



'''simple docstring'''






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
     lowerCAmelCase: Tuple							=						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.',
     )
     lowerCAmelCase: Dict							=						parser.parse_args()

     lowerCAmelCase: 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 random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 							a__( lowerCamelCase__		,			lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   CycleDiffusionPipeline
    lowercase__			=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        """negative_prompt""",
        """height""",
        """width""",
        """negative_prompt_embeds""",
    }
    lowercase__			=   PipelineTesterMixin.required_optional_params - {"""latents"""}
    lowercase__			=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}     )
    lowercase__			=   IMAGE_TO_IMAGE_IMAGE_PARAMS
    lowercase__			=   IMAGE_TO_IMAGE_IMAGE_PARAMS







    def       lowercase_	(				self			:  Any     ):
      torch.manual_seed(0     )
      a						: Union[str, Any]					       =					UNetaDConditionModel(
          block_out_channels=(32, 64)						,      layers_per_block=2						,      sample_size=32						,      in_channels=4						,      out_channels=4						,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,      cross_attention_dim=32						,      )
      a						: str					       =					DDIMScheduler(
          beta_start=0.00085						,      beta_end=0.012						,      beta_schedule='scaled_linear'						,      num_train_timesteps=10_00						,      clip_sample=__snake_case						,      set_alpha_to_one=__snake_case						,      )
      torch.manual_seed(0     )
      a						: List[str]					       =					AutoencoderKL(
          block_out_channels=[32, 64]						,      in_channels=3						,      out_channels=3						,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,      latent_channels=4						,      )
      torch.manual_seed(0     )
      a						: int					       =					CLIPTextConfig(
          bos_token_id=0						,      eos_token_id=2						,      hidden_size=32						,      intermediate_size=37						,      layer_norm_eps=1e-0_5						,      num_attention_heads=4						,      num_hidden_layers=5						,      pad_token_id=1						,      vocab_size=10_00						,      )
      a						: List[str]					       =					CLIPTextModel(__snake_case     )
      a						: int					       =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

      a						: Tuple					       =					{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }
      return components







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Dict						,      __snake_case			:  Any=0     ):
      a						: str					       =					floats_tensor((1, 3, 32, 32)						,      rng=random.Random(__snake_case     )     ).to(__snake_case     )
      a						: Optional[Any]					       =					image / 2 + 0.5
      if str(__snake_case     ).startswith('mps'     ):
        a						: List[str]					       =					torch.manual_seed(__snake_case     )
      else:
        a						: Union[str, Any]					       =					torch.Generator(device=__snake_case     ).manual_seed(__snake_case     )
      a						: List[Any]					       =					{
          'prompt': 'An astronaut riding an elephant',
          'source_prompt': 'An astronaut riding a horse',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'eta': 0.1,
          'strength': 0.8,
          'guidance_scale': 3,
          'source_guidance_scale': 1,
          'output_type': 'numpy',
      }
      return inputs







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Optional[Any]					       =					'cpu'  # ensure determinism for the device-dependent torch.Generator

      a						: int					       =					self.get_dummy_components()
      a						: str					       =					CycleDiffusionPipeline(**__snake_case     )
      a						: List[str]					       =					pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )

      a						: Dict					       =					self.get_dummy_inputs(__snake_case     )
      a						: Union[str, Any]					       =					pipe(**__snake_case     )
      a						: List[Any]					       =					output.images

      a						: Optional[Any]					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: Tuple					       =					np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







    @unittest.skipIf(torch_device != 'cuda'						,      'This test requires a GPU'     )
    def       lowercase_	(				self			:  int     ):
      a						: List[Any]					       =					self.get_dummy_components()
      for name, module in components.items():
        if hasattr(__snake_case						,      'half'     ):
          a						: Any					       =					module.half()
      a						: Tuple					       =					CycleDiffusionPipeline(**__snake_case     )
      a						: Any					       =					pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )

      a						: str					       =					self.get_dummy_inputs(__snake_case     )
      a						: int					       =					pipe(**__snake_case     )
      a						: Optional[int]					       =					output.images

      a						: Tuple					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: int					       =					np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







    @skip_mps
    def       lowercase_	(				self			:  List[Any]     ):
      return super().test_save_load_local()







    @unittest.skip('non-deterministic pipeline'     )
    def       lowercase_	(				self			:  Dict     ):
      return super().test_inference_batch_single_identical()







    @skip_mps
    def       lowercase_	(				self			:  int     ):
      return super().test_dict_tuple_outputs_equivalent()







    @skip_mps
    def       lowercase_	(				self			:  Dict     ):
      return super().test_save_load_optional_components()







    @skip_mps
    def       lowercase_	(				self			:  List[Any]     ):
      return super().test_attention_slicing_forward_pass()

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







    def       lowercase_	(				self			:  Tuple     ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Any					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: Optional[int]					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'     )
      a						: List[str]					       =					init_image.resize((5_12, 5_12)     )

      a						: Dict					       =					'CompVis/stable-diffusion-v1-4'
      a						: List[str]					       =					DDIMScheduler.from_pretrained(__snake_case						,      subfolder='scheduler'     )
      a						: Any					       =					CycleDiffusionPipeline.from_pretrained(
          __snake_case						,      scheduler=__snake_case						,      safety_checker=__snake_case						,      torch_dtype=torch.floataa						,      revision='fp16'     )

      pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )
      pipe.enable_attention_slicing()

      a						: Union[str, Any]					       =					'A black colored car'
      a						: Optional[Any]					       =					'A blue colored car'

      a						: int					       =					torch.manual_seed(0     )
      a						: Optional[Any]					       =					pipe(
          prompt=__snake_case						,      source_prompt=__snake_case						,      image=__snake_case						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__snake_case						,      output_type='np'						,      )
      a						: Dict					       =					output.images

      # the values aren't exactly equal, but the images look the same visually
      assert np.abs(image - expected_image     ).max() < 5e-1







    def       lowercase_	(				self			:  int     ):
      a						: Any					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: List[str]					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'     )
      a						: str					       =					init_image.resize((5_12, 5_12)     )

      a						: Optional[int]					       =					'CompVis/stable-diffusion-v1-4'
      a						: Union[str, Any]					       =					DDIMScheduler.from_pretrained(__snake_case						,      subfolder='scheduler'     )
      a						: str					       =					CycleDiffusionPipeline.from_pretrained(__snake_case						,      scheduler=__snake_case						,      safety_checker=__snake_case     )

      pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )
      pipe.enable_attention_slicing()

      a						: Tuple					       =					'A black colored car'
      a						: Tuple					       =					'A blue colored car'

      a						: List[str]					       =					torch.manual_seed(0     )
      a						: str					       =					pipe(
          prompt=__snake_case						,      source_prompt=__snake_case						,      image=__snake_case						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__snake_case						,      output_type='np'						,      )
      a						: Tuple					       =					output.images

      assert np.abs(image - expected_image     ).max() < 2e-2