





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

import numpy as np



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Any			      =	f"""{sampling_rate}"""
   A_     :				int			      =	"""1"""
   A_     :				Optional[Any]			      =	"""f32le"""
   A_     :				Dict			      =	[
       """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:
         A_     :				Union[str, Any]			      =	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
   A_     :				List[str]			      =	output_stream[0]
   A_     :				Any			      =	np.frombuffer(_lowerCAmelCase  ,np.floataa     )
   if audio.shape[0] == 0:
      raise ValueError("""Malformed soundfile"""     )
   return audio



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase = "f32le"  ,):
   '''simple docstring'''


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

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

   A_     :				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_     :				Optional[Any]			      =	_ffmpeg_stream(_lowerCAmelCase  ,_lowerCAmelCase     )
   for item in iterator:
      yield item



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase = None  ,_lowerCAmelCase = None  ,_lowerCAmelCase = "f32le"  ,):
   '''simple docstring'''


   if stream_chunk_s is not None:
      A_     :				List[str]			      =	stream_chunk_s
   else:
      A_     :				Tuple			      =	chunk_length_s

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

   if stride_length_s is None:
      A_     :				Optional[Any]			      =	chunk_length_s / 6
   A_     :				str			      =	int(round(sampling_rate * chunk_length_s     )     ) * size_of_sample
   if isinstance(_lowerCAmelCase  ,(int, float)     ):
      A_     :				Optional[Any]			      =	[stride_length_s, stride_length_s]

   A_     :				Tuple			      =	int(round(sampling_rate * stride_length_s[0]     )     ) * size_of_sample
   A_     :				Optional[Any]			      =	int(round(sampling_rate * stride_length_s[1]     )     ) * size_of_sample
   A_     :				Any			      =	datetime.datetime.now()
   A_     :				List[Any]			      =	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
      A_     :				Any			      =	np.frombuffer(item["""raw"""]  ,dtype=_lowerCAmelCase     )
      A_     :				Union[str, Any]			      =	(
          item["""stride"""][0] // size_of_sample,
          item["""stride"""][1] // size_of_sample,
      )
      A_     :				List[str]			      =	sampling_rate
      audio_time += delta
      if datetime.datetime.now() > audio_time + 1_0 * delta:
         # We're late !! SKIP
         continue
      yield item



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase = False     ):
   '''simple docstring'''


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







def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Optional[int]			      =	2**2_4  # 16Mo
   try:
      with subprocess.Popen(_lowerCAmelCase  ,stdout=subprocess.PIPE  ,bufsize=_lowerCAmelCase     ) as ffmpeg_process:
         while True:
            A_     :				Any			      =	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













# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


_lowerCAmelCase 				=     """3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
      import torch

      print("""Torch version:""", torch.__version__)
      print("""Cuda available:""", torch.cuda.is_available())
      print("""Cuda version:""", torch.version.cuda)
      print("""CuDNN version:""", torch.backends.cudnn.version())
      print("""Number of GPUs available:""", torch.cuda.device_count())
      print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
      print("""Torch version:""", None)

try:
      import deepspeed

      print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
      print("""DeepSpeed version:""", None)

try:
      import tensorflow as tf

      print("""TensorFlow version:""", tf.__version__)
      print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
      print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
      print("""TensorFlow version:""", None)






