import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			KandinskyVaaControlnetPipeline
  _SCREAMING_SNAKE_CASE    				=			["""image_embeds""", """negative_image_embeds""", """hint"""]
  _SCREAMING_SNAKE_CASE    				=			["""image_embeds""", """negative_image_embeds""", """hint"""]
  _SCREAMING_SNAKE_CASE    				=			[
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  _SCREAMING_SNAKE_CASE    				=			False

  @property
  def 		_UpperCAmelCase   (		self :      int       ):
   return 32

  @property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   return 32

  @property
  def 		_UpperCAmelCase   (		self :      int       ):
   return self.time_input_dim

  @property
  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   return self.time_input_dim * 4

  @property
  def 		_UpperCAmelCase   (		self :      Any       ):
   return 1_00

  @property
  def 		_UpperCAmelCase   (		self :      int       ):
   torch.manual_seed(0       )

   __a       =  {
       "in_channels": 8,
       # Out channels is double in channels because predicts mean and variance
       "out_channels": 8,
       "addition_embed_type": "image_hint",
       "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
       "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
       "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
       "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
       "layers_per_block": 1,
       "encoder_hid_dim": self.text_embedder_hidden_size,
       "encoder_hid_dim_type": "image_proj",
       "cross_attention_dim": self.cross_attention_dim,
       "attention_head_dim": 4,
       "resnet_time_scale_shift": "scale_shift",
       "class_embed_type": None,
   }

   __a       =  UNetaDConditionModel(**__SCREAMING_SNAKE_CASE       )
   return model

  @property
  def 		_UpperCAmelCase   (		self :      str       ):
   return {
       "block_out_channels": [32, 32, 64, 64],
       "down_block_types": [
           "DownEncoderBlock2D",
           "DownEncoderBlock2D",
           "DownEncoderBlock2D",
           "AttnDownEncoderBlock2D",
       ],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 12,
       "out_channels": 3,
       "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
       "vq_embed_dim": 4,
   }

  @property
  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   torch.manual_seed(0       )
   __a       =  VQModel(**self.dummy_movq_kwargs       )
   return model

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       =  self.dummy_unet
   __a       =  self.dummy_movq

   __a       =  DDIMScheduler(
       num_train_timesteps=10_00     ,							beta_schedule="linear"     ,							beta_start=0.0_00_85     ,							beta_end=0.0_12     ,							clip_sample=__SCREAMING_SNAKE_CASE     ,							set_alpha_to_one=__SCREAMING_SNAKE_CASE     ,							steps_offset=1     ,							prediction_type="epsilon"     ,							thresholding=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  {
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }
   return components

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Any=0       ):
   __a       =  floats_tensor((1, self.text_embedder_hidden_size)     ,							rng=random.Random(__SCREAMING_SNAKE_CASE       )       ).to(__SCREAMING_SNAKE_CASE       )
   __a       =  floats_tensor((1, self.text_embedder_hidden_size)     ,							rng=random.Random(seed + 1       )       ).to(
       __SCREAMING_SNAKE_CASE       )

   # create hint
   __a       =  floats_tensor((1, 3, 64, 64)     ,							rng=random.Random(__SCREAMING_SNAKE_CASE       )       ).to(__SCREAMING_SNAKE_CASE       )

   if str(__SCREAMING_SNAKE_CASE       ).startswith("mps"       ):
    __a       =  torch.manual_seed(__SCREAMING_SNAKE_CASE       )
   else:
    __a       =  torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
   __a       =  {
       "image_embeds": image_embeds,
       "negative_image_embeds": negative_image_embeds,
       "hint": hint,
       "generator": generator,
       "height": 64,
       "width": 64,
       "guidance_scale": 4.0,
       "num_inference_steps": 2,
       "output_type": "np",
   }
   return inputs

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  "cpu"

   __a       =  self.get_dummy_components()

   __a       =  self.pipeline_class(**__SCREAMING_SNAKE_CASE       )
   __a       =  pipe.to(__SCREAMING_SNAKE_CASE       )

   pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )       )
   __a       =  output.images

   __a       =  pipe(
       **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )     ,							return_dict=__SCREAMING_SNAKE_CASE     ,							)[0]

   __a       =  image[0, -3:, -3:, -1]
   __a       =  image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)

   __a       =  np.array(
       [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]       )

   assert (
       np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
   ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

   assert (
       np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2
   ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"       )

   __a       =  load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinskyv22/hint_image_cat.png"       )
   __a       =  torch.from_numpy(np.array(__SCREAMING_SNAKE_CASE       )       ).float() / 2_55.0
   __a       =  hint.permute(2     ,							0     ,							1       ).unsqueeze(0       )

   __a       =  KandinskyVaaPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-2-prior"     ,							torch_dtype=torch.floataa       )
   pipe_prior.to(__SCREAMING_SNAKE_CASE       )

   __a       =  KandinskyVaaControlnetPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,							torch_dtype=torch.floataa       )
   __a       =  pipeline.to(__SCREAMING_SNAKE_CASE       )
   pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  "A robot, 4k photo"

   __a       =  torch.Generator(device="cuda"       ).manual_seed(0       )
   __a       ,     __a       =  pipe_prior(
       __SCREAMING_SNAKE_CASE     ,							generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=5     ,							negative_prompt=""     ,							).to_tuple()

   __a       =  torch.Generator(device="cuda"       ).manual_seed(0       )
   __a       =  pipeline(
       image_embeds=__SCREAMING_SNAKE_CASE     ,							negative_image_embeds=__SCREAMING_SNAKE_CASE     ,							hint=__SCREAMING_SNAKE_CASE     ,							generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=1_00     ,							output_type="np"     ,							)

   __a       =  output.images[0]

   assert image.shape == (5_12, 5_12, 3)

   assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )





from datetime import datetime as dt
import os

from github import Github


SCREAMING_SNAKE_CASE :	Optional[Any]							     =		[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]



def   __A		(			):



 """simple docstring"""


 __a       =  Github(os.environ["GITHUB_TOKEN"]					)
 __a       =  g.get_repo("huggingface/transformers"					)
 __a       =  repo.get_issues(state="open"					)

 for issue in open_issues:
  __a       =  sorted([comment for comment in issue.get_comments()]							,				key=lambda _A					: i.created_at							,				reverse=_A					)
  __a       =  comments[0] if len(_A					) > 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()


