
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 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







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






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           torch.manual_seed(0	)
           UpperCamelCase__     : str											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           torch.manual_seed(0	)
           UpperCamelCase__     : Dict											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           torch.manual_seed(0	)
           UpperCamelCase__     : Tuple											=					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__     : List[str]											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : str											=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase__     : Union[str, Any]											=					Mel(
               x_res=self.dummy_unet.config.sample_size[1]    ,    y_res=self.dummy_unet.config.sample_size[0]    ,    )

           UpperCamelCase__     : str											=					DDPMScheduler()
           UpperCamelCase__     : Optional[int]											=					AudioDiffusionPipeline(vqvae=__SCREAMING_SNAKE_CASE    ,    unet=self.dummy_unet    ,    mel=__SCREAMING_SNAKE_CASE    ,    scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[Any]											=					pipe.to(__SCREAMING_SNAKE_CASE	)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[str]											=					torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(4_2	)
           UpperCamelCase__     : Any											=					pipe(generator=__SCREAMING_SNAKE_CASE    ,    steps=4	)
           UpperCamelCase__     : Tuple											=					output.audios[0]
           UpperCamelCase__     : str											=					output.images[0]

           UpperCamelCase__     : Dict											=					torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(4_2	)
           UpperCamelCase__     : Tuple											=					pipe(generator=__SCREAMING_SNAKE_CASE    ,    steps=4    ,    return_dict=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					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__     : Dict											=					np.frombuffer(image.tobytes()    ,    dtype='''uint8'''	)[:1_0]
           UpperCamelCase__     : Tuple											=					np.frombuffer(image_from_tuple.tobytes()    ,    dtype='''uint8'''	)[:1_0]
           UpperCamelCase__     : List[Any]											=					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__     : str											=					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__     : int											=					DDIMScheduler()
           UpperCamelCase__     : Dict											=					self.dummy_vqvae_and_unet
           UpperCamelCase__     : Optional[Any]											=					AudioDiffusionPipeline(
               vqvae=self.dummy_vqvae_and_unet[0]    ,    unet=dummy_vqvae_and_unet[1]    ,    mel=__SCREAMING_SNAKE_CASE    ,    scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					pipe.to(__SCREAMING_SNAKE_CASE	)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

           np.random.seed(0	)
           UpperCamelCase__     : List[str]											=					np.random.uniform(-1    ,    1    ,    ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)	)
           UpperCamelCase__     : Any											=					torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(4_2	)
           UpperCamelCase__     : List[str]											=					pipe(raw_audio=__SCREAMING_SNAKE_CASE    ,    generator=__SCREAMING_SNAKE_CASE    ,    start_step=5    ,    steps=1_0	)
           UpperCamelCase__     : Union[str, Any]											=					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__     : Tuple											=					np.frombuffer(image.tobytes()    ,    dtype='''uint8'''	)[:1_0]
           UpperCamelCase__     : Optional[int]											=					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__     : str											=					self.dummy_unet_condition
           UpperCamelCase__     : Tuple											=					AudioDiffusionPipeline(
               vqvae=self.dummy_vqvae_and_unet[0]    ,    unet=__SCREAMING_SNAKE_CASE    ,    mel=__SCREAMING_SNAKE_CASE    ,    scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					pipe.to(__SCREAMING_SNAKE_CASE	)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

           np.random.seed(0	)
           UpperCamelCase__     : Optional[int]											=					torch.rand((1, 1, 1_0)	)
           UpperCamelCase__     : Optional[Any]											=					pipe(generator=__SCREAMING_SNAKE_CASE    ,    encoding=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					output.images[0]
           UpperCamelCase__     : Any											=					np.frombuffer(image.tobytes()    ,    dtype='''uint8'''	)[:1_0]
           UpperCamelCase__     : int											=					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 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					torch_device

           UpperCamelCase__     : Any											=					DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''	)
           UpperCamelCase__     : Optional[int]											=					pipe.to(__SCREAMING_SNAKE_CASE	)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(4_2	)
           UpperCamelCase__     : str											=					pipe(generator=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					output.audios[0]
           UpperCamelCase__     : Tuple											=					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__     : Dict											=					np.frombuffer(image.tobytes()    ,    dtype='''uint8'''	)[:1_0]
           UpperCamelCase__     : str											=					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 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):

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

            return "".join(UpperCamelCase__		)

      # matching characters
      UpperCamelCase__     : Any											=					get_matched_characters(UpperCamelCase__			,		UpperCamelCase__		)
      UpperCamelCase__     : Union[str, Any]											=					get_matched_characters(UpperCamelCase__			,		UpperCamelCase__		)
      UpperCamelCase__     : List[Any]											=					len(UpperCamelCase__		)

      # transposition
      UpperCamelCase__     : str											=					(
          len([(ca, ca) for ca, ca in zip(UpperCamelCase__			,		UpperCamelCase__		) if ca != ca]		) // 2
      )

      if not match_count:
            UpperCamelCase__     : Union[str, Any]											=					0.0
      else:
            UpperCamelCase__     : Optional[int]											=					(
                1
                / 3
                * (
                    match_count / len(UpperCamelCase__		)
                    + match_count / len(UpperCamelCase__		)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      UpperCamelCase__     : Dict											=					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"))


