'''simple docstring'''




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class     a__      (   UpperCAmelCase__							,   UpperCAmelCase__							,   UpperCAmelCase__							,   unittest.TestCase ):
     lowerCamelCase  :     Optional[int]							=StableDiffusionInpaintPipeline
     lowerCamelCase  :     str							=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     lowerCamelCase  :     str							=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     lowerCamelCase  :     Tuple							=frozenset(
         [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     lowerCamelCase  :     Optional[Any]							=frozenset([] )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         torch.manual_seed(0    )
         __lowerCamelCase		=  UNetaDConditionModel(
             block_out_channels=(32, 64)  ,				layers_per_block=2  ,				sample_size=32  ,				in_channels=9  ,				out_channels=4  ,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,				cross_attention_dim=32  ,				attention_head_dim=(2, 4)  ,				use_linear_projection=_SCREAMING_SNAKE_CASE  ,				)
         __lowerCamelCase		=  PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE    )
         torch.manual_seed(0    )
         __lowerCamelCase		=  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  ,				sample_size=1_28  ,				)
         torch.manual_seed(0    )
         __lowerCamelCase		=  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  ,				hidden_act='''gelu'''  ,				projection_dim=5_12  ,				)
         __lowerCamelCase		=  CLIPTextModel(_SCREAMING_SNAKE_CASE    )
         __lowerCamelCase		=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

         __lowerCamelCase		=  {
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		List[str]  ,				a		:		Dict=0    ):





         """simple docstring"""



         __lowerCamelCase		=  floats_tensor((1, 3, 32, 32)  ,				rng=random.Random(_SCREAMING_SNAKE_CASE    )    ).to(_SCREAMING_SNAKE_CASE    )
         __lowerCamelCase		=  image.cpu().permute(0  ,				2  ,				3  ,				1    )[0]
         __lowerCamelCase		=  Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE    )    ).convert('''RGB'''    ).resize((64, 64)    )
         __lowerCamelCase		=  Image.fromarray(np.uinta(image + 4    )    ).convert('''RGB'''    ).resize((64, 64)    )
         if str(_SCREAMING_SNAKE_CASE    ).startswith('''mps'''    ):
             __lowerCamelCase		=  torch.manual_seed(_SCREAMING_SNAKE_CASE    )
         else:
             __lowerCamelCase		=  torch.Generator(device=_SCREAMING_SNAKE_CASE    ).manual_seed(_SCREAMING_SNAKE_CASE    )
         __lowerCamelCase		=  {
             """prompt""": """A painting of a squirrel eating a burger""",
             """image""": init_image,
             """mask_image""": mask_image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
         }
         return inputs






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         __lowerCamelCase		=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  StableDiffusionInpaintPipeline(**_SCREAMING_SNAKE_CASE    )
         __lowerCamelCase		=  sd_pipe.to(_SCREAMING_SNAKE_CASE    )
         sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

         __lowerCamelCase		=  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )
         __lowerCamelCase		=  sd_pipe(**_SCREAMING_SNAKE_CASE    ).images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         __lowerCamelCase		=  np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]    )

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         super().test_inference_batch_single_identical(expected_max_diff=3e-3    )




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






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





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		int    ):





         """simple docstring"""



         __lowerCamelCase		=  load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-inpaint/init_image.png'''    )
         __lowerCamelCase		=  load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''    )
         __lowerCamelCase		=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
             '''/yellow_cat_sitting_on_a_park_bench.npy'''    )

         __lowerCamelCase		=  """stabilityai/stable-diffusion-2-inpainting"""
         __lowerCamelCase		=  StableDiffusionInpaintPipeline.from_pretrained(_SCREAMING_SNAKE_CASE  ,				safety_checker=_SCREAMING_SNAKE_CASE    )
         pipe.to(_SCREAMING_SNAKE_CASE    )
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )
         pipe.enable_attention_slicing()

         __lowerCamelCase		=  """Face of a yellow cat, high resolution, sitting on a park bench"""

         __lowerCamelCase		=  torch.manual_seed(0    )
         __lowerCamelCase		=  pipe(
             prompt=_SCREAMING_SNAKE_CASE  ,				image=_SCREAMING_SNAKE_CASE  ,				mask_image=_SCREAMING_SNAKE_CASE  ,				generator=_SCREAMING_SNAKE_CASE  ,				output_type='''np'''  ,				)
         __lowerCamelCase		=  output.images[0]

         assert image.shape == (5_12, 5_12, 3)
         assert np.abs(expected_image - image    ).max() < 9e-3






