"""simple docstring"""




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
     _a   :	int 					=     argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
     )
     # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
     parser.add_argument(
         '--original_config_file',
         default=None,
         type=str,
         help='The YAML config file corresponding to the original architecture.',
     )
     parser.add_argument(
         '--num_in_channels',
         default=None,
         type=int,
         help='The number of input channels. If `None` number of input channels will be automatically inferred.',
     )
     parser.add_argument(
         '--scheduler_type',
         default='pndm',
         type=str,
         help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
     )
     parser.add_argument(
         '--pipeline_type',
         default=None,
         type=str,
         help=(
             'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
             '. If `None` pipeline will be automatically inferred.'
         ),
     )
     parser.add_argument(
         '--image_size',
         default=None,
         type=int,
         help=(
             'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
             ' Base. Use 768 for Stable Diffusion v2.'
         ),
     )
     parser.add_argument(
         '--prediction_type',
         default=None,
         type=str,
         help=(
             'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
             ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
         ),
     )
     parser.add_argument(
         '--extract_ema',
         action='store_true',
         help=(
             'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
             ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
             ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
         ),
     )
     parser.add_argument(
         '--upcast_attention',
         action='store_true',
         help=(
             'Whether the attention computation should always be upcasted. This is necessary when running stable'
             ' diffusion 2.1.'
         ),
     )
     parser.add_argument(
         '--from_safetensors',
         action='store_true',
         help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
     )
     parser.add_argument(
         '--to_safetensors',
         action='store_true',
         help='Whether to store pipeline in safetensors format or not.',
     )
     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
     parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
     parser.add_argument(
         '--stable_unclip',
         type=str,
         default=None,
         required=False,
         help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
     )
     parser.add_argument(
         '--stable_unclip_prior',
         type=str,
         default=None,
         required=False,
         help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
     )
     parser.add_argument(
         '--clip_stats_path',
         type=str,
         help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
         required=False,
     )
     parser.add_argument(
         '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
     )
     parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
     parser.add_argument(
         '--vae_path',
         type=str,
         default=None,
         required=False,
         help='Set to a path, hub id to an already converted vae to not convert it again.',
     )
     _a   :	Dict 					=     parser.parse_args()

     _a   :	List[str] 					=     download_from_original_stable_diffusion_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         prediction_type=args.prediction_type,
         model_type=args.pipeline_type,
         extract_ema=args.extract_ema,
         scheduler_type=args.scheduler_type,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         stable_unclip=args.stable_unclip,
         stable_unclip_prior=args.stable_unclip_prior,
         clip_stats_path=args.clip_stats_path,
         controlnet=args.controlnet,
         vae_path=args.vae_path,
     )

     if args.half:
          pipe.to(torch_dtype=torch.floataa)

     if args.controlnet:
          # only save the controlnet model
          pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
     else:
          pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Optional[int]	      =	DiTPipeline
     _UpperCamelCase							:    Union[str, Any]	      =	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	PipelineTesterMixin.required_optional_params - {
         "latents",
         "num_images_per_prompt",
         "callback",
         "callback_steps",
     }
     _UpperCamelCase							:    Dict	      =	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	False






     def 					__A						(	self						):
       torch.manual_seed(0						)
       _lowerCAmelCase		: Tuple						    =  TransformeraDModel(
           sample_size=16       ,  num_layers=2       ,  patch_size=4       ,  attention_head_dim=8       ,  num_attention_heads=2       ,  in_channels=4       ,  out_channels=8       ,  attention_bias=a__       ,  activation_fn="""gelu-approximate"""       ,  num_embeds_ada_norm=1000       ,  norm_type="""ada_norm_zero"""       ,  norm_elementwise_affine=a__       ,  )
       _lowerCAmelCase		: Optional[int]						    =  AutoencoderKL()
       _lowerCAmelCase		: Union[str, Any]						    =  DDIMScheduler()
       _lowerCAmelCase		: Optional[Any]						    =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
       return components






     def 					__A						(	self       ,  a__       ,  a__=0						):
       if str(a__						).startswith("""mps"""						):
         _lowerCAmelCase		: Any						    =  torch.manual_seed(a__						)
       else:
         _lowerCAmelCase		: Tuple						    =  torch.Generator(device=a__						).manual_seed(a__						)
       _lowerCAmelCase		: Any						    =  {
           """class_labels""": [1],
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }
       return inputs






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  """cpu"""

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_components()
       _lowerCAmelCase		: Optional[int]						    =  self.pipeline_class(**a__						)
       pipe.to(a__						)
       pipe.set_progress_bar_config(disable=a__						)

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_inputs(a__						)
       _lowerCAmelCase		: List[str]						    =  pipe(**a__						).images
       _lowerCAmelCase		: Optional[int]						    =  image[0, -3:, -3:, -1]

       self.assertEqual(image.shape       ,  (1, 16, 16, 3)						)
       _lowerCAmelCase		: List[Any]						    =  np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]						)
       _lowerCAmelCase		: Optional[int]						    =  np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(a__       ,  1e-3						)






     def 					__A						(	self						):
       self._test_inference_batch_single_identical(relax_max_difference=a__       ,  expected_max_diff=1e-3						)






     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()       ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,  )
     def 					__A						(	self						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)







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






     def 					__A						(	self						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  torch.manual_seed(0						)

       _lowerCAmelCase		: int						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: Dict						    =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
       _lowerCAmelCase		: Union[str, Any]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: Any						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=40       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"						)
         assert np.abs((expected_image - image).max()						) < 1e-2






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""						)
       _lowerCAmelCase		: Dict						    =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: List[str]						    =  ["""vase""", """umbrella"""]
       _lowerCAmelCase		: Optional[int]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: str						    =  torch.manual_seed(0						)
       _lowerCAmelCase		: List[str]						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=25       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             F"/dit/{word}_512.npy"						)

         assert np.abs((expected_image - image).max()						) < 1e-1




