'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class     a__      (   UpperCAmelCase__							,   unittest.TestCase ):
     lowerCamelCase  :     Optional[int]							=KandinskyInpaintPipeline
     lowerCamelCase  :     Optional[int]							=['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
     lowerCamelCase  :     Union[str, Any]							=[
         'prompt',
         'negative_prompt',
         'image_embeds',
         'negative_image_embeds',
         'image',
         'mask_image',
     ]
     lowerCamelCase  :     Optional[int]							=[
         'generator',
         'height',
         'width',
         'latents',
         'guidance_scale',
         'negative_prompt',
         'num_inference_steps',
         'return_dict',
         'guidance_scale',
         'num_images_per_prompt',
         'output_type',
         'return_dict',
     ]
     lowerCamelCase  :     str							=False






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





         """simple docstring"""



         return 32






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         return 32






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         return self.time_input_dim






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         return self.time_input_dim * 4






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





         """simple docstring"""



         return 1_00






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''    )
         return tokenizer






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         torch.manual_seed(0    )
         __lowerCamelCase		=  MCLIPConfig(
             numDims=self.cross_attention_dim  ,				transformerDimensions=self.text_embedder_hidden_size  ,				hidden_size=self.text_embedder_hidden_size  ,				intermediate_size=37  ,				num_attention_heads=4  ,				num_hidden_layers=5  ,				vocab_size=10_05  ,				)

         __lowerCamelCase		=  MultilingualCLIP(_SCREAMING_SNAKE_CASE    )
         __lowerCamelCase		=  text_encoder.eval()

         return text_encoder






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		List[str]    ):





         """simple docstring"""



         torch.manual_seed(0    )

         __lowerCamelCase		=  {
             """in_channels""": 9,
             # Out channels is double in channels because predicts mean and variance
             """out_channels""": 8,
             """addition_embed_type""": """text_image""",
             """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
             """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
             """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
             """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
             """layers_per_block""": 1,
             """encoder_hid_dim""": self.text_embedder_hidden_size,
             """encoder_hid_dim_type""": """text_image_proj""",
             """cross_attention_dim""": self.cross_attention_dim,
             """attention_head_dim""": 4,
             """resnet_time_scale_shift""": """scale_shift""",
             """class_embed_type""": None,
         }

         __lowerCamelCase		=  UNetaDConditionModel(**_SCREAMING_SNAKE_CASE    )
         return model






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		int    ):





         """simple docstring"""



         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         torch.manual_seed(0    )
         __lowerCamelCase		=  VQModel(**self.dummy_movq_kwargs    )
         return model






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  self.dummy_text_encoder
         __lowerCamelCase		=  self.dummy_tokenizer
         __lowerCamelCase		=  self.dummy_unet
         __lowerCamelCase		=  self.dummy_movq

         __lowerCamelCase		=  DDIMScheduler(
             num_train_timesteps=10_00  ,				beta_schedule='''linear'''  ,				beta_start=0.0_00_85  ,				beta_end=0.0_12  ,				clip_sample=_SCREAMING_SNAKE_CASE  ,				set_alpha_to_one=_SCREAMING_SNAKE_CASE  ,				steps_offset=1  ,				prediction_type='''epsilon'''  ,				thresholding=_SCREAMING_SNAKE_CASE  ,				)

         __lowerCamelCase		=  {
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """unet""": unet,
             """scheduler""": scheduler,
             """movq""": movq,
         }

         return components






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple  ,				a		:		Tuple  ,				a		:		Union[str, Any]=0    ):





         """simple docstring"""



         __lowerCamelCase		=  floats_tensor((1, self.cross_attention_dim)  ,				rng=random.Random(_SCREAMING_SNAKE_CASE    )    ).to(_SCREAMING_SNAKE_CASE    )
         __lowerCamelCase		=  floats_tensor((1, self.cross_attention_dim)  ,				rng=random.Random(seed + 1    )    ).to(_SCREAMING_SNAKE_CASE    )
         # create init_image
         __lowerCamelCase		=  floats_tensor((1, 3, 64, 64)  ,				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((2_56, 2_56)    )
         # create mask
         __lowerCamelCase		=  np.ones((64, 64)  ,				dtype=np.floataa    )
         __lowerCamelCase		=  0

         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""": """horse""",
             """image""": init_image,
             """mask_image""": mask,
             """image_embeds""": image_embeds,
             """negative_image_embeds""": negative_image_embeds,
             """generator""": generator,
             """height""": 64,
             """width""": 64,
             """num_inference_steps""": 2,
             """guidance_scale""": 4.0,
             """output_type""": """np""",
         }
         return inputs






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __lowerCamelCase		=  """cpu"""

         __lowerCamelCase		=  self.get_dummy_components()

         __lowerCamelCase		=  self.pipeline_class(**_SCREAMING_SNAKE_CASE    )
         __lowerCamelCase		=  pipe.to(_SCREAMING_SNAKE_CASE    )

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

         __lowerCamelCase		=  pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )    )
         __lowerCamelCase		=  output.images

         __lowerCamelCase		=  pipe(
             **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )  ,				return_dict=_SCREAMING_SNAKE_CASE  ,				)[0]

         __lowerCamelCase		=  image[0, -3:, -3:, -1]
         __lowerCamelCase		=  image_from_tuple[0, -3:, -3:, -1]

         print(f"""image.shape {image.shape}"""    )

         assert image.shape == (1, 64, 64, 3)

         __lowerCamelCase		=  np.array(
             [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]    )

         assert (
             np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
         ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
         ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






     def    SCREAMING_SNAKE_CASE__   ( self		:		int    ):





         """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		:		List[Any]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''    )

         __lowerCamelCase		=  load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''    )
         __lowerCamelCase		=  np.ones((7_68, 7_68)  ,				dtype=np.floataa    )
         __lowerCamelCase		=  0

         __lowerCamelCase		=  """a hat"""

         __lowerCamelCase		=  KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''  ,				torch_dtype=torch.floataa    )
         pipe_prior.to(_SCREAMING_SNAKE_CASE    )

         __lowerCamelCase		=  KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint'''  ,				torch_dtype=torch.floataa    )
         __lowerCamelCase		=  pipeline.to(_SCREAMING_SNAKE_CASE    )
         pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

         __lowerCamelCase		=  torch.Generator(device='''cpu'''    ).manual_seed(0    )
         __lowerCamelCase		=  pipe_prior(
             _SCREAMING_SNAKE_CASE  ,				generator=_SCREAMING_SNAKE_CASE  ,				num_inference_steps=5  ,				negative_prompt=''''''  ,				).to_tuple()

         __lowerCamelCase		=  pipeline(
             _SCREAMING_SNAKE_CASE  ,				image=_SCREAMING_SNAKE_CASE  ,				mask_image=_SCREAMING_SNAKE_CASE  ,				image_embeds=_SCREAMING_SNAKE_CASE  ,				negative_image_embeds=_SCREAMING_SNAKE_CASE  ,				generator=_SCREAMING_SNAKE_CASE  ,				num_inference_steps=1_00  ,				height=7_68  ,				width=7_68  ,				output_type='''np'''  ,				)

         __lowerCamelCase		=  output.images[0]

         assert image.shape == (7_68, 7_68, 3)

         assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE    )





"""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)
