{sampling_rate}"
 UpperCAmelCase			:      Optional[int]			=			"""1"""
 UpperCAmelCase			:      Dict			=			"""f32le"""
 UpperCAmelCase			:      Optional[int]			=			[
     """ffmpeg""",
     """-i""",
     """pipe:0""",
     """-ac""",
     ac,
     """-ar""",
     ar,
     """-f""",
     format_for_conversion,
     """-hide_banner""",
     """-loglevel""",
     """quiet""",
     """pipe:1""",
 ]

 try:
  with subprocess.Popen(UpperCamelCase    ,	stdin=subprocess.PIPE    ,	stdout=subprocess.PIPE		) as ffmpeg_process:
   UpperCAmelCase			:      str			=			ffmpeg_process.communicate(UpperCamelCase		)
 except FileNotFoundError as error:
  raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""		) from error
 UpperCAmelCase			:      Tuple			=			output_stream[0]
 UpperCAmelCase			:      Union[str, Any]			=			np.frombuffer(UpperCamelCase    ,	np.floataa		)
 if audio.shape[0] == 0:
  raise ValueError("""Malformed soundfile"""		)
 return audio

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	float    ,	UpperCamelCase		:	str = "f32le"    ,	):
 UpperCAmelCase			:      Tuple			=			F"{sampling_rate}"
 UpperCAmelCase			:      List[str]			=			"""1"""
 if format_for_conversion == "s16le":
  UpperCAmelCase			:      int			=			2
 elif format_for_conversion == "f32le":
  UpperCAmelCase			:      int			=			4
 else:
  raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"		)

 UpperCAmelCase			:      Optional[Any]			=			platform.system()
 if system == "Linux":
  UpperCAmelCase			:      Tuple			=			"""alsa"""
  UpperCAmelCase			:      Any			=			"""default"""
 elif system == "Darwin":
  UpperCAmelCase			:      Any			=			"""avfoundation"""
  UpperCAmelCase			:      int			=			""":0"""
 elif system == "Windows":
  UpperCAmelCase			:      Optional[int]			=			"""dshow"""
  UpperCAmelCase			:      str			=			"""default"""

 UpperCAmelCase			:      Dict			=			[
     """ffmpeg""",
     """-f""",
     format_,
     """-i""",
     input_,
     """-ac""",
     ac,
     """-ar""",
     ar,
     """-f""",
     format_for_conversion,
     """-fflags""",
     """nobuffer""",
     """-hide_banner""",
     """-loglevel""",
     """quiet""",
     """pipe:1""",
 ]
 UpperCAmelCase			:      str			=			int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
 UpperCAmelCase			:      Union[str, Any]			=			_ffmpeg_stream(UpperCamelCase    ,	UpperCamelCase		)
 for item in iterator:
  yield item

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	float    ,	UpperCamelCase		:	Optional[int] = None    ,	UpperCamelCase		:	Optional[Union[Tuple[float, float], float]] = None    ,	UpperCamelCase		:	str = "f32le"    ,	):
 if stream_chunk_s is not None:
  UpperCAmelCase			:      List[Any]			=			stream_chunk_s
 else:
  UpperCAmelCase			:      List[Any]			=			chunk_length_s

 UpperCAmelCase			:      Union[str, Any]			=			ffmpeg_microphone(UpperCamelCase    ,	UpperCamelCase    ,	format_for_conversion=UpperCamelCase		)
 if format_for_conversion == "s16le":
  UpperCAmelCase			:      Dict			=			np.intaa
  UpperCAmelCase			:      Optional[Any]			=			2
 elif format_for_conversion == "f32le":
  UpperCAmelCase			:      Any			=			np.floataa
  UpperCAmelCase			:      Dict			=			4
 else:
  raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"		)

 if stride_length_s is None:
  UpperCAmelCase			:      Tuple			=			chunk_length_s / 6
 UpperCAmelCase			:      Optional[Any]			=			int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
 if isinstance(UpperCamelCase    ,	(int, float)		):
  UpperCAmelCase			:      str			=			[stride_length_s, stride_length_s]

 UpperCAmelCase			:      List[Any]			=			int(round(sampling_rate * stride_length_s[0]		)		) * size_of_sample
 UpperCAmelCase			:      Optional[int]			=			int(round(sampling_rate * stride_length_s[1]		)		) * size_of_sample
 UpperCAmelCase			:      List[Any]			=			datetime.datetime.now()
 UpperCAmelCase			:      Tuple			=			datetime.timedelta(seconds=UpperCamelCase		)
 for item in chunk_bytes_iter(UpperCamelCase    ,	UpperCamelCase    ,	stride=(stride_left, stride_right)    ,	stream=UpperCamelCase		):
  # Put everything back in numpy scale
  UpperCAmelCase			:      Dict			=			np.frombuffer(item["""raw"""]    ,	dtype=UpperCamelCase		)
  UpperCAmelCase			:      List[str]			=			(
      item["""stride"""][0] // size_of_sample,
      item["""stride"""][1] // size_of_sample,
  )
  UpperCAmelCase			:      Optional[Any]			=			sampling_rate
  audio_time += delta
  if datetime.datetime.now() > audio_time + 10 * delta:
   # We're late !! SKIP
   continue
  yield item

def    _snake_case		(		UpperCamelCase		:	Dict    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	Tuple[int, int]    ,	UpperCamelCase		:	bool = False		):
 UpperCAmelCase			:      Any			=			B""""""
 UpperCAmelCase			,	UpperCAmelCase			:      List[Any]			=			stride
 if stride_left + stride_right >= chunk_len:
  raise ValueError(
      F"Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"		)
 UpperCAmelCase			:      int			=			0
 for raw in iterator:
  acc += raw
  if stream and len(UpperCamelCase		) < chunk_len:
   UpperCAmelCase			:      Optional[Any]			=			(_stride_left, 0)
   yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
  else:
   while len(UpperCamelCase		) >= chunk_len:
    # We are flushing the accumulator
    UpperCAmelCase			:      List[str]			=			(_stride_left, stride_right)
    UpperCAmelCase			:      List[str]			=			{"""raw""": acc[:chunk_len], """stride""": stride}
    if stream:
     UpperCAmelCase			:      List[str]			=			False
    yield item
    UpperCAmelCase			:      Optional[Any]			=			stride_left
    UpperCAmelCase			:      Union[str, Any]			=			acc[chunk_len - stride_left - stride_right :]
    # Last chunk
 if len(UpperCamelCase		) > stride_left:
  UpperCAmelCase			:      Union[str, Any]			=			{"""raw""": acc, """stride""": (_stride_left, 0)}
  if stream:
   UpperCAmelCase			:      str			=			False
  yield item

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	int		):
 UpperCAmelCase			:      List[Any]			=			2**24  # 16Mo
 try:
  with subprocess.Popen(UpperCamelCase    ,	stdout=subprocess.PIPE    ,	bufsize=UpperCamelCase		) as ffmpeg_process:
   while True:
    UpperCAmelCase			:      Optional[Any]			=			ffmpeg_process.stdout.read(UpperCamelCase		)
    if raw == b"":
     break
    yield raw
 except FileNotFoundError as error:
  raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""		) from error
