import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						Optional[Any]		)						->				int:
         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()


   @property
   def 			lowercase_      (self     :						List[str]		)						->				Dict:
         """simple docstring"""



         torch.manual_seed(0		)
         UpperCAmelCase__								=  UNetaDModel(
             sample_size=(3_2, 6_4)       ,  in_channels=1       ,  out_channels=1       ,  layers_per_block=2       ,  block_out_channels=(1_2_8, 1_2_8)       ,  down_block_types=("AttnDownBlock2D", "DownBlock2D")       ,  up_block_types=("UpBlock2D", "AttnUpBlock2D")       ,  )
         return model


   @property
   def 			lowercase_      (self     :						Tuple		)						->				Optional[Any]:
         """simple docstring"""



         torch.manual_seed(0		)
         UpperCAmelCase__								=  UNetaDConditionModel(
             sample_size=(6_4, 3_2)       ,  in_channels=1       ,  out_channels=1       ,  layers_per_block=2       ,  block_out_channels=(1_2_8, 1_2_8)       ,  down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")       ,  up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")       ,  cross_attention_dim=1_0       ,  )
         return model


   @property
   def 			lowercase_      (self     :						Any		)						->				List[Any]:
         """simple docstring"""



         torch.manual_seed(0		)
         UpperCAmelCase__								=  AutoencoderKL(
             sample_size=(1_2_8, 6_4)       ,  in_channels=1       ,  out_channels=1       ,  latent_channels=1       ,  layers_per_block=2       ,  block_out_channels=(1_2_8, 1_2_8)       ,  down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")       ,  up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")       ,  )
         UpperCAmelCase__								=  UNetaDModel(
             sample_size=(6_4, 3_2)       ,  in_channels=1       ,  out_channels=1       ,  layers_per_block=2       ,  block_out_channels=(1_2_8, 1_2_8)       ,  down_block_types=("AttnDownBlock2D", "DownBlock2D")       ,  up_block_types=("UpBlock2D", "AttnUpBlock2D")       ,  )
         return vqvae, unet





   @slow
   def 			lowercase_      (self     :						Any		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__								=  Mel(
             x_res=self.dummy_unet.config.sample_size[1]       ,  y_res=self.dummy_unet.config.sample_size[0]       ,  )

         UpperCAmelCase__								=  DDPMScheduler()
         UpperCAmelCase__								=  AudioDiffusionPipeline(vqvae=__UpperCAmelCase       ,  unet=self.dummy_unet       ,  mel=__UpperCAmelCase       ,  scheduler=__UpperCAmelCase		)
         UpperCAmelCase__								=  pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  torch.Generator(device=__UpperCAmelCase		).manual_seed(4_2		)
         UpperCAmelCase__								=  pipe(generator=__UpperCAmelCase       ,  steps=4		)
         UpperCAmelCase__								=  output.audios[0]
         UpperCAmelCase__								=  output.images[0]

         UpperCAmelCase__								=  torch.Generator(device=__UpperCAmelCase		).manual_seed(4_2		)
         UpperCAmelCase__								=  pipe(generator=__UpperCAmelCase       ,  steps=4       ,  return_dict=__UpperCAmelCase		)
         UpperCAmelCase__								=  output[0][0]

         assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
         assert (
             image.height == self.dummy_unet.config.sample_size[0]
             and image.width == self.dummy_unet.config.sample_size[1]
         )
         UpperCAmelCase__								=  np.frombuffer(image.tobytes()       ,  dtype="uint8"		)[:1_0]
         UpperCAmelCase__								=  np.frombuffer(image_from_tuple.tobytes()       ,  dtype="uint8"		)[:1_0]
         UpperCAmelCase__								=  np.array([6_9, 2_5_5, 2_5_5, 2_5_5, 0, 0, 7_7, 1_8_1, 1_2, 1_2_7]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() == 0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() == 0

         UpperCAmelCase__								=  Mel(
             x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]       ,  y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]       ,  )

