"""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








# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			1





     @register_to_config
     def __init__(self		,     UpperCAmelCase=2_0_0_0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=1e-3       )     ->   List[str]:
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   str:
      _lowercase							      =torch.linspace(1		,     self.config.sampling_eps		,     UpperCAmelCase		,     device=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Optional[int]:
      if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      _lowercase							      =(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      _lowercase							      =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
      _lowercase							      =std.flatten()
      while len(std.shape       ) < len(score.shape       ):
       _lowercase							      =std.unsqueeze(-1       )
      _lowercase							      =-score / std

      # compute
      _lowercase							      =-1.0 / len(self.timesteps       )

      _lowercase							      =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      _lowercase							      =beta_t.flatten()
      while len(beta_t.shape       ) < len(x.shape       ):
       _lowercase							      =beta_t.unsqueeze(-1       )
      _lowercase							      =-0.5 * beta_t * x

      _lowercase							      =torch.sqrt(UpperCAmelCase       )
      _lowercase							      =drift - diffusion**2 * score
      _lowercase							      =x + drift * dt

      # add noise
      _lowercase							      =randn_tensor(x.shape		,     layout=x.layout		,     generator=UpperCAmelCase		,     device=x.device		,     dtype=x.dtype       )
      _lowercase							      =x_mean + diffusion * math.sqrt(-dt       ) * noise

      return x, x_mean





     def __len__(self       )     ->   str:
      return self.config.num_train_timesteps

