





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

import numpy as np

def   a_   (			lowerCAmelCase_				:      bytes,     lowerCAmelCase_				:      int     ):
   __lowerCAmelCase							     =     F"""{sampling_rate}"""
   __lowerCAmelCase							     =     '1'
   __lowerCAmelCase							     =     'f32le'
   __lowerCAmelCase							     =     [
       '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:
         __lowerCAmelCase							     =     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
   __lowerCAmelCase							     =     output_stream[0]
   __lowerCAmelCase							     =     np.frombuffer(lowerCAmelCase_,     np.floataa     )
   if audio.shape[0] == 0:
      raise ValueError('Malformed soundfile'     )
   return audio

def   a_   (			lowerCAmelCase_				:      int,     lowerCAmelCase_				:      float,     lowerCAmelCase_				:      str = "f32le",     ):
   __lowerCAmelCase							     =     F"""{sampling_rate}"""
   __lowerCAmelCase							     =     '1'
   if format_for_conversion == "s16le":
      __lowerCAmelCase							     =     2
   elif format_for_conversion == "f32le":
      __lowerCAmelCase							     =     4
   else:
      raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""     )

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

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

def   a_   (			lowerCAmelCase_				:      int,     lowerCAmelCase_				:      float,     lowerCAmelCase_				:      Optional[int] = None,     lowerCAmelCase_				:      Optional[Union[Tuple[float, float], float]] = None,     lowerCAmelCase_				:      str = "f32le",     ):
   if stream_chunk_s is not None:
      __lowerCAmelCase							     =     stream_chunk_s
   else:
      __lowerCAmelCase							     =     chunk_length_s

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

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

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

def   a_   (			lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Tuple[int, int],     lowerCAmelCase_				:      bool = False     ):
   __lowerCAmelCase							     =     B''
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     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}"""     )
   __lowerCAmelCase							     =     0
   for raw in iterator:
      acc += raw
      if stream and len(lowerCAmelCase_     ) < chunk_len:
         __lowerCAmelCase							     =     (_stride_left, 0)
         yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
      else:
         while len(lowerCAmelCase_     ) >= chunk_len:
            # We are flushing the accumulator
            __lowerCAmelCase							     =     (_stride_left, stride_right)
            __lowerCAmelCase							     =     {'raw': acc[:chunk_len], 'stride': stride}
            if stream:
               __lowerCAmelCase							     =     False
            yield item
            __lowerCAmelCase							     =     stride_left
            __lowerCAmelCase							     =     acc[chunk_len - stride_left - stride_right :]
    # Last chunk
   if len(lowerCAmelCase_     ) > stride_left:
      __lowerCAmelCase							     =     {'raw': acc, 'stride': (_stride_left, 0)}
      if stream:
         __lowerCAmelCase							     =     False
      yield item




def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      int     ):
   __lowerCAmelCase							     =     2**24  # 16Mo
   try:
      with subprocess.Popen(lowerCAmelCase_,     stdout=subprocess.PIPE,     bufsize=lowerCAmelCase_     ) as ffmpeg_process:
         while True:
            __lowerCAmelCase							     =     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








import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort



@nightly
@require_onnxruntime
@require_torch_gpu
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )






  @property
  def     lowercase		(    self			:		List[Any]					)							->						int:
     __lowerCAmelCase							     =     ort.SessionOptions()
     __lowerCAmelCase							     =     False
     return options






  def     lowercase		(    self			:		Tuple					)							->						List[Any]:
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'					)
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'					)
     __lowerCAmelCase							     =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'					)

     # using the PNDM scheduler by default
     __lowerCAmelCase							     =     OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='onnx' ,  safety_checker=lowerCAmelCase_ ,  feature_extractor=lowerCAmelCase_ ,  provider=self.gpu_provider ,  sess_options=self.gpu_options ,  )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     'A red cat sitting on a park bench'

     __lowerCAmelCase							     =     np.random.RandomState(0					)
     __lowerCAmelCase							     =     pipe(
         prompt=lowerCAmelCase_ ,  image=lowerCAmelCase_ ,  mask_image=lowerCAmelCase_ ,  strength=0.75 ,  guidance_scale=7.5 ,  num_inference_steps=1_5 ,  generator=lowerCAmelCase_ ,  output_type='np' ,  )

     __lowerCAmelCase							     =     output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image					).max() < 1e-2

