

import os
from datetime import datetime as dt

from github import Github


_UpperCamelCase 			=		[
    "good first issue",
    "good second issue",
    "good difficult issue",
    "enhancement",
    "new pipeline/model",
    "new scheduler",
    "wip",
]







def 		_lowercase (     ):
      __lowerCAmelCase					:						Dict					=					Github(os.environ['''GITHUB_TOKEN''']						)
      __lowerCAmelCase					:						Tuple					=					g.get_repo('''huggingface/diffusers'''						)
      __lowerCAmelCase					:						Dict					=					repo.get_issues(state='''open'''						)

      for issue in open_issues:
            __lowerCAmelCase					:						Dict					=					sorted(issue.get_comments()   ,	key=lambda lowercase__						: i.created_at   ,	reverse=lowercase__						)
            __lowerCAmelCase					:						Optional[int]					=					comments[0] if len(lowercase__						) > 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 >= 3_0
                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 > 2_3
                and (dt.utcnow() - issue.created_at).days >= 3_0
                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 torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class   __lowercase      (_UpperCAmelCase		,   unittest.TestCase ):
 _UpperCamelCase			  =  ConsistencyModelPipeline
 _UpperCamelCase			  =  UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 _UpperCamelCase			  =  UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

 # Override required_optional_params to remove num_images_per_prompt
 _UpperCamelCase			  =  frozenset(
     [
         """num_inference_steps""",
         """generator""",
         """latents""",
         """output_type""",
         """return_dict""",
         """callback""",
         """callback_steps""",
     ] )
 @property
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					UNetaDModel.from_pretrained(
           '''diffusers/consistency-models-test'''      ,							subfolder='''test_unet'''      ,							)
       return unet
 @property
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					UNetaDModel.from_pretrained(
           '''diffusers/consistency-models-test'''      ,							subfolder='''test_unet_class_cond'''      ,							)
       return unet
 def 	UpperCamelCase__  (							self      ,							A_=False					)					->Dict:



       '''simple docstring'''





       if class_cond:
             __lowerCAmelCase					:						List[str]					=					self.dummy_cond_unet
       else:
             __lowerCAmelCase					:						Optional[Any]					=					self.dummy_uncond_unet

       # Default to CM multistep sampler
       __lowerCAmelCase					:						List[str]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)

       __lowerCAmelCase					:						Dict					=					{
           '''unet''': unet,
           '''scheduler''': scheduler,
       }

       return components
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_=0					)					->Tuple:



       '''simple docstring'''





       if str(A_					).startswith('''mps'''					):
             __lowerCAmelCase					:						str					=					torch.manual_seed(A_					)
       else:
             __lowerCAmelCase					:						Dict					=					torch.Generator(device=A_					).manual_seed(A_					)

       __lowerCAmelCase					:						Tuple					=					{
           '''batch_size''': 1,
           '''num_inference_steps''': None,
           '''timesteps''': [22, 0],
           '''generator''': generator,
           '''output_type''': '''np''',
       }

       return inputs
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						Tuple					=					self.get_dummy_components()
       __lowerCAmelCase					:						Union[str, Any]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						List[str]					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Any					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						int					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						Tuple					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						str					=					np.array([0.3_572, 0.6_273, 0.4_031, 0.3_961, 0.4_321, 0.5_730, 0.5_266, 0.4_780, 0.5_004]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						str					=					self.get_dummy_components(class_cond=A_					)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						List[Any]					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Optional[Any]					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						Optional[int]					=					0
       __lowerCAmelCase					:						int					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						List[str]					=					np.array([0.3_572, 0.6_273, 0.4_031, 0.3_961, 0.4_321, 0.5_730, 0.5_266, 0.4_780, 0.5_004]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_dummy_components()
       __lowerCAmelCase					:						List[Any]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						int					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Tuple					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						Any					=					1
       __lowerCAmelCase					:						List[Any]					=					None
       __lowerCAmelCase					:						Dict					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						Any					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						List[Any]					=					np.array([0.5_004, 0.5_004, 0.4_994, 0.5_008, 0.4_976, 0.5_018, 0.4_990, 0.4_982, 0.4_987]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3





