


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

import numpy as np






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					F'''{sampling_rate}'''
			SCREAMING_SNAKE_CASE_		:     str					=					'1'
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'f32le'
			SCREAMING_SNAKE_CASE_		:     str					=					[
			    '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:
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					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
			SCREAMING_SNAKE_CASE_		:     Tuple					=					output_stream[0]
			SCREAMING_SNAKE_CASE_		:     str					=					np.frombuffer(A__,      np.floataa				)
			if audio.shape[0] == 0:
						raise ValueError('Malformed soundfile'				)
			return audio






def 						a__			(				A__,      A__,      A__ = "f32le",      ):
			SCREAMING_SNAKE_CASE_		:     int					=					F'''{sampling_rate}'''
			SCREAMING_SNAKE_CASE_		:     Any					=					'1'
			if format_for_conversion == "s16le":
						SCREAMING_SNAKE_CASE_		:     Tuple					=					2
			elif format_for_conversion == "f32le":
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					4
			else:
						raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''				)

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

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






def 						a__			(				A__,      A__,      A__ = None,      A__ = None,      A__ = "f32le",      ):
			if stream_chunk_s is not None:
						SCREAMING_SNAKE_CASE_		:     int					=					stream_chunk_s
			else:
						SCREAMING_SNAKE_CASE_		:     Any					=					chunk_length_s

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

			if stride_length_s is None:
						SCREAMING_SNAKE_CASE_		:     Any					=					chunk_length_s / 6
			SCREAMING_SNAKE_CASE_		:     Any					=					int(round(sampling_rate * chunk_length_s				)				) * size_of_sample
			if isinstance(A__,      (int, float)				):
						SCREAMING_SNAKE_CASE_		:     Dict					=					[stride_length_s, stride_length_s]

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






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






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					2**2_4  # 16Mo
			try:
						with subprocess.Popen(A__,      stdout=subprocess.PIPE,      bufsize=A__				) as ffmpeg_process:
									while True:
												SCREAMING_SNAKE_CASE_		:     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'''

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							Union[str, Any]=False)			->					Optional[int]:
   lowerCAmelCase_			=      OmegaConf.load(__snake_case)
   if display:
      print(yaml.dump(OmegaConf.to_container(__snake_case)))
   return config



def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]=None	,   __snake_case  :							str=None)			->					str:
   if conf_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.yaml'''
   lowerCAmelCase_			=      load_config(__snake_case	,   display=__snake_case)
   lowerCAmelCase_			=      VQModel(**config.model.params)
   if ckpt_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.pt'''
   lowerCAmelCase_			=      torch.load(__snake_case	,   map_location=__snake_case)
   if ".ckpt" in ckpt_path:
      lowerCAmelCase_			=      sd['''state_dict''']
   model.load_state_dict(__snake_case	,   strict=__snake_case)
   model.to(__snake_case)
   del sd
   return model



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							str)			->					Optional[int]:
   lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      model.encode(__snake_case)
   print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''')
   lowerCAmelCase_			=      model.decode(__snake_case)
   return xrec



def snake_case_      (					__snake_case  :							int	,   __snake_case  :							List[Any]=False)			->					Union[str, Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      string.rsplit('''.'''	,   1)
   if reload:
      lowerCAmelCase_			=      importlib.import_module(__snake_case)
      importlib.reload(__snake_case)
   return getattr(importlib.import_module(__snake_case	,   package=__snake_case)	,   cls)



def snake_case_      (					__snake_case  :							Tuple)			->					Union[str, Any]:
   if "target" not in config:
      raise KeyError('''Expected key `target` to instantiate.''')
   return get_obj_from_str(config['''target'''])(**config.get('''params'''	,   {}))



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Any	,   __snake_case  :							Optional[int]=True	,   __snake_case  :							Any=True)			->					List[str]:
   lowerCAmelCase_			=      instantiate_from_config(__snake_case)
   if sd is not None:
      model.load_state_dict(__snake_case)
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}






def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]	,   __snake_case  :							Dict	,   __snake_case  :							str)			->					Any:
   # load the specified checkpoint
   if ckpt:
      lowerCAmelCase_			=      torch.load(__snake_case	,   map_location='''cpu''')
      lowerCAmelCase_			=      pl_sd['''global_step''']
      print(F'''loaded model from global step {global_step}.''')
   else:
      lowerCAmelCase_			=      {'''state_dict''': None}
      lowerCAmelCase_			=      None
   lowerCAmelCase_			=      load_model_from_config(config.model	,   pl_sd['''state_dict''']	,   gpu=__snake_case	,   eval_mode=__snake_case)['''model''']
   return model, global_step





