


import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np






class     UpperCamelCase:


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Tuple:




											'''simple docstring'''

											torch.manual_seed(0						)
											__snake_case				=		TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"						)

											torch.manual_seed(0						)
											__snake_case				=		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"						)

											torch.manual_seed(0						)
											__snake_case				=		UNetaDConditionModel(
											    sample_size=3_2     ,    layers_per_block=1     ,    block_out_channels=[3_2, 6_4]     ,    down_block_types=[
											        "ResnetDownsampleBlock2D",
											        "SimpleCrossAttnDownBlock2D",
											    ]     ,    mid_block_type="UNetMidBlock2DSimpleCrossAttn"     ,    up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]     ,    in_channels=3     ,    out_channels=6     ,    cross_attention_dim=3_2     ,    encoder_hid_dim=3_2     ,    attention_head_dim=8     ,    addition_embed_type="text"     ,    addition_embed_type_num_heads=2     ,    cross_attention_norm="group_norm"     ,    resnet_time_scale_shift="scale_shift"     ,    act_fn="gelu"     ,    )
											unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

											torch.manual_seed(0						)
											__snake_case				=		DDPMScheduler(
											    num_train_timesteps=1_0_0_0     ,    beta_schedule="squaredcos_cap_v2"     ,    beta_start=0.0001     ,    beta_end=0.02     ,    thresholding=SCREAMING_SNAKE_CASE     ,    dynamic_thresholding_ratio=0.95     ,    sample_max_value=1.0     ,    prediction_type="epsilon"     ,    variance_type="learned_range"     ,    )

											torch.manual_seed(0						)
											__snake_case				=		IFWatermarker()

											return {
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "unet": unet,
											    "scheduler": scheduler,
											    "watermarker": watermarker,
											    "safety_checker": None,
											    "feature_extractor": None,
											}


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	str:




											'''simple docstring'''

											torch.manual_seed(0						)
											__snake_case				=		TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"						)

											torch.manual_seed(0						)
											__snake_case				=		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"						)

											torch.manual_seed(0						)
											__snake_case				=		UNetaDConditionModel(
											    sample_size=3_2     ,    layers_per_block=[1, 2]     ,    block_out_channels=[3_2, 6_4]     ,    down_block_types=[
											        "ResnetDownsampleBlock2D",
											        "SimpleCrossAttnDownBlock2D",
											    ]     ,    mid_block_type="UNetMidBlock2DSimpleCrossAttn"     ,    up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]     ,    in_channels=6     ,    out_channels=6     ,    cross_attention_dim=3_2     ,    encoder_hid_dim=3_2     ,    attention_head_dim=8     ,    addition_embed_type="text"     ,    addition_embed_type_num_heads=2     ,    cross_attention_norm="group_norm"     ,    resnet_time_scale_shift="scale_shift"     ,    act_fn="gelu"     ,    class_embed_type="timestep"     ,    mid_block_scale_factor=1.414     ,    time_embedding_act_fn="gelu"     ,    time_embedding_dim=3_2     ,    )
											unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

											torch.manual_seed(0						)
											__snake_case				=		DDPMScheduler(
											    num_train_timesteps=1_0_0_0     ,    beta_schedule="squaredcos_cap_v2"     ,    beta_start=0.0001     ,    beta_end=0.02     ,    thresholding=SCREAMING_SNAKE_CASE     ,    dynamic_thresholding_ratio=0.95     ,    sample_max_value=1.0     ,    prediction_type="epsilon"     ,    variance_type="learned_range"     ,    )

											torch.manual_seed(0						)
											__snake_case				=		DDPMScheduler(
											    num_train_timesteps=1_0_0_0     ,    beta_schedule="squaredcos_cap_v2"     ,    beta_start=0.0001     ,    beta_end=0.02     ,    )

											torch.manual_seed(0						)
											__snake_case				=		IFWatermarker()

											return {
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "unet": unet,
											    "scheduler": scheduler,
											    "image_noising_scheduler": image_noising_scheduler,
											    "watermarker": watermarker,
											    "safety_checker": None,
											    "feature_extractor": None,
											}