 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						Optional[Any]					=					self.get_dummy_components(class_cond=A_					)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						Union[str, Any]					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Any					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						List[str]					=					1
       __lowerCAmelCase					:						Dict					=					None
       __lowerCAmelCase					:						Tuple					=					0
       __lowerCAmelCase					:						Dict					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						List[Any]					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						Any					=					np.array([0.5_004, 0.5_004, 0.4_994, 0.5_008, 0.4_976, 0.5_018, 0.4_990, 0.4_982, 0.4_987]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3







@slow
@require_torch_gpu
class   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 def 	UpperCamelCase__  (							self      ,							A_=0      ,							A_=False      ,							A_="cpu"      ,							A_=torch.floataa      ,							A_=(1, 3, 64, 64)					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					torch.manual_seed(A_					)

       __lowerCAmelCase					:						Tuple					=					{
           '''num_inference_steps''': None,
           '''timesteps''': [22, 0],
           '''class_labels''': 0,
           '''generator''': generator,
           '''output_type''': '''np''',
       }

       if get_fixed_latents:
             __lowerCAmelCase					:						List[str]					=					self.get_fixed_latents(seed=A_      ,							device=A_      ,							dtype=A_      ,							shape=A_					)
             __lowerCAmelCase					:						Union[str, Any]					=					latents

       return inputs
 def 	UpperCamelCase__  (							self      ,							A_=0      ,							A_="cpu"      ,							A_=torch.floataa      ,							A_=(1, 3, 64, 64)					)					->Optional[int]:



       '''simple docstring'''





       if type(A_					) == str:
             __lowerCAmelCase					:						int					=					torch.device(A_					)
       __lowerCAmelCase					:						Optional[Any]					=					torch.Generator(device=A_					).manual_seed(A_					)
       __lowerCAmelCase					:						Union[str, Any]					=					randn_tensor(A_      ,							generator=A_      ,							device=A_      ,							dtype=A_					)
       return latents
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						List[Any]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						str					=					self.get_inputs()
       __lowerCAmelCase					:						Any					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						Dict					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						Optional[int]					=					np.array([0.0_888, 0.0_881, 0.0_666, 0.0_479, 0.0_292, 0.0_195, 0.0_201, 0.0_163, 0.0_254]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						Optional[Any]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						List[Any]					=					self.get_inputs()
       __lowerCAmelCase					:						Tuple					=					1
       __lowerCAmelCase					:						Optional[Any]					=					None
       __lowerCAmelCase					:						str					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						List[Any]					=					np.array([0.0_340, 0.0_152, 0.0_063, 0.0_267, 0.0_221, 0.0_107, 0.0_416, 0.0_186, 0.0_217]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2
 @require_torch_a
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						List[str]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						Union[str, Any]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_      ,							torch_dtype=torch.floataa					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Any					=					self.get_inputs(get_fixed_latents=A_      ,							device=A_					)
       # Ensure usage of flash attention in torch 2.0
       with sdp_kernel(enable_flash=A_      ,							enable_math=A_      ,							enable_mem_efficient=A_					):
             __lowerCAmelCase					:						Dict					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						Dict					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						Optional[int]					=					np.array([0.1_875, 0.1_428, 0.1_289, 0.2_151, 0.2_092, 0.1_477, 0.1_877, 0.1_641, 0.1_353]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3





 @require_torch_a
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						List[Any]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						Union[str, Any]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_      ,							torch_dtype=torch.floataa					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Union[str, Any]					=					self.get_inputs(get_fixed_latents=A_      ,							device=A_					)
       __lowerCAmelCase					:						Any					=					1
       __lowerCAmelCase					:						int					=					None
       # Ensure usage of flash attention in torch 2.0
       with sdp_kernel(enable_flash=A_      ,							enable_math=A_      ,							enable_mem_efficient=A_					):
             __lowerCAmelCase					:						int					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						str					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						Any					=					np.array([0.1_663, 0.1_948, 0.2_275, 0.1_680, 0.1_204, 0.1_245, 0.1_858, 0.1_338, 0.2_095]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3


