






from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


A_		:		List[Any]		     =			logging.get_logger(__name__)  # pylint: disable=invalid-name




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         super().__init__()

         if hasattr(scheduler.config  ,      'steps_offset'							) and scheduler.config.steps_offset != 1:
           UpperCamelCase_: List[str]   				=    (
               f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
               f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
               'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
               ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
               ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
               ' file'
           )
           deprecate('steps_offset!=1'  ,      '1.0.0'  ,      _lowerCamelCase  ,      standard_warn=_lowerCamelCase							)
           UpperCamelCase_: Tuple   				=    dict(scheduler.config							)
           UpperCamelCase_: Dict   				=    1
           UpperCamelCase_: List[Any]   				=    FrozenDict(_lowerCamelCase							)

         if hasattr(scheduler.config  ,      'skip_prk_steps'							) and scheduler.config.skip_prk_steps is False:
           UpperCamelCase_: Union[str, Any]   				=    (
               f'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
               ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
               ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
               ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
               ' Hub, it would be very nice if you could open a Pull request for the'
               ' `scheduler/scheduler_config.json` file'
           )
           deprecate('skip_prk_steps not set'  ,      '1.0.0'  ,      _lowerCamelCase  ,      standard_warn=_lowerCamelCase							)
           UpperCamelCase_: Union[str, Any]   				=    dict(scheduler.config							)
           UpperCamelCase_: Tuple   				=    True
           UpperCamelCase_: Optional[Any]   				=    FrozenDict(_lowerCamelCase							)

         if safety_checker is None:
           logger.warning(
               f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
               ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
               ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
               ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
               ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
               ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'							)

         self.register_modules(
             segmentation_model=_lowerCamelCase  ,      segmentation_processor=_lowerCamelCase  ,      vae=_lowerCamelCase  ,      text_encoder=_lowerCamelCase  ,      tokenizer=_lowerCamelCase  ,      unet=_lowerCamelCase  ,      scheduler=_lowerCamelCase  ,      safety_checker=_lowerCamelCase  ,      feature_extractor=_lowerCamelCase  ,      )





       def       _a   ( self  ,      _lowerCamelCase = "auto"							):
         if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           UpperCamelCase_: Union[str, Any]   				=    self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(_lowerCamelCase							)





       def       _a   ( self							):
         self.enable_attention_slicing(_lowerCamelCase							)





       def       _a   ( self							):
         if is_accelerate_available():
           from accelerate import cpu_offload
         else:
           raise ImportError('Please install accelerate via `pip install accelerate`'							)

         UpperCamelCase_: Optional[Any]   				=    torch.device('cuda'							)

         for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
           if cpu_offloaded_model is not None:
             cpu_offload(_lowerCamelCase  ,      _lowerCamelCase							)





       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def       _a   ( self							):
         if self.device != torch.device('meta'							) or not hasattr(self.unet  ,      '_hf_hook'							):
           return self.device
         for module in self.unet.modules():
           if (
               hasattr(_lowerCamelCase  ,      '_hf_hook'							)
               and hasattr(module._hf_hook  ,      'execution_device'							)
               and module._hf_hook.execution_device is not None
           ):
             return torch.device(module._hf_hook.execution_device							)
         return self.device





       @torch.no_grad()
       def __call__( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = 5_1_2  ,      _lowerCamelCase = 5_1_2  ,      _lowerCamelCase = 5_0  ,      _lowerCamelCase = 7.5  ,      _lowerCamelCase = None  ,      _lowerCamelCase = 1  ,      _lowerCamelCase = 0.0  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = "pil"  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = 1  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[int]   				=    self.segmentation_processor(
             text=[text]  ,      images=[image]  ,      padding='max_length'  ,      return_tensors='pt'							).to(self.device							)
         UpperCamelCase_: Union[str, Any]   				=    self.segmentation_model(**_lowerCamelCase							)
         UpperCamelCase_: int   				=    torch.sigmoid(outputs.logits							).cpu().detach().unsqueeze(-1							).numpy()
         UpperCamelCase_: Dict   				=    self.numpy_to_pil(_lowerCamelCase							)[0].resize(image.size							)

