




import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      F"""{sampling_rate}"""
  lowerCAmelCase__										=      '''1'''
  lowerCAmelCase__										=      '''f32le'''
  lowerCAmelCase__										=      [
      '''ffmpeg''',
      '''-i''',
      '''pipe:0''',
      '''-ac''',
      ac,
      '''-ar''',
      ar,
      '''-f''',
      format_for_conversion,
      '''-hide_banner''',
      '''-loglevel''',
      '''quiet''',
      '''pipe:1''',
  ]

  try:
    with subprocess.Popen(__lowerCAmelCase   , stdin=subprocess.PIPE   , stdout=subprocess.PIPE				) as ffmpeg_process:
      lowerCAmelCase__										=      ffmpeg_process.communicate(__lowerCAmelCase				)
  except FileNotFoundError as error:
    raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''				) from error
  lowerCAmelCase__										=      output_stream[0]
  lowerCAmelCase__										=      np.frombuffer(__lowerCAmelCase   , np.floataa				)
  if audio.shape[0] == 0:
    raise ValueError('''Malformed soundfile'''				)
  return audio
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase = "f32le"   , ):
  lowerCAmelCase__										=      F"""{sampling_rate}"""
  lowerCAmelCase__										=      '''1'''
  if format_for_conversion == "s16le":
    lowerCAmelCase__										=      2
  elif format_for_conversion == "f32le":
    lowerCAmelCase__										=      4
  else:
    raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""				)

  lowerCAmelCase__										=      platform.system()
  if system == "Linux":
    lowerCAmelCase__										=      '''alsa'''
    lowerCAmelCase__										=      '''default'''
  elif system == "Darwin":
    lowerCAmelCase__										=      '''avfoundation'''
    lowerCAmelCase__										=      ''':0'''
  elif system == "Windows":
    lowerCAmelCase__										=      '''dshow'''
    lowerCAmelCase__										=      '''default'''

  lowerCAmelCase__										=      [
      '''ffmpeg''',
      '''-f''',
      format_,
      '''-i''',
      input_,
      '''-ac''',
      ac,
      '''-ar''',
      ar,
      '''-f''',
      format_for_conversion,
      '''-fflags''',
      '''nobuffer''',
      '''-hide_banner''',
      '''-loglevel''',
      '''quiet''',
      '''pipe:1''',
  ]
  lowerCAmelCase__										=      int(round(sampling_rate * chunk_length_s				)				) * size_of_sample
  lowerCAmelCase__										=      _ffmpeg_stream(__lowerCAmelCase   , __lowerCAmelCase				)
  for item in iterator:
    yield item
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase = None   , __lowerCAmelCase = None   , __lowerCAmelCase = "f32le"   , ):







  if stream_chunk_s is not None:
    lowerCAmelCase__										=      stream_chunk_s
  else:
    lowerCAmelCase__										=      chunk_length_s

  lowerCAmelCase__										=      ffmpeg_microphone(__lowerCAmelCase   , __lowerCAmelCase   , format_for_conversion=__lowerCAmelCase				)
  if format_for_conversion == "s16le":
    lowerCAmelCase__										=      np.intaa
    lowerCAmelCase__										=      2
  elif format_for_conversion == "f32le":
    lowerCAmelCase__										=      np.floataa
    lowerCAmelCase__										=      4
  else:
    raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""				)

  if stride_length_s is None:
    lowerCAmelCase__										=      chunk_length_s / 6
  lowerCAmelCase__										=      int(round(sampling_rate * chunk_length_s				)				) * size_of_sample
  if isinstance(__lowerCAmelCase   , (int, float)				):
    lowerCAmelCase__										=      [stride_length_s, stride_length_s]

  lowerCAmelCase__										=      int(round(sampling_rate * stride_length_s[0]				)				) * size_of_sample
  lowerCAmelCase__										=      int(round(sampling_rate * stride_length_s[1]				)				) * size_of_sample
  lowerCAmelCase__										=      datetime.datetime.now()
  lowerCAmelCase__										=      datetime.timedelta(seconds=__lowerCAmelCase				)
  for item in chunk_bytes_iter(__lowerCAmelCase   , __lowerCAmelCase   , stride=(stride_left, stride_right)   , stream=__lowerCAmelCase				):
    # Put everything back in numpy scale
    lowerCAmelCase__										=      np.frombuffer(item['''raw''']   , dtype=__lowerCAmelCase				)
    lowerCAmelCase__										=      (
        item['''stride'''][0] // size_of_sample,
        item['''stride'''][1] // size_of_sample,
    )
    lowerCAmelCase__										=      sampling_rate
    audio_time += delta
    if datetime.datetime.now() > audio_time + 10 * delta:
      # We're late !! SKIP
      continue
    yield item
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase = False				):
  lowerCAmelCase__										=      B''''''
  lowerCAmelCase__										=      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}"""				)
  lowerCAmelCase__										=      0
  for raw in iterator:
    acc += raw
    if stream and len(__lowerCAmelCase				) < chunk_len:
      lowerCAmelCase__										=      (_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(__lowerCAmelCase				) >= chunk_len:
        # We are flushing the accumulator
        lowerCAmelCase__										=      (_stride_left, stride_right)
        lowerCAmelCase__										=      {'''raw''': acc[:chunk_len], '''stride''': stride}
        if stream:
          lowerCAmelCase__										=      False
        yield item
        lowerCAmelCase__										=      stride_left
        lowerCAmelCase__										=      acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(__lowerCAmelCase				) > stride_left:
    lowerCAmelCase__										=      {'''raw''': acc, '''stride''': (_stride_left, 0)}
    if stream:
      lowerCAmelCase__										=      False
    yield item




def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      2**24  # 16Mo
  try:
    with subprocess.Popen(__lowerCAmelCase   , stdout=subprocess.PIPE   , bufsize=__lowerCAmelCase				) as ffmpeg_process:
      while True:
        lowerCAmelCase__										=      ffmpeg_process.stdout.read(__lowerCAmelCase				)
        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








"""simple docstring"""





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

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

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

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

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

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




