





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     _UpperCAmelCase   (   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


  a_      =			DanceDiffusionPipeline
  a_      =			UNCONDITIONAL_AUDIO_GENERATION_PARAMS
  a_      =			PipelineTesterMixin.required_optional_params - {
      """callback""",
      """latents""",
      """callback_steps""",
      """output_type""",
      """num_images_per_prompt""",
  }
  a_      =			UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
  a_      =			False
  a_      =			False






  def     lowercase		(    self			:		List[Any]					)							->						Dict:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDModel(
         block_out_channels=(3_2, 3_2, 6_4) ,  extra_in_channels=1_6 ,  sample_size=5_1_2 ,  sample_rate=1_6_0_0_0 ,  in_channels=2 ,  out_channels=2 ,  flip_sin_to_cos=lowerCAmelCase_ ,  use_timestep_embedding=lowerCAmelCase_ ,  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     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[str]=0					)							->						Any:
     if str(lowerCAmelCase_					).startswith('mps'					):
        __lowerCAmelCase							     =     torch.manual_seed(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
     __lowerCAmelCase							     =     {
         'batch_size': 1,
         'generator': generator,
         'num_inference_steps': 4,
     }
     return inputs






  def     lowercase		(    self			:		Union[str, Any]					)							->						int:
     __lowerCAmelCase							     =     'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase							     =     self.get_dummy_components()
     __lowerCAmelCase							     =     DanceDiffusionPipeline(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.get_dummy_inputs(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.audios

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

     assert audio.shape == (1, 2, components["unet"].sample_size)
     __lowerCAmelCase							     =     np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00]					)
     assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1e-2






  @skip_mps
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return super().test_save_load_local()






  @skip_mps
  def     lowercase		(    self			:		List[str]					)							->						Dict:
     return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)






  @skip_mps
  def     lowercase		(    self			:		str					)							->						List[str]:
     return super().test_save_load_optional_components()






  @skip_mps
  def     lowercase		(    self			:		List[Any]					)							->						List[str]:
     return super().test_attention_slicing_forward_pass()






  def     lowercase		(    self			:		str					)							->						int:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





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

  """simple docstring"""


  def     lowercase		(    self			:		Any					)							->						Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		List[str]					)							->						List[str]:
     __lowerCAmelCase							     =     torch_device

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

     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  num_inference_steps=1_0_0 ,  audio_length_in_s=4.0_96					)
     __lowerCAmelCase							     =     output.audios

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

     assert audio.shape == (1, 2, pipe.unet.sample_size)
     __lowerCAmelCase							     =     np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20]					)

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






  def     lowercase		(    self			:		Tuple					)							->						Dict:
     __lowerCAmelCase							     =     torch_device

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

     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  num_inference_steps=1_0_0 ,  audio_length_in_s=4.0_96					)
     __lowerCAmelCase							     =     output.audios

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

     assert audio.shape == (1, 2, pipe.unet.sample_size)
     __lowerCAmelCase							     =     np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41]					)

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








import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


_snake_case  :   Union[str, Any]						   =  False
try:
     _snake_case  :   Tuple						   =  _is_package_available('google.colab')
except ModuleNotFoundError:
     pass



@input.register
class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str = None ,  lowerCAmelCase_			:		list = []					)							->						Optional[int]:
     __lowerCAmelCase							     =     0
     __lowerCAmelCase							     =     choices
     __lowerCAmelCase							     =     prompt
     if sys.platform == "win32":
        __lowerCAmelCase							     =     '*'
     else:
        __lowerCAmelCase							     =     '➔ '






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		str = ""					)							->						Any:
     if sys.platform != "win32":
        writeColor(self.choices[index] ,  3_2 ,  lowerCAmelCase_					)
     else:
        forceWrite(self.choices[index] ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		int					)							->						List[Any]:
     if index == self.position:
        forceWrite(f""" {self.arrow_char} """					)
        self.write_choice(lowerCAmelCase_					)
     else:
        forceWrite(f"""    {self.choices[index]}"""					)
     reset_cursor()






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Direction ,  lowerCAmelCase_			:		int = 1					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     self.position
     if direction == Direction.DOWN:
        if self.position + 1 >= len(self.choices					):
           return
        self.position += num_spaces
     else:
        if self.position - 1 < 0:
           return
        self.position -= num_spaces
     clear_line()
     self.print_choice(lowerCAmelCase_					)
     move_cursor(lowerCAmelCase_ ,  direction.name					)
     self.print_choice(self.position					)






  @input.mark(KEYMAP['up']					)
  def     lowercase		(    self			:		Optional[int]					)							->						Tuple:
     self.move_direction(Direction.UP					)






  @input.mark(KEYMAP['down']					)
  def     lowercase		(    self			:		str					)							->						Optional[Any]:
     self.move_direction(Direction.DOWN					)






  @input.mark(KEYMAP['newline']					)
  def     lowercase		(    self			:		str					)							->						Any:
     move_cursor(len(self.choices					) - self.position ,  'DOWN'					)
     return self.position






  @input.mark(KEYMAP['interrupt']					)
  def     lowercase		(    self			:		Dict					)							->						Optional[Any]:
     move_cursor(len(self.choices					) - self.position ,  'DOWN'					)
     raise KeyboardInterrupt






  @input.mark_multiple(*[KEYMAP[str(lowerCAmelCase_					)] for number in range(1_0					)]					)
  def     lowercase		(    self			:		List[Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     int(chr(self.current_selection					)					)
     __lowerCAmelCase							     =     index - self.position
     if index == self.position:
        return
     if index < len(self.choices					):
        if self.position > index:
           self.move_direction(Direction.UP ,  -movement					)
        elif self.position < index:
           self.move_direction(Direction.DOWN ,  lowerCAmelCase_					)
        else:
           return
     else:
        return






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		int = 0					)							->						int:
     if self.prompt:
        linebreak()
        forceWrite(self.prompt ,  '\n'					)
        if in_colab:
           forceWrite('Please input a choice index (starting from 0), and press enter' ,  '\n'					)
        else:
           forceWrite('Please select a choice using the arrow or number keys, and selecting with enter' ,  '\n'					)
     __lowerCAmelCase							     =     default_choice
     for i in range(len(self.choices					)					):
        self.print_choice(lowerCAmelCase_					)
        forceWrite('\n'					)
     move_cursor(len(self.choices					) - self.position ,  'UP'					)
     with cursor.hide():
        while True:
           if in_colab:
              try:
                 __lowerCAmelCase							     =     int(builtins.input()					)
              except ValueError:
                 __lowerCAmelCase							     =     default_choice
           else:
              __lowerCAmelCase							     =     self.handle_input()
           if choice is not None:
              reset_cursor()
              for _ in range(len(self.choices					) + 1					):
                 move_cursor(1 ,  'UP'					)
                 clear_line()
              self.write_choice(lowerCAmelCase_ ,  '\n'					)
              return choice

