'''simple docstring'''





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

import numpy as np





def      lowerCAmelCase_    ( snake_case_ ,   snake_case_      )   ->				np.array:


     '''simple docstring'''




     UpperCAmelCase_	   =       f"""{sampling_rate}"""
     UpperCAmelCase_	   =       "1"
     UpperCAmelCase_	   =       "f32le"
     UpperCAmelCase_	   =       [
         "ffmpeg",
         "-i",
         "pipe:0",
         "-ac",
         ac,
         "-ar",
         ar,
         "-f",
         format_for_conversion,
         "-hide_banner",
         "-loglevel",
         "quiet",
         "pipe:1",
     ]

     try:
          with subprocess.Popen(snake_case_ ,   stdin=subprocess.PIPE ,   stdout=subprocess.PIPE      ) as ffmpeg_process:
               UpperCAmelCase_	   =       ffmpeg_process.communicate(snake_case_      )
     except FileNotFoundError as error:
          raise ValueError("ffmpeg was not found but is required to load audio files from filename"      ) from error
     UpperCAmelCase_	   =       output_stream[0]
     UpperCAmelCase_	   =       np.frombuffer(snake_case_ ,   np.floataa      )
     if audio.shape[0] == 0:
          raise ValueError("Malformed soundfile"      )
     return audio





def      lowerCAmelCase_    ( snake_case_ ,   snake_case_ ,   snake_case_ = "f32le" ,   )   ->				Union[str, Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       f"""{sampling_rate}"""
     UpperCAmelCase_	   =       "1"
     if format_for_conversion == "s16le":
          UpperCAmelCase_	   =       2
     elif format_for_conversion == "f32le":
          UpperCAmelCase_	   =       4
     else:
          raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""      )

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

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





def      lowerCAmelCase_    ( snake_case_ ,   snake_case_ ,   snake_case_ = None ,   snake_case_ = None ,   snake_case_ = "f32le" ,   )   ->				List[Any]:


     '''simple docstring'''




     if stream_chunk_s is not None:
          UpperCAmelCase_	   =       stream_chunk_s
     else:
          UpperCAmelCase_	   =       chunk_length_s

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

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

     UpperCAmelCase_	   =       int(round(sampling_rate * stride_length_s[0]      )      ) * size_of_sample
     UpperCAmelCase_	   =       int(round(sampling_rate * stride_length_s[1]      )      ) * size_of_sample
     UpperCAmelCase_	   =       datetime.datetime.now()
     UpperCAmelCase_	   =       datetime.timedelta(seconds=snake_case_      )
     for item in chunk_bytes_iter(snake_case_ ,   snake_case_ ,   stride=(stride_left, stride_right) ,   stream=snake_case_      ):
          # Put everything back in numpy scale
          UpperCAmelCase_	   =       np.frombuffer(item["raw"] ,   dtype=snake_case_      )
          UpperCAmelCase_	   =       (
              item["stride"][0] // size_of_sample,
              item["stride"][1] // size_of_sample,
          )
          UpperCAmelCase_	   =       sampling_rate
          audio_time += delta
          if datetime.datetime.now() > audio_time + 10 * delta:
               # We're late !! SKIP
               continue
          yield item





def      lowerCAmelCase_    ( snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ = False      )   ->				Tuple:


     '''simple docstring'''




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






def      lowerCAmelCase_    ( snake_case_ ,   snake_case_      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       2**24  # 16Mo
     try:
          with subprocess.Popen(snake_case_ ,   stdout=subprocess.PIPE ,   bufsize=snake_case_      ) as ffmpeg_process:
               while True:
                    UpperCAmelCase_	   =       ffmpeg_process.stdout.read(snake_case_      )
                    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 unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class 	__A				:



    @property
    def  _lowercase						(self   :   int			):
         return self.get_dummy_input()



    @property
    def  _lowercase						(self   :   Any			):
         if self.block_type == "down":
              return (4, 32, 16, 16)
         elif self.block_type == "mid":
              return (4, 32, 32, 32)
         elif self.block_type == "up":
              return (4, 32, 64, 64)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   List[str]=True       ,      __a   :   Any=False       ,      __a   :   List[Any]=False       ,      __a   :   Any=False       ,      ):
         UpperCAmelCase_	   =       4
         UpperCAmelCase_	   =       32
         UpperCAmelCase_	   =       (32, 32)

         UpperCAmelCase_	   =       torch.manual_seed(0			)
         UpperCAmelCase_	   =       torch.device(__a			)
         UpperCAmelCase_	   =       (batch_size, num_channels) + sizes
         UpperCAmelCase_	   =       randn_tensor(__a       ,      generator=__a       ,      device=__a			)
         UpperCAmelCase_	   =       {"hidden_states": hidden_states}

         if include_temb:
              UpperCAmelCase_	   =       128
              UpperCAmelCase_	   =       randn_tensor((batch_size, temb_channels)       ,      generator=__a       ,      device=__a			)

         if include_res_hidden_states_tuple:
              UpperCAmelCase_	   =       torch.manual_seed(1			)
              UpperCAmelCase_	   =       (randn_tensor(__a       ,      generator=__a       ,      device=__a			),)

         if include_encoder_hidden_states:
              UpperCAmelCase_	   =       floats_tensor((batch_size, 32, 32)			).to(__a			)

         if include_skip_sample:
              UpperCAmelCase_	   =       randn_tensor(((batch_size, 3) + sizes)       ,      generator=__a       ,      device=__a			)

         return dummy_input



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       {
             "in_channels": 32,
             "out_channels": 32,
             "temb_channels": 128,
         }
         if self.block_type == "up":
              UpperCAmelCase_	   =       32

         if self.block_type == "mid":
              init_dict.pop("out_channels"			)

         UpperCAmelCase_	   =       self.dummy_input
         return init_dict, inputs_dict



    def  _lowercase						(self   :   Tuple       ,      __a   :   Any			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase_	   =       self.block_class(**__a			)
         unet_block.to(__a			)
         unet_block.eval()

         with torch.no_grad():
              UpperCAmelCase_	   =       unet_block(**__a			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       output[0]

         self.assertEqual(output.shape       ,      self.output_shape			)

         UpperCAmelCase_	   =       output[0, -1, -3:, -3:]
         UpperCAmelCase_	   =       torch.tensor(__a			).to(__a			)
         assert torch_all_close(output_slice.flatten()       ,      __a       ,      atol=5E-3			)






    @unittest.skipIf(torch_device == "mps"       ,      "Training is not supported in mps"			)
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase_	   =       self.block_class(**__a			)
         model.to(__a			)
         model.train()
         UpperCAmelCase_	   =       model(**__a			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       output[0]

         UpperCAmelCase_	   =       torch.device(__a			)
         UpperCAmelCase_	   =       randn_tensor(output.shape       ,      device=__a			)
         UpperCAmelCase_	   =       torch.nn.functional.mse_loss(__a       ,      __a			)
         loss.backward()