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         __lowerCamelCase		=  load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-inpaint/init_image.png'''    )
         __lowerCamelCase		=  load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''    )
         __lowerCamelCase		=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
             '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''    )

         __lowerCamelCase		=  """stabilityai/stable-diffusion-2-inpainting"""
         __lowerCamelCase		=  StableDiffusionInpaintPipeline.from_pretrained(
             _SCREAMING_SNAKE_CASE  ,				torch_dtype=torch.floataa  ,				safety_checker=_SCREAMING_SNAKE_CASE  ,				)
         pipe.to(_SCREAMING_SNAKE_CASE    )
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )
         pipe.enable_attention_slicing()

         __lowerCamelCase		=  """Face of a yellow cat, high resolution, sitting on a park bench"""

         __lowerCamelCase		=  torch.manual_seed(0    )
         __lowerCamelCase		=  pipe(
             prompt=_SCREAMING_SNAKE_CASE  ,				image=_SCREAMING_SNAKE_CASE  ,				mask_image=_SCREAMING_SNAKE_CASE  ,				generator=_SCREAMING_SNAKE_CASE  ,				output_type='''np'''  ,				)
         __lowerCamelCase		=  output.images[0]

         assert image.shape == (5_12, 5_12, 3)
         assert np.abs(expected_image - image    ).max() < 5e-1






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __lowerCamelCase		=  load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-inpaint/init_image.png'''    )
         __lowerCamelCase		=  load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''    )

         __lowerCamelCase		=  """stabilityai/stable-diffusion-2-inpainting"""
         __lowerCamelCase		=  PNDMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE  ,				subfolder='''scheduler'''    )
         __lowerCamelCase		=  StableDiffusionInpaintPipeline.from_pretrained(
             _SCREAMING_SNAKE_CASE  ,				safety_checker=_SCREAMING_SNAKE_CASE  ,				scheduler=_SCREAMING_SNAKE_CASE  ,				torch_dtype=torch.floataa  ,				)
         pipe.to(_SCREAMING_SNAKE_CASE    )
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )
         pipe.enable_attention_slicing(1    )
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase		=  """Face of a yellow cat, high resolution, sitting on a park bench"""

         __lowerCamelCase		=  torch.manual_seed(0    )
         __lowerCamelCase		=  pipe(
             prompt=_SCREAMING_SNAKE_CASE  ,				image=_SCREAMING_SNAKE_CASE  ,				mask_image=_SCREAMING_SNAKE_CASE  ,				generator=_SCREAMING_SNAKE_CASE  ,				num_inference_steps=2  ,				output_type='''np'''  ,				)

         __lowerCamelCase		=  torch.cuda.max_memory_allocated()
         # make sure that less than 2.65 GB is allocated
         assert mem_bytes < 2.65 * 10**9





"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A:   Optional[int]								=		logging.get_logger(__name__)

A:   Optional[int]								=		torch.device("cpu")

def       _snake_case						(						):
 UpperCAmelCase	:					List[Any]	=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase	:					Tuple	=	Image.open(requests.get(UpperCamelCase			,		stream=UpperCamelCase						).raw						)
 return im

def       _snake_case						(						UpperCamelCase						:		int						):
 if swiftformer_name == "swiftformer_xs":
  return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]						)

 elif swiftformer_name == "swiftformer_s":
  return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]						)

 elif swiftformer_name == "swiftformer_l1":
  return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]						)

 elif swiftformer_name == "swiftformer_l3":
  return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]						)

def       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		List[str]			,		UpperCamelCase						:		str						):
 UpperCAmelCase	:					int	=	dct.pop(UpperCamelCase						)
 UpperCAmelCase	:					Any	=	val

def       _snake_case						(						UpperCamelCase						:		Union[str, Any]						):
 UpperCAmelCase	:					Optional[int]	=	[]
 for k in state_dict.keys():
  UpperCAmelCase	:					Optional[Any]	=	k
  if ".pwconv" in k:
   UpperCAmelCase	:					int	=	k_new.replace(""".pwconv"""			,		""".point_wise_conv"""						)
  if ".dwconv" in k:
   UpperCAmelCase	:					Tuple	=	k_new.replace(""".dwconv"""			,		""".depth_wise_conv"""						)
  if ".Proj." in k:
   UpperCAmelCase	:					List[Any]	=	k_new.replace(""".Proj."""			,		""".proj."""						)
  if "patch_embed" in k_new:
   UpperCAmelCase	:					Any	=	k_new.replace("""patch_embed"""			,		"""swiftformer.patch_embed.patch_embedding"""						)
  if "network" in k_new:
   UpperCAmelCase	:					int	=	k_new.split("""."""						)
   if ls[2].isdigit():
    UpperCAmelCase	:					List[Any]	=	"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]						)
   else:
    UpperCAmelCase	:					Any	=	k_new.replace("""network"""			,		"""swiftformer.encoder.network"""						)
  rename_keys.append((k, k_new)						)
 return rename_keys

