




import argparse
import json
import subprocess







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Optional[Any]:



       """simple docstring"""

       A__ 					=       []

       A__ 					=       (
           f"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
           ''' https://api.github.com/repos/huggingface/transformers/actions/runners'''
       )
       A__ 					=       subprocess.run(lowercase_					,							shell=lowercase_					,							stdout=subprocess.PIPE		)
       A__ 					=       output.stdout.decode('''utf-8'''		)
       A__ 					=       json.loads(lowercase_		)

       A__ 					=       status['''runners''']
       for runner in runners:
              if runner["name"] in target_runners:
                     if runner["status"] == "offline":
                            offline_runners.append(lowercase_		)

    # save the result so we can report them on Slack
       with open('''offline_runners.txt'''					,							'''w'''		) as fp:
              fp.write(json.dumps(lowercase_		)		)

       if len(lowercase_		) > 0:
              A__ 					=       '''\n'''.join([x['''name'''] for x in offline_runners]		)
              raise ValueError(f"""The following runners are offline:\n{failed}"""		)


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Any:



         """simple docstring"""

         return values.split(''','''		)

  _lowerCamelCase      :						Any							=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--target_runners""",
      default=None,
      type=list_str,
      required=True,
      help="""Comma-separated list of runners to check status.""",
  )

  parser.add_argument(
      """--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
  )
  _lowerCamelCase      :						Optional[Any]							=  parser.parse_args()
  get_runner_status(args.target_runners, args.token)








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  :







						'''simple docstring'''





						def 			snake_case__		(		self				:	List[Any]		):






							"""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=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		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      thresholding=__lowercase			,      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 			snake_case__		(		self				:	List[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=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=1.414			,      time_embedding_act_fn="gelu"			,      time_embedding_dim=32			,      )
							unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      thresholding=__lowercase			,      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=10_00			,      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 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	self.get_dummy_components()
							snake_case_						       =	self.pipeline_class(**__lowercase		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)

							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(__lowercase		)

							# 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(__lowercase			,      __lowercase			,      __lowercase		)

							snake_case_						       =	pipe(**__lowercase		)[0]

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

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

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

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)

							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(**__lowercase		)[0]

							snake_case_						       =	np.abs(to_np(__lowercase		) - to_np(__lowercase		)		).max()
							self.assertLess(__lowercase			,      1E-4		)






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.get_dummy_components()
							snake_case_						       =	self.pipeline_class(**__lowercase		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)
							snake_case_						       =	pipe(**__lowercase		)[0]

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

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

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)
							snake_case_						       =	pipe_loaded(**__lowercase		)[0]

							snake_case_						       =	np.abs(to_np(__lowercase		) - to_np(__lowercase		)		).max()
							self.assertLess(__lowercase			,      1E-4		)
