






'''simple docstring'''

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

import numpy as np



def 							snake_case					(     a_							: bytes    ,   a_							: int	)       ->				np.array:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							f"{sampling_rate}"
      UpperCamelCase_				:   Union[str, Any]					    =							"""1"""
      UpperCamelCase_				:   Any					    =							"""f32le"""
      UpperCamelCase_				:   Optional[Any]					    =							[
          """ffmpeg""",
          """-i""",
          """pipe:0""",
          """-ac""",
          ac,
          """-ar""",
          ar,
          """-f""",
          format_for_conversion,
          """-hide_banner""",
          """-loglevel""",
          """quiet""",
          """pipe:1""",
      ]

      try:
            with subprocess.Popen(a_    ,   stdin=subprocess.PIPE    ,   stdout=subprocess.PIPE	) as ffmpeg_process:
                  UpperCamelCase_				:   Any					    =							ffmpeg_process.communicate(a_	)
      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_				:   Any					    =							np.frombuffer(a_    ,   np.floataa	)
      if audio.shape[0] == 0:
            raise ValueError("""Malformed soundfile"""	)
      return audio



def 							snake_case					(     a_							: int    ,   a_							: float    ,   a_							: str = "f32le"    ,   )       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   Union[str, Any]					    =							f"{sampling_rate}"
      UpperCamelCase_				:   Dict					    =							"""1"""
      if format_for_conversion == "s16le":
            UpperCamelCase_				:   int					    =							2
      elif format_for_conversion == "f32le":
            UpperCamelCase_				:   Union[str, Any]					    =							4
      else:
            raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"	)

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

      UpperCamelCase_				:   Any					    =							[
          """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_				:   Tuple					    =							_ffmpeg_stream(a_    ,   a_	)
      for item in iterator:
            yield item



def 							snake_case					(     a_							: int    ,   a_							: float    ,   a_							: Optional[int] = None    ,   a_							: Optional[Union[Tuple[float, float], float]] = None    ,   a_							: str = "f32le"    ,   )       ->				Tuple:





      """simple docstring"""

      if stream_chunk_s is not None:
            UpperCamelCase_				:   Union[str, Any]					    =							stream_chunk_s
      else:
            UpperCamelCase_				:   Optional[int]					    =							chunk_length_s

      UpperCamelCase_				:   Optional[Any]					    =							ffmpeg_microphone(a_    ,   a_    ,   format_for_conversion=a_	)
      if format_for_conversion == "s16le":
            UpperCamelCase_				:   Union[str, Any]					    =							np.intaa
            UpperCamelCase_				:   Tuple					    =							2
      elif format_for_conversion == "f32le":
            UpperCamelCase_				:   List[Any]					    =							np.floataa
            UpperCamelCase_				:   Optional[Any]					    =							4
      else:
            raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"	)

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

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



def 							snake_case					(     a_							: List[str]    ,   a_							: int    ,   a_							: Tuple[int, int]    ,   a_							: bool = False	)       ->				List[Any]:





      """simple docstring"""

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



def 							snake_case					(     a_							: Union[str, Any]    ,   a_							: int	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   str					    =							2**24  # 16Mo
      try:
            with subprocess.Popen(a_    ,   stdout=subprocess.PIPE    ,   bufsize=a_	) as ffmpeg_process:
                  while True:
                        UpperCamelCase_				:   str					    =							ffmpeg_process.stdout.read(a_	)
                        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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase							={
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase							=[
          "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecAudioForAudioFrameClassification",
          "Data2VecAudioForCTC",
          "Data2VecAudioForSequenceClassification",
          "Data2VecAudioForXVector",
          "Data2VecAudioModel",
          "Data2VecAudioPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecTextForCausalLM",
          "Data2VecTextForMaskedLM",
          "Data2VecTextForMultipleChoice",
          "Data2VecTextForQuestionAnswering",
          "Data2VecTextForSequenceClassification",
          "Data2VecTextForTokenClassification",
          "Data2VecTextModel",
          "Data2VecTextPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecVisionForImageClassification",
          "Data2VecVisionForMaskedImageModeling",
          "Data2VecVisionForSemanticSegmentation",
          "Data2VecVisionModel",
          "Data2VecVisionPreTrainedModel",
      ]

if is_tf_available():
      UpperCamelCase							=[
          "TFData2VecVisionForImageClassification",
          "TFData2VecVisionForSemanticSegmentation",
          "TFData2VecVisionModel",
          "TFData2VecVisionPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
      from .configuration_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecTextConfig,
          DataaVecTextOnnxConfig,
      )
      from .configuration_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecVisionConfig,
          DataaVecVisionOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dataavec_audio import (
                DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecAudioForAudioFrameClassification,
                DataaVecAudioForCTC,
                DataaVecAudioForSequenceClassification,
                DataaVecAudioForXVector,
                DataaVecAudioModel,
                DataaVecAudioPreTrainedModel,
            )
            from .modeling_dataavec_text import (
                DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecTextForCausalLM,
                DataaVecTextForMaskedLM,
                DataaVecTextForMultipleChoice,
                DataaVecTextForQuestionAnswering,
                DataaVecTextForSequenceClassification,
                DataaVecTextForTokenClassification,
                DataaVecTextModel,
                DataaVecTextPreTrainedModel,
            )
            from .modeling_dataavec_vision import (
                DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecVisionForImageClassification,
                DataaVecVisionForMaskedImageModeling,
                DataaVecVisionForSemanticSegmentation,
                DataaVecVisionModel,
                DataaVecVisionPreTrainedModel,
            )
      if is_tf_available():
            from .modeling_tf_dataavec_vision import (
                TFDataaVecVisionForImageClassification,
                TFDataaVecVisionForSemanticSegmentation,
                TFDataaVecVisionModel,
                TFDataaVecVisionPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






