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

import numpy as np







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->np.array:
  a__:     Union[str, Any]										=	F'{sampling_rate}'
  a__:     str										=	'1'
  a__:     Dict										=	'f32le'
  a__:     List[str]										=	[
      'ffmpeg',
      '-i',
      'pipe:0',
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = "f32le"		,     )	->Optional[int]:
  a__:     Optional[Any]										=	F'{sampling_rate}'
  a__:     str										=	'1'
  if format_for_conversion == "s16le":
    a__:     List[str]										=	2
  elif format_for_conversion == "f32le":
    a__:     Optional[int]										=	4
  else:
    raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'   )

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

  a__:     List[str]										=	[
      'ffmpeg',
      '-f',
      format_,
      '-i',
      input_,
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-fflags',
      'nobuffer',
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]
  a__:     Tuple										=	int(round(sampling_rate * chunk_length_s   )   ) * size_of_sample
  a__:     Tuple										=	_ffmpeg_stream(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  for item in iterator:
    yield item







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = None		,     _SCREAMING_SNAKE_CASE = None		,     _SCREAMING_SNAKE_CASE = "f32le"		,     )	->Union[str, Any]:
  if stream_chunk_s is not None:
    a__:     Union[str, Any]										=	stream_chunk_s
  else:
    a__:     Tuple										=	chunk_length_s

  a__:     int										=	ffmpeg_microphone(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     format_for_conversion=_SCREAMING_SNAKE_CASE   )
  if format_for_conversion == "s16le":
    a__:     List[Any]										=	np.intaa
    a__:     Union[str, Any]										=	2
  elif format_for_conversion == "f32le":
    a__:     List[Any]										=	np.floataa
    a__:     Any										=	4
  else:
    raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'   )

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

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = False   )	->List[Any]:
  a__:     int										=	b''
  a__							,    a__:     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}'   )
  a__:     Dict										=	0
  for raw in iterator:
    acc += raw
    if stream and len(_SCREAMING_SNAKE_CASE   ) < chunk_len:
      a__:     str										=	(_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(_SCREAMING_SNAKE_CASE   ) >= chunk_len:
        # We are flushing the accumulator
        a__:     Union[str, Any]										=	(_stride_left, stride_right)
        a__:     int										=	{'raw': acc[:chunk_len], 'stride': stride}
        if stream:
          a__:     Tuple										=	False
        yield item
        a__:     List[str]										=	stride_left
        a__:     Union[str, Any]										=	acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(_SCREAMING_SNAKE_CASE   ) > stride_left:
    a__:     int										=	{'raw': acc, 'stride': (_stride_left, 0)}
    if stream:
      a__:     Optional[int]										=	False
    yield item







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->str:
  a__:     Union[str, Any]										=	2**24  # 16Mo
  try:
    with subprocess.Popen(_SCREAMING_SNAKE_CASE		,     stdout=subprocess.PIPE		,     bufsize=_SCREAMING_SNAKE_CASE   ) as ffmpeg_process:
      while True:
        a__:     List[Any]										=	ffmpeg_process.stdout.read(_SCREAMING_SNAKE_CASE   )
        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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""decision_transformer"""
      a__        =		["""past_key_values"""]
      a__        =		{
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }



      def __init__(						self    ,			lowercase=17    ,			lowercase=4    ,			lowercase=1_28    ,			lowercase=40_96    ,			lowercase=True    ,			lowercase=1    ,			lowercase=10_24    ,			lowercase=3    ,			lowercase=1    ,			lowercase=None    ,			lowercase="relu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=1e-5    ,			lowercase=0.02    ,			lowercase=True    ,			lowercase=True    ,			lowercase=5_02_56    ,			lowercase=5_02_56    ,			lowercase=False    ,			lowercase=False    ,			**lowercase    ,			)	->			Tuple:





        '''simple docstring'''





        a__:     List[str]										=	state_dim
        a__:     int										=	act_dim
        a__:     List[Any]										=	hidden_size
        a__:     List[str]										=	max_ep_len
        a__:     List[Any]										=	action_tanh
        a__:     Optional[Any]										=	vocab_size
        a__:     Tuple										=	n_positions
        a__:     Dict										=	n_layer
        a__:     Optional[int]										=	n_head
        a__:     Optional[int]										=	n_inner
        a__:     Any										=	activation_function
        a__:     Union[str, Any]										=	resid_pdrop
        a__:     Any										=	embd_pdrop
        a__:     Any										=	attn_pdrop
        a__:     List[Any]										=	layer_norm_epsilon
        a__:     Optional[Any]										=	initializer_range
        a__:     Any										=	scale_attn_weights
        a__:     Dict										=	use_cache
        a__:     Optional[int]										=	scale_attn_by_inverse_layer_idx
        a__:     List[str]										=	reorder_and_upcast_attn

        a__:     Any										=	bos_token_id
        a__:     int										=	eos_token_id

        super().__init__(bos_token_id=lowercase    ,			eos_token_id=lowercase    ,			**lowercase)