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		int			,		UpperCamelCase						:		Optional[int]						):
 UpperCAmelCase	:					List[Any]	=	SwiftFormerConfig()

 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 UpperCAmelCase	:					Optional[Any]	=	1000
 UpperCAmelCase	:					Tuple	=	"""huggingface/label-files"""
 UpperCAmelCase	:					List[str]	=	"""imagenet-1k-id2label.json"""
 UpperCAmelCase	:					Dict	=	json.load(open(hf_hub_download(UpperCamelCase			,		UpperCamelCase			,		repo_type="""dataset"""						)			,		"""r"""						)						)
 UpperCAmelCase	:					Dict	=	{int(UpperCamelCase						): v for k, v in idalabel.items()}
 UpperCAmelCase	:					str	=	idalabel
 UpperCAmelCase	:					Dict	=	{v: k for k, v in idalabel.items()}

 # size of the architecture
 if swiftformer_name == "swiftformer_xs":
  UpperCAmelCase	:					Any	=	[3, 3, 6, 4]
  UpperCAmelCase	:					List[str]	=	[48, 56, 112, 220]

 elif swiftformer_name == "swiftformer_s":
  UpperCAmelCase	:					Dict	=	[3, 3, 9, 6]
  UpperCAmelCase	:					Union[str, Any]	=	[48, 64, 168, 224]

 elif swiftformer_name == "swiftformer_l1":
  UpperCAmelCase	:					int	=	[4, 3, 10, 5]
  UpperCAmelCase	:					Optional[int]	=	[48, 96, 192, 384]

 elif swiftformer_name == "swiftformer_l3":
  UpperCAmelCase	:					Union[str, Any]	=	[4, 4, 12, 6]
  UpperCAmelCase	:					List[Any]	=	[64, 128, 320, 512]

 # load state_dict of original model, remove and rename some keys
 if original_ckpt:
  if original_ckpt.startswith("""https"""						):
   UpperCAmelCase	:					List[str]	=	torch.hub.load_state_dict_from_url(UpperCamelCase			,		map_location="""cpu"""			,		check_hash=UpperCamelCase						)
  else:
   UpperCAmelCase	:					Any	=	torch.load(UpperCamelCase			,		map_location="""cpu"""						)
 UpperCAmelCase	:					Optional[Any]	=	checkpoint

 UpperCAmelCase	:					Dict	=	create_rename_keys(UpperCamelCase						)
 for rename_key_src, rename_key_dest in rename_keys:
  rename_key(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)

 # load HuggingFace model
 UpperCAmelCase	:					List[Any]	=	SwiftFormerForImageClassification(UpperCamelCase						).eval()
 hf_model.load_state_dict(UpperCamelCase						)

 # prepare test inputs
 UpperCAmelCase	:					Dict	=	prepare_img()
 UpperCAmelCase	:					Tuple	=	ViTImageProcessor.from_pretrained("""preprocessor_config"""						)
 UpperCAmelCase	:					Optional[int]	=	processor(images=UpperCamelCase			,		return_tensors="""pt"""						)

 # compare outputs from both models
 UpperCAmelCase	:					Optional[int]	=	get_expected_output(UpperCamelCase						)
 UpperCAmelCase	:					List[str]	=	hf_model(inputs["""pixel_values"""]						).logits

 assert hf_logits.shape == torch.Size([1, 1000]						)
 assert torch.allclose(hf_logits[0, 0:5]			,		UpperCamelCase			,		atol=1e-3						)

 Path(UpperCamelCase						).mkdir(exist_ok=UpperCamelCase						)
 print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"						)
 hf_model.save_pretrained(UpperCamelCase						)


if __name__ == "__main__":
      A:   List[str]								=		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--swiftformer_name",
          default="swiftformer_xs",
          choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
          type=str,
          help="Name of the SwiftFormer model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default="./converted_outputs/",
          type=str,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

      A:   str								=		parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