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.get_dummy_components()
											__snake_case				=		self.pipeline_class(**SCREAMING_SNAKE_CASE						)
											pipe.to(SCREAMING_SNAKE_CASE						)
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

											__snake_case				=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE						)

											__snake_case				=		inputs["prompt"]
											__snake_case				=		inputs["generator"]
											__snake_case				=		inputs["num_inference_steps"]
											__snake_case				=		inputs["output_type"]

											if "image" in inputs:
																	__snake_case				=		inputs["image"]
											else:
																	__snake_case				=		None

											if "mask_image" in inputs:
																	__snake_case				=		inputs["mask_image"]
											else:
																	__snake_case				=		None

											if "original_image" in inputs:
																	__snake_case				=		inputs["original_image"]
											else:
																	__snake_case				=		None

											__snake_case		,					__snake_case				=		pipe.encode_prompt(SCREAMING_SNAKE_CASE						)

											# inputs with prompt converted to embeddings
											__snake_case				=		{
											    "prompt_embeds": prompt_embeds,
											    "negative_prompt_embeds": negative_prompt_embeds,
											    "generator": generator,
											    "num_inference_steps": num_inference_steps,
											    "output_type": output_type,
											}

											if image is not None:
																	__snake_case				=		image

											if mask_image is not None:
																	__snake_case				=		mask_image

											if original_image is not None:
																	__snake_case				=		original_image

											# set all optional components to None
											for optional_component in pipe._optional_components:
																	setattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

											__snake_case				=		pipe(**SCREAMING_SNAKE_CASE						)[0]

											with tempfile.TemporaryDirectory() as tmpdir:
																	pipe.save_pretrained(SCREAMING_SNAKE_CASE						)
																	__snake_case				=		self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE						)
																	pipe_loaded.to(SCREAMING_SNAKE_CASE						)
																	pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

											pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

											for optional_component in pipe._optional_components:
																	self.assertTrue(
																	    getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						) is None     ,    f'''`{optional_component}` did not stay set to None after loading.'''     ,    )

											__snake_case				=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE						)

											__snake_case				=		inputs["generator"]
											__snake_case				=		inputs["num_inference_steps"]
											__snake_case				=		inputs["output_type"]

											# inputs with prompt converted to embeddings
											__snake_case				=		{
											    "prompt_embeds": prompt_embeds,
											    "negative_prompt_embeds": negative_prompt_embeds,
											    "generator": generator,
											    "num_inference_steps": num_inference_steps,
											    "output_type": output_type,
											}

											if image is not None:
																	__snake_case				=		image

											if mask_image is not None:
																	__snake_case				=		mask_image

											if original_image is not None:
																	__snake_case				=		original_image

											__snake_case				=		pipe_loaded(**SCREAMING_SNAKE_CASE						)[0]

											__snake_case				=		np.abs(to_np(SCREAMING_SNAKE_CASE						) - to_np(SCREAMING_SNAKE_CASE						)						).max()
											self.assertLess(SCREAMING_SNAKE_CASE     ,    1e-4						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.get_dummy_components()
											__snake_case				=		self.pipeline_class(**SCREAMING_SNAKE_CASE						)
											pipe.to(SCREAMING_SNAKE_CASE						)
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

											__snake_case				=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE						)
											__snake_case				=		pipe(**SCREAMING_SNAKE_CASE						)[0]

											with tempfile.TemporaryDirectory() as tmpdir:
																	pipe.save_pretrained(SCREAMING_SNAKE_CASE						)
																	__snake_case				=		self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE						)
																	pipe_loaded.to(SCREAMING_SNAKE_CASE						)
																	pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

											pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

											__snake_case				=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE						)
											__snake_case				=		pipe_loaded(**SCREAMING_SNAKE_CASE						)[0]

											__snake_case				=		np.abs(to_np(SCREAMING_SNAKE_CASE						) - to_np(SCREAMING_SNAKE_CASE						)						).max()
											self.assertLess(SCREAMING_SNAKE_CASE     ,    1e-4						)









import os
from datetime import datetime as dt

from github import Github


A							:					Union[str, Any]     						=     [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]





def 	_lowerCAmelCase       (    )						->	str:


						'''simple docstring'''






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

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





