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




							def 						__snake_case(    self :       Union[str, Any]   )       ->					Dict:



												'''simple docstring'''

												torch.manual_seed(0   )
												SCREAMING_SNAKE_CASE           =							UNetaDModel(
												    block_out_channels=(32, 32, 64)						,				extra_in_channels=16						,				sample_size=512						,				sample_rate=16_000						,				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")						,				)
												SCREAMING_SNAKE_CASE           =							IPNDMScheduler()

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




							def 						__snake_case(    self :       List[str]						,				_UpperCamelCase :       Any						,				_UpperCamelCase :       List[Any]=0   )       ->					Optional[Any]:



												'''simple docstring'''

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




							def 						__snake_case(    self :       Dict   )       ->					Any:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =							self.get_dummy_inputs(_lowerCamelCase   )
												SCREAMING_SNAKE_CASE           =							pipe(**_lowerCamelCase   )
												SCREAMING_SNAKE_CASE           =							output.audios

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

												assert audio.shape == (1, 2, components["unet"].sample_size)
												SCREAMING_SNAKE_CASE           =							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 						__snake_case(    self :       Any   )       ->					Optional[int]:



												'''simple docstring'''

												return super().test_save_load_local()




							@skip_mps
							def 						__snake_case(    self :       Any   )       ->					Dict:



												'''simple docstring'''

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




							@skip_mps
							def 						__snake_case(    self :       str   )       ->					int:



												'''simple docstring'''

												return super().test_save_load_optional_components()




							@skip_mps
							def 						__snake_case(    self :       Optional[int]   )       ->					Tuple:



												'''simple docstring'''

												return super().test_attention_slicing_forward_pass()





							def 						__snake_case(    self :       List[Any]   )       ->					Dict:



												'''simple docstring'''

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



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




							def 						__snake_case(    self :       List[str]   )       ->					List[str]:



												'''simple docstring'''

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




							def 						__snake_case(    self :       Dict   )       ->					Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							torch_device

												SCREAMING_SNAKE_CASE           =							DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"   )
												SCREAMING_SNAKE_CASE           =							pipe.to(_lowerCamelCase   )
												pipe.set_progress_bar_config(disable=_lowerCamelCase   )

												SCREAMING_SNAKE_CASE           =							torch.manual_seed(0   )
												SCREAMING_SNAKE_CASE           =							pipe(generator=_lowerCamelCase						,				num_inference_steps=100						,				audio_length_in_s=4.0_9_6   )
												SCREAMING_SNAKE_CASE           =							output.audios

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

												assert audio.shape == (1, 2, pipe.unet.sample_size)
												SCREAMING_SNAKE_CASE           =							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 						__snake_case(    self :       Any   )       ->					Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							torch_device

												SCREAMING_SNAKE_CASE           =							DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"						,				torch_dtype=torch.floataa   )
												SCREAMING_SNAKE_CASE           =							pipe.to(_lowerCamelCase   )
												pipe.set_progress_bar_config(disable=_lowerCamelCase   )

												SCREAMING_SNAKE_CASE           =							torch.manual_seed(0   )
												SCREAMING_SNAKE_CASE           =							pipe(generator=_lowerCamelCase						,				num_inference_steps=100						,				audio_length_in_s=4.0_9_6   )
												SCREAMING_SNAKE_CASE           =							output.audios

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

												assert audio.shape == (1, 2, pipe.unet.sample_size)
												SCREAMING_SNAKE_CASE           =							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"""






# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile




def       A_  (				__lowercase						,	__lowercase						,	__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Tuple   	=multiprocessing.Manager()
 UpperCamelCase_     :					Dict   	=manager.list()

 UpperCamelCase_     :					Union[str, Any]   	=multiprocessing.Process(target=__lowercase						,	args=(check_program, result, timeout)	)
 p.start()
 p.join(timeout=timeout + 1	)
 if p.is_alive():
  p.kill()

 if not result:
  result.append('timed out'	)

 return {
     "task_id": task_id,
     "passed": result[0] == "passed",
     "result": result[0],
     "completion_id": completion_id,
 }




def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 with create_tempdir():
  # These system calls are needed when cleaning up tempdir.
  import os
  import shutil

  UpperCamelCase_     :					Tuple   	=shutil.rmtree
  UpperCamelCase_     :					List[str]   	=os.rmdir
  UpperCamelCase_     :					Any   	=os.chdir

  # Disable functionalities that can make destructive changes to the test.
  reliability_guard()

  # Run program.
  try:
   UpperCamelCase_     :					List[str]   	={}
   with swallow_io():
    with time_limit(__lowercase	):
     exec(__lowercase						,	__lowercase	)
   result.append('passed'	)
  except TimeoutException:
   result.append('timed out'	)
  except BaseException as e:
   result.append(F'''failed: {e}'''	)

