



"""simple docstring"""
from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
      import timm


if is_torch_available():
      from torch import Tensor



class    SCREAMING_SNAKE_CASE__						(      __snake_case   ,    __snake_case				):
   __lowerCAmelCase						:     Optional[int]		=		'pixel_values'
   __lowerCAmelCase						:     Any		=		False
   __lowerCAmelCase						:     Optional[Any]		=		TimmBackboneConfig

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    requires_backends(self   ,  """timm"""		)
    super().__init__(__UpperCamelCase		)
    UpperCAmelCase			:      Optional[Any]			=			config

    if config.backbone is None:
     raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""		)

    if config.backbone not in timm.list_models():
     raise ValueError(F"backbone {config.backbone} is not supported by timm."		)

    if hasattr(__UpperCamelCase   ,  """out_features"""		) and config.out_features is not None:
     raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""		)

    UpperCAmelCase			:      Union[str, Any]			=			getattr(__UpperCamelCase   ,  """use_pretrained_backbone"""   ,  __UpperCamelCase		)
    if pretrained is None:
     raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""		)

    # We just take the final layer by default. This matches the default for the transformers models.
    UpperCAmelCase			:      Union[str, Any]			=			config.out_indices if getattr(__UpperCamelCase   ,  """out_indices"""   ,  __UpperCamelCase		) is not None else (-1,)

    UpperCAmelCase			:      str			=			timm.create_model(
        config.backbone   ,  pretrained=__UpperCamelCase   ,  features_only=config.features_only   ,  in_chans=config.num_channels   ,  out_indices=__UpperCamelCase   ,  **__UpperCamelCase   ,  )
    # These are used to control the output of the model when called. If output_hidden_states is True, then
    # return_layers is modified to include all layers.
    UpperCAmelCase			:      str			=			self._backbone.return_layers
    UpperCAmelCase			:      Union[str, Any]			=			{layer["""module"""]: str(__UpperCamelCase		) for i, layer in enumerate(self._backbone.feature_info.info		)}
    super()._init_backbone(__UpperCamelCase		)

   @classmethod
   def 			SCREAMING_SNAKE_CASE  (     cls   ,  _SCREAMING_SNAKE_CASE   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    requires_backends(cls   ,  ["""vision""", """timm"""]		)
    from ...models.timm_backbone import TimmBackboneConfig

    UpperCAmelCase			:      List[str]			=			kwargs.pop("""config"""   ,  TimmBackboneConfig()		)

    UpperCAmelCase			:      Any			=			kwargs.pop("""use_timm_backbone"""   ,  __UpperCamelCase		)
    if not use_timm:
     raise ValueError("""use_timm_backbone must be True for timm backbones"""		)

    UpperCAmelCase			:      Optional[int]			=			kwargs.pop("""num_channels"""   ,  config.num_channels		)
    UpperCAmelCase			:      int			=			kwargs.pop("""features_only"""   ,  config.features_only		)
    UpperCAmelCase			:      List[str]			=			kwargs.pop("""use_pretrained_backbone"""   ,  config.use_pretrained_backbone		)
    UpperCAmelCase			:      Any			=			kwargs.pop("""out_indices"""   ,  config.out_indices		)
    UpperCAmelCase			:      str			=			TimmBackboneConfig(
        backbone=__UpperCamelCase   ,  num_channels=__UpperCamelCase   ,  features_only=__UpperCamelCase   ,  use_pretrained_backbone=__UpperCamelCase   ,  out_indices=__UpperCamelCase   ,  )
    return super()._from_config(__UpperCamelCase   ,  **__UpperCamelCase		)

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





    '''simple docstring'''




    pass




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  **_SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			return_dict if return_dict is not None else self.config.use_return_dict
    UpperCAmelCase			:      Optional[Any]			=			(
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    UpperCAmelCase			:      List[str]			=			output_attentions if output_attentions is not None else self.config.output_attentions

    if output_attentions:
     raise ValueError("""Cannot output attentions for timm backbones at the moment"""		)

    if output_hidden_states:
     # We modify the return layers to include all the stages of the backbone
     UpperCAmelCase			:      Any			=			self._all_layers
     UpperCAmelCase			:      List[str]			=			self._backbone(__UpperCamelCase   ,  **__UpperCamelCase		)
     UpperCAmelCase			:      int			=			self._return_layers
     UpperCAmelCase			:      List[Any]			=			tuple(hidden_states[i] for i in self.out_indices		)
    else:
     UpperCAmelCase			:      Union[str, Any]			=			self._backbone(__UpperCamelCase   ,  **__UpperCamelCase		)
     UpperCAmelCase			:      Optional[Any]			=			None

    UpperCAmelCase			:      List[str]			=			tuple(__UpperCamelCase		)
    UpperCAmelCase			:      Optional[int]			=			tuple(__UpperCamelCase		) if hidden_states is not None else None

    if not return_dict:
     UpperCAmelCase			:      Union[str, Any]			=			(feature_maps,)
     if output_hidden_states:
      UpperCAmelCase			:      List[Any]			=			output + (hidden_states,)
     return output

    return BackboneOutput(feature_maps=__UpperCamelCase   ,  hidden_states=__UpperCamelCase   ,  attentions=__UpperCamelCase		)










"""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