         # Run inpainting pipeline with the generated mask
         UpperCamelCase_: int   				=    StableDiffusionInpaintPipeline(
             vae=self.vae  ,      text_encoder=self.text_encoder  ,      tokenizer=self.tokenizer  ,      unet=self.unet  ,      scheduler=self.scheduler  ,      safety_checker=self.safety_checker  ,      feature_extractor=self.feature_extractor  ,      )
         return inpainting_pipeline(
             prompt=_lowerCamelCase  ,      image=_lowerCamelCase  ,      mask_image=_lowerCamelCase  ,      height=_lowerCamelCase  ,      width=_lowerCamelCase  ,      num_inference_steps=_lowerCamelCase  ,      guidance_scale=_lowerCamelCase  ,      negative_prompt=_lowerCamelCase  ,      num_images_per_prompt=_lowerCamelCase  ,      eta=_lowerCamelCase  ,      generator=_lowerCamelCase  ,      latents=_lowerCamelCase  ,      output_type=_lowerCamelCase  ,      return_dict=_lowerCamelCase  ,      callback=_lowerCamelCase  ,      callback_steps=_lowerCamelCase  ,      )







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

import numpy as np

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.array:
  UpperCamelCase_: Dict   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Any   				=    '1'
  UpperCamelCase_: Any   				=    'f32le'
  UpperCamelCase_: Union[str, Any]   				=    [
      '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_: Optional[Any]   				=    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_: Union[str, Any]   				=    output_stream[0]
  UpperCamelCase_: List[str]   				=    np.frombuffer(UpperCAmelCase__     ,   np.floataa  )
  if audio.shape[0] == 0:
    raise ValueError('Malformed soundfile'  )
  return audio

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Tuple:
  UpperCamelCase_: Any   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Union[str, Any]   				=    '1'
  if format_for_conversion == "s16le":
    UpperCamelCase_: Optional[Any]   				=    2
  elif format_for_conversion == "f32le":
    UpperCamelCase_: Any   				=    4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

  UpperCamelCase_: int   				=    platform.system()
  if system == "Linux":
    UpperCamelCase_: Tuple   				=    'alsa'
    UpperCamelCase_: List[str]   				=    'default'
  elif system == "Darwin":
    UpperCamelCase_: int   				=    'avfoundation'
    UpperCamelCase_: Union[str, Any]   				=    ':0'
  elif system == "Windows":
    UpperCamelCase_: Tuple   				=    'dshow'
    UpperCamelCase_: Dict   				=    '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_: Tuple   				=    int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
  UpperCamelCase_: Optional[int]   				=    _ffmpeg_stream(UpperCAmelCase__     ,   UpperCAmelCase__  )
  for item in iterator:
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Any:
  if stream_chunk_s is not None:
    UpperCamelCase_: List[Any]   				=    stream_chunk_s
  else:
    UpperCamelCase_: Dict   				=    chunk_length_s

  UpperCamelCase_: List[str]   				=    ffmpeg_microphone(UpperCAmelCase__     ,   UpperCAmelCase__     ,   format_for_conversion=UpperCAmelCase__  )
  if format_for_conversion == "s16le":
    UpperCamelCase_: Union[str, Any]   				=    np.intaa
    UpperCamelCase_: List[Any]   				=    2
  elif format_for_conversion == "f32le":
    UpperCamelCase_: str   				=    np.floataa
    UpperCamelCase_: Tuple   				=    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_: Tuple   				=    int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
  if isinstance(UpperCAmelCase__     ,   (int, float)  ):
    UpperCamelCase_: Union[str, Any]   				=    [stride_length_s, stride_length_s]

  UpperCamelCase_: Any   				=    int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
  UpperCamelCase_: Dict   				=    int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
  UpperCamelCase_: Optional[int]   				=    datetime.datetime.now()
  UpperCamelCase_: Optional[int]   				=    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_: Tuple   				=    np.frombuffer(item['raw']     ,   dtype=UpperCAmelCase__  )
    UpperCamelCase_: Optional[int]   				=    (
        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 + 1_0 * delta:
      # We're late !! SKIP
      continue
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = False  )	->    int:
  UpperCamelCase_: str   				=    b''
  UpperCamelCase_				,UpperCamelCase_: Union[str, 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_: List[str]   				=    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_: int   				=    (_stride_left, stride_right)
        UpperCamelCase_: Optional[Any]   				=    {'raw': acc[:chunk_len], 'stride': stride}
        if stream:
          UpperCamelCase_: Any   				=    False
        yield item
        UpperCamelCase_: Optional[int]   				=    stride_left
        UpperCamelCase_: Optional[Any]   				=    acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(UpperCAmelCase__  ) > stride_left:
    UpperCamelCase_: int   				=    {'raw': acc, 'stride': (_stride_left, 0)}
    if stream:
      UpperCamelCase_: Optional[Any]   				=    False
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Any   				=    2**2_4  # 16Mo
  try:
    with subprocess.Popen(UpperCAmelCase__     ,   stdout=subprocess.PIPE     ,   bufsize=UpperCAmelCase__  ) as ffmpeg_process:
      while True:
        UpperCamelCase_: 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