





import os
from datetime import datetime as dt

from github import Github


_a     :    Optional[int]         =						[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]
def 							a_	(    )				->			List[str]:




				"""simple docstring"""






				snake_case   :					List[Any]    						=				Github(os.environ['''GITHUB_TOKEN''']   )
				snake_case   :					Union[str, Any]    						=				g.get_repo('''huggingface/diffusers'''   )
				snake_case   :					Dict    						=				repo.get_issues(state='''open'''   )

				for issue in open_issues:
								snake_case   :					List[str]    						=				sorted(issue.get_comments()		,     key=lambda __magic_name__   : i.created_at		,     reverse=__magic_name__   )
								snake_case   :					str    						=				comments[0] if len(__magic_name__   ) > 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()   )
								):
												# Closes the issue after 7 days of inactivity since the Stalebot notification.
												issue.edit(state='''closed'''   )
								elif (
								    "stale" in issue.get_labels()
								    and last_comment is not None
								    and last_comment.user.login != "github-actions[bot]"
								):
												# Opens the issue if someone other than Stalebot commented.
												issue.edit(state='''open'''   )
												issue.remove_from_labels('''stale'''   )
								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()   )
								):
												# Post a Stalebot notification after 23 days of inactivity.
												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/diffusers/blob/main/CONTRIBUTING.md) '''
												    '''are likely to be ignored.'''   )
												issue.add_to_labels('''stale'''   )


if __name__ == "__main__":
			main()








import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


UpperCAmelCase__							  =						False






class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Dict    )       ->str:




       """simple docstring"""


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







      @property
      def  __lowerCAmelCase   ( self :		List[str]    )       ->Any:




       """simple docstring"""


       return 12







      @property
      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Dict:




       """simple docstring"""


       return 12







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->Any:




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		int    )       ->List[str]:




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = VQModel(
           block_out_channels=[32, 64]					,   in_channels=3					,   out_channels=3					,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,   latent_channels=3					,   num_vq_embeddings=self.num_embed					,   vq_embed_dim=3					,   )
       return model







      @property
      def  __lowerCAmelCase   ( self :		str    )       ->str:




       """simple docstring"""


       a						    = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )
       return tokenizer







      @property
      def  __lowerCAmelCase   ( self :		str    )       ->Union[str, Any]:




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = CLIPTextConfig(
           bos_token_id=0					,   eos_token_id=2					,   hidden_size=self.text_embedder_hidden_size					,   intermediate_size=37					,   layer_norm_eps=1e-0_5					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1_000					,   )
       return CLIPTextModel(__UpperCAmelCase    )







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->Tuple:




       """simple docstring"""


       torch.manual_seed(0    )

       a						    = 12
       a						    = 12

       a						    = {
           '''attention_bias''': True,
           '''cross_attention_dim''': 32,
           '''attention_head_dim''': height * width,
           '''num_attention_heads''': 1,
           '''num_vector_embeds''': self.num_embed,
           '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
           '''norm_num_groups''': 32,
           '''sample_size''': width,
           '''activation_fn''': '''geglu-approximate''',
       }

       a						    = TransformeraDModel(**__UpperCAmelCase    )
       return model







      def  __lowerCAmelCase   ( self :		Dict    )       ->Any:




       """simple docstring"""


       a						    = '''cpu'''

       a						    = self.dummy_vqvae
       a						    = self.dummy_text_encoder
       a						    = self.dummy_tokenizer
       a						    = self.dummy_transformer
       a						    = VQDiffusionScheduler(self.num_embed    )
       a						    = LearnedClassifierFreeSamplingEmbeddings(learnable=__UpperCAmelCase    )

       a						    = VQDiffusionPipeline(
           vqvae=__UpperCAmelCase					,   text_encoder=__UpperCAmelCase					,   tokenizer=__UpperCAmelCase					,   transformer=__UpperCAmelCase					,   scheduler=__UpperCAmelCase					,   learned_classifier_free_sampling_embeddings=__UpperCAmelCase					,   )
       a						    = pipe.to(__UpperCAmelCase    )
       pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = '''teddy bear playing in the pool'''

       a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(0    )
       a						    = pipe([prompt]					,   generator=__UpperCAmelCase					,   num_inference_steps=2					,   output_type='''np'''    )
       a						    = output.images

       a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(0    )
       a						    = pipe(
           [prompt]					,   generator=__UpperCAmelCase					,   output_type='''np'''					,   return_dict=__UpperCAmelCase					,   num_inference_steps=2    )[0]

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

       assert image.shape == (1, 24, 24, 3)

       a						    = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]    )

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







      def  __lowerCAmelCase   ( self :		Tuple    )       ->str:




       """simple docstring"""


       a						    = '''cpu'''

       a						    = self.dummy_vqvae
       a						    = self.dummy_text_encoder
       a						    = self.dummy_tokenizer
       a						    = self.dummy_transformer
       a						    = VQDiffusionScheduler(self.num_embed    )
       a						    = LearnedClassifierFreeSamplingEmbeddings(
           learnable=__UpperCAmelCase					,   hidden_size=self.text_embedder_hidden_size					,   length=tokenizer.model_max_length    )

       a						    = VQDiffusionPipeline(
           vqvae=__UpperCAmelCase					,   text_encoder=__UpperCAmelCase					,   tokenizer=__UpperCAmelCase					,   transformer=__UpperCAmelCase					,   scheduler=__UpperCAmelCase					,   learned_classifier_free_sampling_embeddings=__UpperCAmelCase					,   )
       a						    = pipe.to(__UpperCAmelCase    )
       pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = '''teddy bear playing in the pool'''

       a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(0    )
       a						    = pipe([prompt]					,   generator=__UpperCAmelCase					,   num_inference_steps=2					,   output_type='''np'''    )
       a						    = output.images

       a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(0    )
       a						    = pipe(
           [prompt]					,   generator=__UpperCAmelCase					,   output_type='''np'''					,   return_dict=__UpperCAmelCase					,   num_inference_steps=2    )[0]

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

       assert image.shape == (1, 24, 24, 3)

       a						    = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]    )

       assert np.abs(image_slice.flatten() - expected_slice    ).max() < 2.0
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2






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






      '''simple docstring'''




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




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Dict    )       ->int:




       """simple docstring"""


       a						    = load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''    )

       a						    = VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''    )
       a						    = pipeline.to(__UpperCAmelCase    )
       pipeline.set_progress_bar_config(disable=__UpperCAmelCase    )

       # requires GPU generator for gumbel softmax
       # don't use GPU generator in tests though
       a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(0    )
       a						    = pipeline(
           '''teddy bear playing in the pool'''					,   num_images_per_prompt=1					,   generator=__UpperCAmelCase					,   output_type='''np'''					,   )

       a						    = output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image    ).max() < 2.0


