



"""simple docstring"""







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

import numpy as np

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							np.array:
       A         =					f"""{sampling_rate}"""
       A         =					"""1"""
       A         =					"""f32le"""
       A         =					[
           """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:
                     A         =					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
       A         =					output_stream[0]
       A         =					np.frombuffer(UpperCamelCase__				,      np.floataa			)
       if audio.shape[0] == 0:
              raise ValueError('Malformed soundfile'			)
       return audio

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = "f32le"				,      ) ->							Dict:
       A         =					f"""{sampling_rate}"""
       A         =					"""1"""
       if format_for_conversion == "s16le":
              A         =					2
       elif format_for_conversion == "f32le":
              A         =					4
       else:
              raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""			)

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

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

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "f32le"				,      ) ->							Any:
       if stream_chunk_s is not None:
              A         =					stream_chunk_s
       else:
              A         =					chunk_length_s

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

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

       A         =					int(round(sampling_rate * stride_length_s[0]			)			) * size_of_sample
       A         =					int(round(sampling_rate * stride_length_s[1]			)			) * size_of_sample
       A         =					datetime.datetime.now()
       A         =					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
              A         =					np.frombuffer(item['raw']				,      dtype=UpperCamelCase__			)
              A         =					(
                  item["""stride"""][0] // size_of_sample,
                  item["""stride"""][1] // size_of_sample,
              )
              A         =					sampling_rate
              audio_time += delta
              if datetime.datetime.now() > audio_time + 10 * delta:
                     # We're late !! SKIP
                     continue
              yield item

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = False			) ->							Optional[Any]:
       A         =					B""""""
       A         =					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         =					0
       for raw in iterator:
              acc += raw
              if stream and len(UpperCamelCase__			) < chunk_len:
                     A         =					(_stride_left, 0)
                     yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
              else:
                     while len(UpperCamelCase__			) >= chunk_len:
                            # We are flushing the accumulator
                            A         =					(_stride_left, stride_right)
                            A         =					{"""raw""": acc[:chunk_len], """stride""": stride}
                            if stream:
                                   A         =					False
                            yield item
                            A         =					stride_left
                            A         =					acc[chunk_len - stride_left - stride_right :]
    # Last chunk
       if len(UpperCamelCase__			) > stride_left:
              A         =					{"""raw""": acc, """stride""": (_stride_left, 0)}
              if stream:
                     A         =					False
              yield item

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							List[Any]:
       A         =					2**24  # 16Mo
       try:
              with subprocess.Popen(UpperCamelCase__				,      stdout=subprocess.PIPE				,      bufsize=UpperCamelCase__			) as ffmpeg_process:
                     while True:
                            A         =					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








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A    =			{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			['PLBartTokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'PLBartForCausalLM',
							    'PLBartForConditionalGeneration',
							    'PLBartForSequenceClassification',
							    'PLBartModel',
							    'PLBartPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_plbart import PLBartTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_plbart import (
														    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PLBartForCausalLM,
														    PLBartForConditionalGeneration,
														    PLBartForSequenceClassification,
														    PLBartModel,
														    PLBartPreTrainedModel,
														)


else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure)



