"""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							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			str							=		DanceDiffusionPipeline
     _UpperCamelCase							:			int							=		UNCONDITIONAL_AUDIO_GENERATION_PARAMS
     _UpperCamelCase							:			Union[str, Any]							=		PipelineTesterMixin.required_optional_params - {
         "callback",
         "latents",
         "callback_steps",
         "output_type",
         "num_images_per_prompt",
     }
     _UpperCamelCase							:			List[Any]							=		UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
     _UpperCamelCase							:			Optional[Any]							=		False
     _UpperCamelCase							:			Optional[int]							=		False






     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       Any  			=     UNetaDModel(
           block_out_channels=(32, 32, 64)				,     extra_in_channels=16				,     sample_size=512				,     sample_rate=16000				,     in_channels=2				,     out_channels=2				,     flip_sin_to_cos=a__				,     use_timestep_embedding=a__				,     time_embedding_type="""fourier"""				,     mid_block_type="""UNetMidBlock1D"""				,     down_block_types=("""DownBlock1DNoSkip""", """DownBlock1D""", """AttnDownBlock1D""")				,     up_block_types=("""AttnUpBlock1D""", """UpBlock1D""", """UpBlock1DNoSkip""")				,     )
       _lowerCAmelCase						:       List[str]  			=     IPNDMScheduler()

       _lowerCAmelCase						:       Dict  			=     {
           """unet""": unet,
           """scheduler""": scheduler,
       }
       return components






     def     __A  (		self				,     a__				,     a__=0  ):
       if str(a__  ).startswith("""mps"""  ):
         _lowerCAmelCase						:       List[str]  			=     torch.manual_seed(a__  )
       else:
         _lowerCAmelCase						:       Dict  			=     torch.Generator(device=a__  ).manual_seed(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     {
           """batch_size""": 1,
           """generator""": generator,
           """num_inference_steps""": 4,
       }
       return inputs






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """cpu"""  # ensure determinism for the device-dependent torch.Generator
       _lowerCAmelCase						:       Dict  			=     self.get_dummy_components()
       _lowerCAmelCase						:       Union[str, Any]  			=     DanceDiffusionPipeline(**a__  )
       _lowerCAmelCase						:       Tuple  			=     pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     self.get_dummy_inputs(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     pipe(**a__  )
       _lowerCAmelCase						:       int  			=     output.audios

       _lowerCAmelCase						:       List[Any]  			=     audio[0, -3:, -3:]

       assert audio.shape == (1, 2, components["unet"].sample_size)
       _lowerCAmelCase						:       Dict  			=     np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0]  )
       assert np.abs(audio_slice.flatten() - expected_slice  ).max() < 1e-2






     @skip_mps
     def     __A  (		self  ):
       return super().test_save_load_local()






     @skip_mps
     def     __A  (		self  ):
       return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3  )






     @skip_mps
     def     __A  (		self  ):
       return super().test_save_load_optional_components()






     @skip_mps
     def     __A  (		self  ):
       return super().test_attention_slicing_forward_pass()






     def     __A  (		self  ):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







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






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     torch_device

       _lowerCAmelCase						:       List[str]  			=     DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""  )
       _lowerCAmelCase						:       List[Any]  			=     pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Union[str, Any]  			=     pipe(generator=a__				,     num_inference_steps=100				,     audio_length_in_s=4.0_9_6  )
       _lowerCAmelCase						:       Dict  			=     output.audios

       _lowerCAmelCase						:       Union[str, Any]  			=     audio[0, -3:, -3:]

       assert audio.shape == (1, 2, pipe.unet.sample_size)
       _lowerCAmelCase						:       int  			=     np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0]  )

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     torch_device

       _lowerCAmelCase						:       Tuple  			=     DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""				,     torch_dtype=torch.floataa  )
       _lowerCAmelCase						:       str  			=     pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Dict  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Optional[int]  			=     pipe(generator=a__				,     num_inference_steps=100				,     audio_length_in_s=4.0_9_6  )
       _lowerCAmelCase						:       Any  			=     output.audios

       _lowerCAmelCase						:       Tuple  			=     audio[0, -3:, -3:]

       assert audio.shape == (1, 2, pipe.unet.sample_size)
       _lowerCAmelCase						:       Tuple  			=     np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1]  )

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





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[Any]:  # noqa: E741
  _lowerCAmelCase						:       Optional[int]  			=     len(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     0
  _lowerCAmelCase						:       Any  			=     [0] * n
  _lowerCAmelCase						:       str  			=     [False] * n
  _lowerCAmelCase						:       str  			=     [False] * n

  def dfs(_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str	):
    if parent == root:
      out_edge_count += 1
    _lowerCAmelCase						:       Any  			=     True
    _lowerCAmelCase						:       int  			=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _lowerCAmelCase						:       Union[str, Any]  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
        _lowerCAmelCase						:       Optional[int]  			=     min(low[at]			,low[to]	)

        # AP found via bridge
        if at < low[to]:
          _lowerCAmelCase						:       int  			=     True
        # AP found via cycle
        if at == low[to]:
          _lowerCAmelCase						:       Tuple  			=     True
      else:
        _lowerCAmelCase						:       Union[str, Any]  			=     min(low[at]			,_lowerCamelCase	)
    return out_edge_count

  for i in range(_lowerCamelCase	):
    if not visited[i]:
      _lowerCAmelCase						:       int  			=     0
      _lowerCAmelCase						:       Dict  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,-1			,_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     out_edge_count > 1

  for x in range(len(_lowerCamelCase	)	):
    if is_art[x] is True:
      print(_lowerCamelCase	)


# Adjacency list of graph
_a      :		Optional[Any]           =   {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)




