

"""simple docstring"""




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      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[Any]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[int]:
						torch.manual_seed(0							)
						SCREAMING_SNAKE_CASE__										=   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					:    Dict							) ->     Any:
						torch.manual_seed(0							)
						SCREAMING_SNAKE_CASE__										=   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					:    List[Any]							) ->     int:
						torch.manual_seed(0							)
						SCREAMING_SNAKE_CASE__										=   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""")     ,					)
						SCREAMING_SNAKE_CASE__										=   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					:    int							) ->     int:
						SCREAMING_SNAKE_CASE__										=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE__										=   Mel(
						    x_res=self.dummy_unet.config.sample_size[1]     ,					y_res=self.dummy_unet.config.sample_size[0]     ,					)

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

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

						SCREAMING_SNAKE_CASE__										=   torch.Generator(device=A_							).manual_seed(4_2							)
						SCREAMING_SNAKE_CASE__										=   pipe(generator=A_     ,					steps=4     ,					return_dict=A_							)
						SCREAMING_SNAKE_CASE__										=   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]
						)
						SCREAMING_SNAKE_CASE__										=   np.frombuffer(image.tobytes()     ,					dtype="""uint8"""							)[:1_0]
						SCREAMING_SNAKE_CASE__										=   np.frombuffer(image_from_tuple.tobytes()     ,					dtype="""uint8"""							)[:1_0]
						SCREAMING_SNAKE_CASE__										=   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

						SCREAMING_SNAKE_CASE__										=   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]     ,					)

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

						np.random.seed(0							)
						SCREAMING_SNAKE_CASE__										=   np.random.uniform(-1     ,					1     ,					((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)							)
						SCREAMING_SNAKE_CASE__										=   torch.Generator(device=A_							).manual_seed(4_2							)
						SCREAMING_SNAKE_CASE__										=   pipe(raw_audio=A_     ,					generator=A_     ,					start_step=5     ,					steps=1_0							)
						SCREAMING_SNAKE_CASE__										=   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]
						)
						SCREAMING_SNAKE_CASE__										=   np.frombuffer(image.tobytes()     ,					dtype="""uint8"""							)[:1_0]
						SCREAMING_SNAKE_CASE__										=   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

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

						np.random.seed(0							)
						SCREAMING_SNAKE_CASE__										=   torch.rand((1, 1, 1_0)							)
						SCREAMING_SNAKE_CASE__										=   pipe(generator=A_     ,					encoding=A_							)
						SCREAMING_SNAKE_CASE__										=   output.images[0]
						SCREAMING_SNAKE_CASE__										=   np.frombuffer(image.tobytes()     ,					dtype="""uint8"""							)[:1_0]
						SCREAMING_SNAKE_CASE__										=   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      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Optional[int]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   torch_device

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

						SCREAMING_SNAKE_CASE__										=   torch.Generator(device=A_							).manual_seed(4_2							)
						SCREAMING_SNAKE_CASE__										=   pipe(generator=A_							)
						SCREAMING_SNAKE_CASE__										=   output.audios[0]
						SCREAMING_SNAKE_CASE__										=   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]
						SCREAMING_SNAKE_CASE__										=   np.frombuffer(image.tobytes()     ,					dtype="""uint8"""							)[:1_0]
						SCREAMING_SNAKE_CASE__										=   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




"""simple docstring"""




import math
import sys




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  ''''''
  try:
    with open(_UpperCamelCase			,					'''rb'''    ) as binary_file:
      _lowercase:      Dict   			=  binary_file.read()
    for dat in data:
      _lowercase:      List[str]   			=  f'''{dat:08b}'''
      result += curr_byte
    return result
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  {'''0''': '''0''', '''1''': '''1'''}
  _lowercase			,					_lowercase:      Optional[int]   			=  '''''', ''''''
  _lowercase:      Dict   			=  len(_UpperCamelCase    )

  for i in range(len(_UpperCamelCase    )    ):
    curr_string += data_bits[i]
    if curr_string not in lexicon:
      continue

    _lowercase:      Any   			=  lexicon[curr_string]
    result += last_match_id
    _lowercase:      Optional[int]   			=  last_match_id + '''0'''

    if math.loga(_UpperCamelCase    ).is_integer():
      _lowercase:      str   			=  {}
      for curr_key in list(_UpperCamelCase    ):
        _lowercase:      List[Any]   			=  lexicon.pop(_UpperCamelCase    )
      _lowercase:      Tuple   			=  new_lex

    _lowercase:      List[Any]   			=  last_match_id + '''1'''
    index += 1
    _lowercase:      List[Any]   			=  ''''''
  return result




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[Any]   			=  8
  try:
    with open(_UpperCamelCase			,					'''wb'''    ) as opened_file:
      _lowercase:      List[Any]   			=  [
          to_write[i : i + byte_length]
          for i in range(0			,					len(_UpperCamelCase    )			,					_UpperCamelCase    )
      ]

      if len(result_byte_array[-1]    ) % byte_length == 0:
        result_byte_array.append('''10000000'''    )
      else:
        result_byte_array[-1] += "1" + "0" * (
            byte_length - len(result_byte_array[-1]    ) - 1
        )

      for elem in result_byte_array[:-1]:
        opened_file.write(int(_UpperCamelCase			,					2    ).to_bytes(1			,					byteorder='''big'''    )    )
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  0
  for letter in data_bits:
    if letter == "1":
      break
    counter += 1

  _lowercase:      List[str]   			=  data_bits[counter:]
  _lowercase:      Union[str, Any]   			=  data_bits[counter + 1 :]
  return data_bits




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Tuple   			=  read_file_binary(_UpperCamelCase    )
  _lowercase:      Optional[Any]   			=  remove_prefix(_UpperCamelCase    )
  _lowercase:      Dict   			=  decompress_data(_UpperCamelCase    )
  write_file_binary(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  compress(sys.argv[1], sys.argv[2])
