



"""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:     Optional[Any]										=	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:     Any										=	parser.parse_args()

      A:     List[Any]										=	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 inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     int		=		PriorTransformer
   __lowerCAmelCase						:     Dict		=		'hidden_states'

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			4
    UpperCAmelCase			:      Optional[Any]			=			8
    UpperCAmelCase			:      Union[str, Any]			=			7

    UpperCAmelCase			:      Any			=			floats_tensor((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			floats_tensor((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			floats_tensor((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    return {
        "hidden_states": hidden_states,
        "timestep": 2,
        "proj_embedding": proj_embedding,
        "encoder_hidden_states": encoder_hidden_states,
    }

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=0		)						->   Any:





    '''simple docstring'''




    torch.manual_seed(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			4
    UpperCAmelCase			:      Any			=			8
    UpperCAmelCase			:      List[Any]			=			7

    UpperCAmelCase			:      Any			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			torch.randn((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    return {
        "hidden_states": hidden_states,
        "timestep": 2,
        "proj_embedding": proj_embedding,
        "encoder_hidden_states": encoder_hidden_states,
    }

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    return (4, 8)

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    return (4, 8)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			{
        """num_attention_heads""": 2,
        """attention_head_dim""": 4,
        """num_layers""": 2,
        """embedding_dim""": 8,
        """num_embeddings""": 7,
        """additional_embeddings""": 4,
    }
    UpperCAmelCase			:      Tuple			=			self.dummy_input
    return init_dict, inputs_dict

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Optional[Any]			=			PriorTransformer.from_pretrained(
        """hf-internal-testing/prior-dummy"""   ,  output_loading_info=_SCREAMING_SNAKE_CASE		)
    self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
    self.assertEqual(len(loading_info["""missing_keys"""]		)   ,  0		)

    model.to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			model(**self.dummy_input		)[0]

    assert hidden_states is not None, "Make sure output is not None"

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Tuple			=			self.prepare_init_args_and_inputs_for_common()

    UpperCAmelCase			:      str			=			self.model_class(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			inspect.signature(model.forward		)
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    UpperCAmelCase			:      Union[str, Any]			=			[*signature.parameters.keys()]

    UpperCAmelCase			:      int			=			["""hidden_states""", """timestep"""]
    self.assertListEqual(arg_names[:2]   ,  _SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""		)
    UpperCAmelCase			:      Dict			=			model.to(_SCREAMING_SNAKE_CASE		)

    if hasattr(_SCREAMING_SNAKE_CASE   ,  """set_default_attn_processor"""		):
     model.set_default_attn_processor()

    UpperCAmelCase			:      str			=			self.get_dummy_seed_input()

    with torch.no_grad():
     UpperCAmelCase			:      str			=			model(**_SCREAMING_SNAKE_CASE		)[0]

    UpperCAmelCase			:      Dict			=			output[0, :5].flatten().cpu()
    print(_SCREAMING_SNAKE_CASE		)

    # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
    # the expected output slices are not the same for CPU and GPU.
    UpperCAmelCase			:      Dict			=			torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]		)
    self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  rtol=1E-2		)		)



@slow
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=1   ,  _SCREAMING_SNAKE_CASE=768   ,  _SCREAMING_SNAKE_CASE=77   ,  _SCREAMING_SNAKE_CASE=0		)						->   int:





    '''simple docstring'''




    torch.manual_seed(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			batch_size
    UpperCAmelCase			:      int			=			embedding_dim
    UpperCAmelCase			:      Tuple			=			num_embeddings

    UpperCAmelCase			:      Optional[Any]			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			torch.randn((batch_size, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			torch.randn((batch_size, num_embeddings, embedding_dim)		).to(_SCREAMING_SNAKE_CASE		)

    return {
        "hidden_states": hidden_states,
        "timestep": 2,
        "proj_embedding": proj_embedding,
        "encoder_hidden_states": encoder_hidden_states,
    }

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
           [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
           # fmt: on
       ]		)
   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""   ,  subfolder="""prior"""		)
    model.to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			self.get_dummy_seed_input(seed=_SCREAMING_SNAKE_CASE		)

    with torch.no_grad():
     UpperCAmelCase			:      Any			=			model(**_SCREAMING_SNAKE_CASE		)[0]

    assert list(sample.shape		) == [1, 768]

    UpperCAmelCase			:      int			=			sample[0, :8].flatten().cpu()
    print(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[int]			=			torch.tensor(_SCREAMING_SNAKE_CASE		)

    assert torch_all_close(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  atol=1E-3		)