         UpperCAmelCase__								=  DDIMScheduler()
         UpperCAmelCase__								=  self.dummy_vqvae_and_unet
         UpperCAmelCase__								=  AudioDiffusionPipeline(
             vqvae=self.dummy_vqvae_and_unet[0]       ,  unet=dummy_vqvae_and_unet[1]       ,  mel=__UpperCAmelCase       ,  scheduler=__UpperCAmelCase		)
         UpperCAmelCase__								=  pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         np.random.seed(0		)
         UpperCAmelCase__								=  np.random.uniform(-1       ,  1       ,  ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)		)
         UpperCAmelCase__								=  torch.Generator(device=__UpperCAmelCase		).manual_seed(4_2		)
         UpperCAmelCase__								=  pipe(raw_audio=__UpperCAmelCase       ,  generator=__UpperCAmelCase       ,  start_step=5       ,  steps=1_0		)
         UpperCAmelCase__								=  output.images[0]

         assert (
             image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
             and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
         )
         UpperCAmelCase__								=  np.frombuffer(image.tobytes()       ,  dtype="uint8"		)[:1_0]
         UpperCAmelCase__								=  np.array([1_2_0, 1_1_7, 1_1_0, 1_0_9, 1_3_8, 1_6_7, 1_3_8, 1_4_8, 1_3_2, 1_2_1]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() == 0

         UpperCAmelCase__								=  self.dummy_unet_condition
         UpperCAmelCase__								=  AudioDiffusionPipeline(
             vqvae=self.dummy_vqvae_and_unet[0]       ,  unet=__UpperCAmelCase       ,  mel=__UpperCAmelCase       ,  scheduler=__UpperCAmelCase		)
         UpperCAmelCase__								=  pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         np.random.seed(0		)
         UpperCAmelCase__								=  torch.rand((1, 1, 1_0)		)
         UpperCAmelCase__								=  pipe(generator=__UpperCAmelCase       ,  encoding=__UpperCAmelCase		)
         UpperCAmelCase__								=  output.images[0]
         UpperCAmelCase__								=  np.frombuffer(image.tobytes()       ,  dtype="uint8"		)[:1_0]
         UpperCAmelCase__								=  np.array([1_0_7, 1_0_3, 1_2_0, 1_2_7, 1_4_2, 1_2_2, 1_1_3, 1_2_2, 9_7, 1_1_1]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() == 0





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


   def 			lowercase_      (self     :						Tuple		)						->				Any:
         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





   def 			lowercase_      (self     :						Union[str, Any]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  torch_device

         UpperCAmelCase__								=  DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256"		)
         UpperCAmelCase__								=  pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  torch.Generator(device=__UpperCAmelCase		).manual_seed(4_2		)
         UpperCAmelCase__								=  pipe(generator=__UpperCAmelCase		)
         UpperCAmelCase__								=  output.audios[0]
         UpperCAmelCase__								=  output.images[0]

         assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
         assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
         UpperCAmelCase__								=  np.frombuffer(image.tobytes()       ,  dtype="uint8"		)[:1_0]
         UpperCAmelCase__								=  np.array([1_5_1, 1_6_7, 1_5_4, 1_4_4, 1_2_2, 1_3_4, 1_2_1, 1_0_5, 7_0, 2_6]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() == 0




def    lowerCAmelCase_ (     __A,  __A							)     ->				float:







      '''simple docstring'''


      def get_matched_characters(__A,  __A							) -> str:
            UpperCAmelCase__								=  []
            UpperCAmelCase__								=  min(len(_stra							),  len(_stra							)							) // 2
            for i, l in enumerate(_stra							):
                  UpperCAmelCase__								=  int(max(0,  i - limit							)							)
                  UpperCAmelCase__								=  int(min(i + limit + 1,  len(_stra							)							)							)
                  if l in _stra[left:right]:
                        matched.append(__A							)
                        UpperCAmelCase__								=  f"""{_stra[0:_stra.index(__A							)]} {_stra[_stra.index(__A							) + 1:]}"""

            return "".join(__A							)

      # matching characters
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  len(__A							)

      # transposition
      UpperCAmelCase__								=  (
          len([(ca, ca) for ca, ca in zip(__A,  __A							) if ca != ca]							) // 2
      )

      if not match_count:
            UpperCAmelCase__								=  0.0
      else:
            UpperCAmelCase__								=  (
                1
                / 3
                * (
                    match_count / len(__A							)
                    + match_count / len(__A							)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      UpperCAmelCase__								=  0
      for ca, ca in zip(stra[:4],  stra[:4]							):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(jaro_winkler('hello', 'world'))



