



import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 		lowercase							(     unittest.TestCase	):

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

     @property
     def       a__			(						self       )		->							Tuple:
           torch.manual_seed(0       )
           _A							:  List[str]    =		UNetaDModel(
               sample_size=(32, 64)	,  in_channels=1	,  out_channels=1	,  layers_per_block=2	,  block_out_channels=(128, 128)	,  down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")	,  up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")	,  )
           return model

     @property
     def       a__			(						self       )		->							List[Any]:
           torch.manual_seed(0       )
           _A							:  Optional[Any]    =		UNetaDConditionModel(
               sample_size=(64, 32)	,  in_channels=1	,  out_channels=1	,  layers_per_block=2	,  block_out_channels=(128, 128)	,  down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")	,  up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")	,  cross_attention_dim=10	,  )
           return model

     @property
     def       a__			(						self       )		->							Tuple:
           torch.manual_seed(0       )
           _A							:  Dict    =		AutoencoderKL(
               sample_size=(128, 64)	,  in_channels=1	,  out_channels=1	,  latent_channels=1	,  layers_per_block=2	,  block_out_channels=(128, 128)	,  down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")	,  up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")	,  )
           _A							:  int    =		UNetaDModel(
               sample_size=(64, 32)	,  in_channels=1	,  out_channels=1	,  layers_per_block=2	,  block_out_channels=(128, 128)	,  down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")	,  up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")	,  )
           return vqvae, unet



     @slow
     def       a__			(						self       )		->							int:
           _A							:  Tuple    =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           _A							:  List[str]    =		Mel(
               x_res=self.dummy_unet.config.sample_size[1]	,  y_res=self.dummy_unet.config.sample_size[0]	,  )

           _A							:  List[str]    =		DDPMScheduler()
           _A							:  Dict    =		AudioDiffusionPipeline(vqvae=_a	,  unet=self.dummy_unet	,  mel=_a	,  scheduler=_a       )
           _A							:  Optional[int]    =		pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )

           _A							:  Optional[int]    =		torch.Generator(device=_a       ).manual_seed(42       )
           _A							:  Union[str, Any]    =		pipe(generator=_a	,  steps=4       )
           _A							:  int    =		output.audios[0]
           _A							:  Dict    =		output.images[0]

           _A							:  Any    =		torch.Generator(device=_a       ).manual_seed(42       )
           _A							:  Tuple    =		pipe(generator=_a	,  steps=4	,  return_dict=_a       )
           _A							:  List[Any]    =		output[0][0]

           assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
           assert (
               image.height == self.dummy_unet.config.sample_size[0]
               and image.width == self.dummy_unet.config.sample_size[1]
           )
           _A							:  List[str]    =		np.frombuffer(image.tobytes()	,  dtype="""uint8"""       )[:10]
           _A							:  Union[str, Any]    =		np.frombuffer(image_from_tuple.tobytes()	,  dtype="""uint8"""       )[:10]
           _A							:  List[Any]    =		np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() == 0
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() == 0

           _A							:  str    =		Mel(
               x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]	,  y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]	,  )

           _A							:  Any    =		DDIMScheduler()
           _A							:  Tuple    =		self.dummy_vqvae_and_unet
           _A							:  List[str]    =		AudioDiffusionPipeline(
               vqvae=self.dummy_vqvae_and_unet[0]	,  unet=dummy_vqvae_and_unet[1]	,  mel=_a	,  scheduler=_a       )
           _A							:  List[Any]    =		pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )

           np.random.seed(0       )
           _A							:  Dict    =		np.random.uniform(-1	,  1	,  ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)       )
           _A							:  int    =		torch.Generator(device=_a       ).manual_seed(42       )
           _A							:  int    =		pipe(raw_audio=_a	,  generator=_a	,  start_step=5	,  steps=10       )
           _A							:  Dict    =		output.images[0]

           assert (
               image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
               and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
           )
           _A							:  Optional[Any]    =		np.frombuffer(image.tobytes()	,  dtype="""uint8"""       )[:10]
           _A							:  List[Any]    =		np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() == 0

           _A							:  List[Any]    =		self.dummy_unet_condition
           _A							:  List[Any]    =		AudioDiffusionPipeline(
               vqvae=self.dummy_vqvae_and_unet[0]	,  unet=_a	,  mel=_a	,  scheduler=_a       )
           _A							:  List[str]    =		pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )

           np.random.seed(0       )
           _A							:  List[str]    =		torch.rand((1, 1, 10)       )
           _A							:  Tuple    =		pipe(generator=_a	,  encoding=_a       )
           _A							:  Tuple    =		output.images[0]
           _A							:  Optional[int]    =		np.frombuffer(image.tobytes()	,  dtype="""uint8"""       )[:10]
           _A							:  int    =		np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() == 0







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

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



     def       a__			(						self       )		->							Optional[int]:
           _A							:  Tuple    =		torch_device

           _A							:  Union[str, Any]    =		DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256"""       )
           _A							:  Optional[int]    =		pipe.to(_a       )
           pipe.set_progress_bar_config(disable=_a       )

           _A							:  List[str]    =		torch.Generator(device=_a       ).manual_seed(42       )
           _A							:  Dict    =		pipe(generator=_a       )
           _A							:  Optional[int]    =		output.audios[0]
           _A							:  Optional[Any]    =		output.images[0]

           assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
           assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
           _A							:  Union[str, Any]    =		np.frombuffer(image.tobytes()	,  dtype="""uint8"""       )[:10]
           _A							:  Any    =		np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() == 0







from datetime import datetime as dt
import os

from github import Github


_snake_case            = [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def        lowerCAmelCase_			(  ):
      _A							:  int    =		Github(os.environ["""GITHUB_TOKEN"""]  )
      _A							:  Tuple    =		g.get_repo("""huggingface/transformers"""  )
      _A							:  Dict    =		repo.get_issues(state="""open"""  )

      for issue in open_issues:
            _A							:  Optional[Any]    =		sorted([comment for comment in issue.get_comments()],key=lambda snake_case_  : i.created_at,reverse=snake_case_  )
            _A							:  Dict    =		comments[0] if len(snake_case_  ) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()  )
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state="""closed"""  )
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()  )
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  issue.create_comment(
                      """This issue has been automatically marked as stale because it has not had """
                      """recent activity. If you think this still needs to be addressed """
                      """please comment on this thread.\n\nPlease note that issues that do not follow the """
                      """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
                      """are likely to be ignored."""  )


if __name__ == "__main__":
  main()

