



"""simple docstring"""





from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCamelCase  			=		numpy.array([0, 0])
lowerCamelCase  			=		numpy.array([0.5, 0.8_660_254])
lowerCamelCase  			=		numpy.array([1, 0])
lowerCamelCase  			=		[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				initial_vectors
      for _ in range(lowerCAmelCase__ ):
            UpperCAmelCase_     					=				iteration_step(lowerCAmelCase__ )
      return vectors
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				[]
      for i, start_vector in enumerate(vectors[:-1] ):
            UpperCAmelCase_     					=				vectors[i + 1]
            new_vectors.append(lowerCAmelCase__ )
            UpperCAmelCase_     					=				end_vector - start_vector
            new_vectors.append(start_vector + difference_vector / 3 )
            new_vectors.append(
                start_vector + difference_vector / 3 + rotate(difference_vector / 3      ,     60 ) )
            new_vectors.append(start_vector + difference_vector * 2 / 3 )
      new_vectors.append(vectors[-1] )
      return new_vectors
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				numpy.radians(lowerCAmelCase__ )
      UpperCAmelCase_					,       UpperCAmelCase_     					=				numpy.cos(lowerCAmelCase__ ), numpy.sin(lowerCAmelCase__ )
      UpperCAmelCase_     					=				numpy.array(((c, -s), (s, c)) )
      return numpy.dot(lowerCAmelCase__      ,     lowerCAmelCase__ )
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				plt.gca()
      axes.set_aspect("equal" )

      # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
      # y-coordinates as inputs, which are constructed from the vector-list using
      # zip()
      UpperCAmelCase_					,       UpperCAmelCase_     					=				zip(*lowerCAmelCase__ )
      plt.plot(lowerCAmelCase__      ,     lowerCAmelCase__ )
      plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowerCamelCase  			=		iterate(INITIAL_VECTORS, 5)
  plot(processed_vectors)











"""simple docstring"""
import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class       a							(   a__   , unittest.TestCase  ):
 snake_case__             =       DanceDiffusionPipeline
 snake_case__             =       UNCONDITIONAL_AUDIO_GENERATION_PARAMS
 snake_case__             =       PipelineTesterMixin.required_optional_params - {
     '''callback''',
     '''latents''',
     '''callback_steps''',
     '''output_type''',
     '''num_images_per_prompt''',
 }
 snake_case__             =       UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
 snake_case__             =       False
 snake_case__             =       False



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  torch.manual_seed(0     )
  lowerCAmelCase    				= UNetaDModel(
      block_out_channels=(32, 32, 64)				,       extra_in_channels=16				,       sample_size=5_12				,       sample_rate=1_60_00				,       in_channels=2				,       out_channels=2				,       flip_sin_to_cos=_snake_case				,       use_timestep_embedding=_snake_case				,       time_embedding_type='fourier'				,       mid_block_type='UNetMidBlock1D'				,       down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D')				,       up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip')				,       )
  lowerCAmelCase    				= IPNDMScheduler()

  lowerCAmelCase    				= {
      'unet': unet,
      'scheduler': scheduler,
  }
  return components



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=0     ):







  """simple docstring"""






  if str(_snake_case     ).startswith('mps'     ):
   lowerCAmelCase    				= torch.manual_seed(_snake_case     )
  else:
   lowerCAmelCase    				= torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )
  lowerCAmelCase    				= {
      'batch_size': 1,
      'generator': generator,
      'num_inference_steps': 4,
  }
  return inputs



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= 'cpu'  # ensure determinism for the device-dependent torch.Generator
  lowerCAmelCase    				= self.get_dummy_components()
  lowerCAmelCase    				= DanceDiffusionPipeline(**_snake_case     )
  lowerCAmelCase    				= pipe.to(_snake_case     )
  pipe.set_progress_bar_config(disable=_snake_case     )

  lowerCAmelCase    				= self.get_dummy_inputs(_snake_case     )
  lowerCAmelCase    				= pipe(**_snake_case     )
  lowerCAmelCase    				= output.audios

  lowerCAmelCase    				= audio[0, -3:, -3:]

  assert audio.shape == (1, 2, components["unet"].sample_size)
  lowerCAmelCase    				= np.array([-0.7_265, 1.0_000, -0.8_388, 0.1_175, 0.9_498, -1.0_000]     )
  assert np.abs(audio_slice.flatten() - expected_slice     ).max() < 1E-2



 @skip_mps
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return super().test_save_load_local()



 @skip_mps
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3     )



 @skip_mps
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return super().test_save_load_optional_components()



 @skip_mps
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return super().test_attention_slicing_forward_pass()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  super().test_inference_batch_single_identical(expected_max_diff=3E-3     )







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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= torch_device

  lowerCAmelCase    				= DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'     )
  lowerCAmelCase    				= pipe.to(_snake_case     )
  pipe.set_progress_bar_config(disable=_snake_case     )

  lowerCAmelCase    				= torch.manual_seed(0     )
  lowerCAmelCase    				= pipe(generator=_snake_case				,       num_inference_steps=1_00				,       audio_length_in_s=4.096     )
  lowerCAmelCase    				= output.audios

  lowerCAmelCase    				= audio[0, -3:, -3:]

  assert audio.shape == (1, 2, pipe.unet.sample_size)
  lowerCAmelCase    				= np.array([-0.0_192, -0.0_231, -0.0_318, -0.0_059, 0.0_002, -0.0_020]     )

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= torch_device

  lowerCAmelCase    				= DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'				,       torch_dtype=torch.floataa     )
  lowerCAmelCase    				= pipe.to(_snake_case     )
  pipe.set_progress_bar_config(disable=_snake_case     )

  lowerCAmelCase    				= torch.manual_seed(0     )
  lowerCAmelCase    				= pipe(generator=_snake_case				,       num_inference_steps=1_00				,       audio_length_in_s=4.096     )
  lowerCAmelCase    				= output.audios

  lowerCAmelCase    				= audio[0, -3:, -3:]

  assert audio.shape == (1, 2, pipe.unet.sample_size)
  lowerCAmelCase    				= np.array([-0.0_367, -0.0_488, -0.0_771, -0.0_525, -0.0_444, -0.0_341]     )

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





