





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

import numpy as np





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







  """simple docstring"""





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





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a = "f32le"      ,       )	->						Optional[int]:







  """simple docstring"""





  lowerCamelCase__: Any         =F"""{sampling_rate}"""
  lowerCamelCase__: Dict         ="1"
  if format_for_conversion == "s16le":
    lowerCamelCase__: Tuple         =2
  elif format_for_conversion == "f32le":
    lowerCamelCase__: List[Any]         =4
  else:
    raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""						)

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

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





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a = None      ,       __a = None      ,       __a = "f32le"      ,       )	->						Tuple:







  """simple docstring"""





  if stream_chunk_s is not None:
    lowerCamelCase__: int         =stream_chunk_s
  else:
    lowerCamelCase__: int         =chunk_length_s

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

  if stride_length_s is None:
    lowerCamelCase__: str         =chunk_length_s / 6
  lowerCamelCase__: str         =int(round(sampling_rate * chunk_length_s						)						) * size_of_sample
  if isinstance(__a      ,       (int, float)						):
    lowerCamelCase__: List[str]         =[stride_length_s, stride_length_s]

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





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a      ,       __a = False						)	->						Union[str, Any]:







  """simple docstring"""





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






def 	lowerCAmelCase_					(			__a      ,       __a						)	->						Optional[int]:







  """simple docstring"""





  lowerCamelCase__: str         =2**24  # 16Mo
  try:
    with subprocess.Popen(__a      ,       stdout=subprocess.PIPE      ,       bufsize=__a						) as ffmpeg_process:
      while True:
        lowerCamelCase__: Optional[Any]         =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 gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		Dict:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	@property
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   1
		snake_case__	       =   3
		snake_case__	       =   (32, 32)

		snake_case__	       =   floats_tensor((batch_size, num_channels) + sizes      ,       rng=random.Random(0		)		).to(UpperCamelCase_		)
		return image





	@property
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		torch.manual_seed(0		)
		snake_case__	       =   UNetaDConditionModel(
		    block_out_channels=(32, 32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=7      ,       out_channels=4      ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')      ,       up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')      ,       cross_attention_dim=32      ,       attention_head_dim=8      ,       use_linear_projection=UpperCamelCase_      ,       only_cross_attention=(True, True, False)      ,       num_class_embeds=100      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		Optional[Any]:
		torch.manual_seed(0		)
		snake_case__	       =   AutoencoderKL(
		    block_out_channels=[32, 32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']      ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']      ,       latent_channels=4      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		List[str]:
		torch.manual_seed(0		)
		snake_case__	       =   CLIPTextConfig(
		    bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1000      ,       hidden_act='gelu'      ,       projection_dim=512      ,       )
		return CLIPTextModel(UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )

		snake_case__	       =   output.images

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       return_dict=UpperCamelCase_      ,       )[0]

		snake_case__	       =   image[0, -3:, -3:, -1]
		snake_case__	       =   image_from_tuple[0, -3:, -3:, -1]

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)
		snake_case__	       =   np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]		)

		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
		assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   sd_pipe(
		    2 * [prompt]      ,       image=2 * [low_res_image]      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_images_per_prompt=2      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2





	@unittest.skipIf(torch_device != 'cuda'      ,       'This test requires a GPU'		)
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# put models in fp16, except vae as it overflows in fp16
		snake_case__	       =   unet.half()
		snake_case__	       =   text_encoder.half()

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=2      ,       output_type='np'      ,       ).images

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)






@slow
@require_torch_gpu
class 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		List[str]:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase_		)
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 1E-3





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat_fp16.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 5E-1





	def   _snake_case   (		self		)      ->		int:
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing(1		)
		pipe.enable_sequential_cpu_offload()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=5      ,       output_type='np'      ,       )

		snake_case__	       =   torch.cuda.max_memory_allocated()
		# make sure that less than 2.9 GB is allocated
		assert mem_bytes < 2.9 * 10**9



