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    A_ :

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   torch.manual_seed(0       )
   __a       =  TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"       )

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

   torch.manual_seed(0       )
   __a       =  UNetaDConditionModel(
       sample_size=32     ,							layers_per_block=1     ,							block_out_channels=[32, 64]     ,							down_block_types=[
           "ResnetDownsampleBlock2D",
           "SimpleCrossAttnDownBlock2D",
       ]     ,							mid_block_type="UNetMidBlock2DSimpleCrossAttn"     ,							up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]     ,							in_channels=3     ,							out_channels=6     ,							cross_attention_dim=32     ,							encoder_hid_dim=32     ,							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       )
   __a       =  DDPMScheduler(
       num_train_timesteps=10_00     ,							beta_schedule="squaredcos_cap_v2"     ,							beta_start=0.00_01     ,							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       )
   __a       =  IFWatermarker()

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

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   torch.manual_seed(0       )
   __a       =  TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"       )

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

   torch.manual_seed(0       )
   __a       =  UNetaDConditionModel(
       sample_size=32     ,							layers_per_block=[1, 2]     ,							block_out_channels=[32, 64]     ,							down_block_types=[
           "ResnetDownsampleBlock2D",
           "SimpleCrossAttnDownBlock2D",
       ]     ,							mid_block_type="UNetMidBlock2DSimpleCrossAttn"     ,							up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]     ,							in_channels=6     ,							out_channels=6     ,							cross_attention_dim=32     ,							encoder_hid_dim=32     ,							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=1.4_14     ,							time_embedding_act_fn="gelu"     ,							time_embedding_dim=32     ,							)
   unet.set_attn_processor(AttnAddedKVProcessor()       )  # For reproducibility tests

   torch.manual_seed(0       )
   __a       =  DDPMScheduler(
       num_train_timesteps=10_00     ,							beta_schedule="squaredcos_cap_v2"     ,							beta_start=0.00_01     ,							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       )
   __a       =  DDPMScheduler(
       num_train_timesteps=10_00     ,							beta_schedule="squaredcos_cap_v2"     ,							beta_start=0.00_01     ,							beta_end=0.02     ,							)

   torch.manual_seed(0       )
   __a       =  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 		_UpperCAmelCase   (		self :      str       ):
   __a       =  self.get_dummy_components()
   __a       =  self.pipeline_class(**__SCREAMING_SNAKE_CASE       )
   pipe.to(__SCREAMING_SNAKE_CASE       )
   pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )

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

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

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

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

   __a       ,     __a       =  pipe.encode_prompt(__SCREAMING_SNAKE_CASE       )

   # inputs with prompt converted to embeddings
   __a       =  {
       "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:
    __a       =  image

   if mask_image is not None:
    __a       =  mask_image

   if original_image is not None:
    __a       =  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       )

   __a       =  pipe(**__SCREAMING_SNAKE_CASE       )[0]

   with tempfile.TemporaryDirectory() as tmpdir:
    pipe.save_pretrained(__SCREAMING_SNAKE_CASE       )
    __a       =  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."""     ,							)

   __a       =  self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )

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

   # inputs with prompt converted to embeddings
   __a       =  {
       "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:
    __a       =  image

   if mask_image is not None:
    __a       =  mask_image

   if original_image is not None:
    __a       =  original_image

   __a       =  pipe_loaded(**__SCREAMING_SNAKE_CASE       )[0]

   __a       =  np.abs(to_np(__SCREAMING_SNAKE_CASE       ) - to_np(__SCREAMING_SNAKE_CASE       )       ).max()
   self.assertLess(__SCREAMING_SNAKE_CASE     ,							1E-4       )

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  self.get_dummy_components()
   __a       =  self.pipeline_class(**__SCREAMING_SNAKE_CASE       )
   pipe.to(__SCREAMING_SNAKE_CASE       )
   pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

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

   with tempfile.TemporaryDirectory() as tmpdir:
    pipe.save_pretrained(__SCREAMING_SNAKE_CASE       )
    __a       =  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

   __a       =  self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )
   __a       =  pipe_loaded(**__SCREAMING_SNAKE_CASE       )[0]

   __a       =  np.abs(to_np(__SCREAMING_SNAKE_CASE       ) - to_np(__SCREAMING_SNAKE_CASE       )       ).max()
   self.assertLess(__SCREAMING_SNAKE_CASE     ,							1E-4       )



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()