  # Needed for cleaning up.
  UpperCamelCase_     :					Optional[Any]   	=rmtree
  UpperCamelCase_     :					str   	=rmdir
  UpperCamelCase_     :					Dict   	=chdir




@contextlib.contextmanager
def       A_  (				__lowercase	):
 def signal_handler(__lowercase						,	__lowercase	):
  raise TimeoutException('Timed out!'	)

 signal.setitimer(signal.ITIMER_REAL						,	__lowercase	)
 signal.signal(signal.SIGALRM						,	__lowercase	)
 try:
  yield
 finally:
  signal.setitimer(signal.ITIMER_REAL						,	0	)




@contextlib.contextmanager
def       A_  (				):
 UpperCamelCase_     :					str   	=WriteOnlyStringIO()
 with contextlib.redirect_stdout(__lowercase	):
  with contextlib.redirect_stderr(__lowercase	):
   with redirect_stdin(__lowercase	):
    yield




@contextlib.contextmanager
def       A_  (				):
 with tempfile.TemporaryDirectory() as dirname:
  with chdir(__lowercase	):
   yield dirname





class 				a__					(						A__       ):
     pass





class 				a__					(						io.StringIO       ):



     def  lowerCamelCase_						(							self :Any      ,  *_lowerCamelCase :Any      ,  **_lowerCamelCase :Tuple   ):







      '''simple docstring'''



      raise OSError



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  *_lowerCamelCase :Any      ,  **_lowerCamelCase :Tuple   ):







      '''simple docstring'''



      raise OSError



     def  lowerCamelCase_						(							self :Tuple      ,  *_lowerCamelCase :str      ,  **_lowerCamelCase :List[str]   ):







      '''simple docstring'''



      raise OSError




     def  lowerCamelCase_						(							self :List[str]      ,  *_lowerCamelCase :Optional[int]      ,  **_lowerCamelCase :str   ):







      '''simple docstring'''



      return False





class 				a__					(						contextlib._RedirectStream       ):  # type: ignore
     UpperCAmelCase__         =      '''stdin'''




@contextlib.contextmanager
def       A_  (				__lowercase	):
 if root == ".":
  yield
  return
 UpperCamelCase_     :					List[str]   	=os.getcwd()
 os.chdir(__lowercase	)
 try:
  yield
 except BaseException as exc:
  raise exc
 finally:
  os.chdir(__lowercase	)





def       A_  (				__lowercase=None	):

 if maximum_memory_bytes is not None:
  import resource

  resource.setrlimit(resource.RLIMIT_AS						,	(maximum_memory_bytes, maximum_memory_bytes)	)
  resource.setrlimit(resource.RLIMIT_DATA						,	(maximum_memory_bytes, maximum_memory_bytes)	)
  if not platform.uname().system == "Darwin":
   resource.setrlimit(resource.RLIMIT_STACK						,	(maximum_memory_bytes, maximum_memory_bytes)	)

 faulthandler.disable()

 import builtins

 UpperCamelCase_     :					Any   	=None
 UpperCamelCase_     :					Any   	=None

 import os

 UpperCamelCase_     :					Union[str, Any]   	='1'

 UpperCamelCase_     :					Union[str, Any]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					Dict   	=None
 UpperCamelCase_     :					List[Any]   	=None
 UpperCamelCase_     :					Any   	=None
 UpperCamelCase_     :					str   	=None
 UpperCamelCase_     :					Optional[int]   	=None
 UpperCamelCase_     :					Union[str, Any]   	=None
 UpperCamelCase_     :					str   	=None
 UpperCamelCase_     :					str   	=None
 UpperCamelCase_     :					List[Any]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					str   	=None
 UpperCamelCase_     :					List[str]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					int   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					Tuple   	=None
 UpperCamelCase_     :					List[Any]   	=None
 UpperCamelCase_     :					Tuple   	=None
 UpperCamelCase_     :					Dict   	=None
 UpperCamelCase_     :					Optional[int]   	=None
 UpperCamelCase_     :					str   	=None
 UpperCamelCase_     :					Union[str, Any]   	=None
 UpperCamelCase_     :					int   	=None

 import shutil

 UpperCamelCase_     :					List[str]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					Dict   	=None

 import subprocess

 UpperCamelCase_     :					Optional[int]   	=None  # type: ignore

 UpperCamelCase_     :					Union[str, Any]   	=None

 import sys

 UpperCamelCase_     :					Union[str, Any]   	=None
 UpperCamelCase_     :					Tuple   	=None
 UpperCamelCase_     :					Union[str, Any]   	=None
 UpperCamelCase_     :					Dict   	=None
 UpperCamelCase_     :					Any   	=None
