






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    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:int   =       DanceDiffusionPipeline
	__snake_case	:Optional[Any]   =       UNCONDITIONAL_AUDIO_GENERATION_PARAMS
	__snake_case	:Any   =       PipelineTesterMixin.required_optional_params - {
	    'callback',
	    'latents',
	    'callback_steps',
	    'output_type',
	    'num_images_per_prompt',
	}
	__snake_case	:Tuple   =       UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
	__snake_case	:List[Any]   =       False
	__snake_case	:Optional[Any]   =       False







	def  _a   (  self      :							int			)		-> Union[str, Any]:
		"""simple docstring"""







		torch.manual_seed(0			)
		__lowercase             =  UNetaDModel(
		    block_out_channels=(32, 32, 64)       ,				extra_in_channels=16       ,				sample_size=512       ,				sample_rate=1_6000       ,				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""")       ,				)
		__lowercase             =  IPNDMScheduler()

		__lowercase             =  {
		    """unet""": unet,
		    """scheduler""": scheduler,
		}
		return components







	def  _a   (  self      :							str       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							List[str]=0			)		-> int:
		"""simple docstring"""







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







	def  _a   (  self      :							Dict			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
		__lowercase             =  self.get_dummy_components()
		__lowercase             =  DanceDiffusionPipeline(**_lowerCAmelCase			)
		__lowercase             =  pipe.to(_lowerCAmelCase			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs(_lowerCAmelCase			)
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.audios

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

		assert audio.shape == (1, 2, components["unet"].sample_size)
		__lowercase             =  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  _a   (  self      :							Any			)		-> Tuple:
		"""simple docstring"""







		return super().test_save_load_local()







	@skip_mps
	def  _a   (  self      :							Union[str, Any]			)		-> Union[str, Any]:
		"""simple docstring"""







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







	@skip_mps
	def  _a   (  self      :							Tuple			)		-> Dict:
		"""simple docstring"""







		return super().test_save_load_optional_components()







	@skip_mps
	def  _a   (  self      :							Union[str, Any]			)		-> Any:
		"""simple docstring"""







		return super().test_attention_slicing_forward_pass()







	def  _a   (  self      :							List[str]			)		-> int:
		"""simple docstring"""







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


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







	def  _a   (  self      :							Union[str, Any]			)		-> Union[str, Any]:
		"""simple docstring"""







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







	def  _a   (  self      :							List[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  torch_device

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

		__lowercase             =  torch.manual_seed(0			)
		__lowercase             =  pipe(generator=_lowerCAmelCase       ,				num_inference_steps=100       ,				audio_length_in_s=4.096			)
		__lowercase             =  output.audios

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

		assert audio.shape == (1, 2, pipe.unet.sample_size)
		__lowercase             =  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  _a   (  self      :							Tuple			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  torch_device

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

		__lowercase             =  torch.manual_seed(0			)
		__lowercase             =  pipe(generator=_lowerCAmelCase       ,				num_inference_steps=100       ,				audio_length_in_s=4.096			)
		__lowercase             =  output.audios

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

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









import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__UpperCamelCase      :      Optional[Any]     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  set()
	__lowercase             =  []

	def parse_line(lowerCamelCase					):
		for line in fp:
			if isinstance(lowerCamelCase				,  lowerCamelCase					):
				__lowercase             =  line.decode("""UTF-8"""					)
			if "warnings summary (final)" in line:
				continue
			# This means we are outside the body of a warning
			elif not line.startswith(""" """					):
				# process a single warning and move it to `selected_warnings`.
				if len(lowerCamelCase					) > 0:
					__lowercase             =  """\n""".join(lowerCamelCase					)
					# Only keep the warnings specified in `targets`
					if any(F': {x}: ' in warning for x in targets					):
						selected_warnings.add(lowerCamelCase					)
					buffer.clear()
				continue
			else:
				__lowercase             =  line.strip()
				buffer.append(lowerCamelCase					)

	if from_gh:
		for filename in os.listdir(lowerCamelCase					):
			__lowercase             =  os.path.join(lowerCamelCase				,  lowerCamelCase					)
			if not os.path.isdir(lowerCamelCase					):
				# read the file
				if filename != "warnings.txt":
					continue
				with open(lowerCamelCase					) as fp:
					parse_line(lowerCamelCase					)
	else:
		try:
			with zipfile.ZipFile(lowerCamelCase					) as z:
				for filename in z.namelist():
					if not os.path.isdir(lowerCamelCase					):
						# read the file
						if filename != "warnings.txt":
							continue
						with z.open(lowerCamelCase					) as fp:
							parse_line(lowerCamelCase					)
		except Exception:
			logger.warning(
			    F'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'					)

	return selected_warnings





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  set()

	__lowercase             =  [os.path.join(lowerCamelCase				,  lowerCamelCase					) for p in os.listdir(lowerCamelCase					) if (p.endswith(""".zip"""					) or from_gh)]
	for p in paths:
		selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase				,  lowerCamelCase					)					)

	return selected_warnings


if __name__ == "__main__":





						def 				snake_case  (     lowerCamelCase					):




							'''simple docstring'''







							return values.split(""","""					)

						__UpperCamelCase      :      Union[str, Any]     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
						parser.add_argument(
						    """--output_dir""",
						    type=str,
						    required=True,
						    help="""Where to store the downloaded artifacts and other result files.""",
						)
						parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
						# optional parameters
						parser.add_argument(
						    """--targets""",
						    default="""DeprecationWarning,UserWarning,FutureWarning""",
						    type=list_str,
						    help="""Comma-separated list of target warning(s) which we want to extract.""",
						)
						parser.add_argument(
						    """--from_gh""",
						    action="""store_true""",
						    help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
						)

						__UpperCamelCase      :      List[str]     						=						parser.parse_args()

						__UpperCamelCase      :      Union[str, Any]     						=						args.from_gh
						if from_gh:
												# The artifacts have to be downloaded using `actions/download-artifact@v3`
												pass
						else:
												os.makedirs(args.output_dir, exist_ok=True)

												# get download links
												__UpperCamelCase      :      Any     						=						get_artifacts_links(args.workflow_run_id, token=args.token)
												with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
																		json.dump(artifacts, fp, ensure_ascii=False, indent=4)

												# download artifacts
												for idx, (name, url) in enumerate(artifacts.items()):
																		print(name)
																		print(url)
																		print("""=""" * 80)
																		download_artifact(name, url, args.output_dir, args.token)
																		# Be gentle to GitHub
																		time.sleep(1)

    # extract warnings from artifacts
						__UpperCamelCase      :      Union[str, Any]     						=						extract_warnings(args.output_dir, args.targets)
						__UpperCamelCase      :      Any     						=						sorted(selected_warnings)
						with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
												json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

