





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Tuple							=    KandinskyVaaControlnetImgaImgPipeline
      A_						:				int							=    ['image_embeds', 'negative_image_embeds', 'image', 'hint']
      A_						:				Optional[Any]							=    ['image_embeds', 'negative_image_embeds', 'image', 'hint']
      A_						:				str							=    [
          'generator',
          'height',
          'width',
          'strength',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      A_						:				Optional[int]							=    False


      @property
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         return 32


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





         """simple docstring"""



         return 32


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         return self.time_input_dim


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





         """simple docstring"""



         return self.time_input_dim * 4


      @property
      def    a (self       :      str      ):





         """simple docstring"""



         return 100


      @property
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         torch.manual_seed(0      )

         __snake_case							       =   {
             """in_channels""": 8,
             # Out channels is double in channels because predicts mean and variance
             """out_channels""": 8,
             """addition_embed_type""": """image_hint""",
             """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""": """image_proj""",
             """cross_attention_dim""": self.cross_attention_dim,
             """attention_head_dim""": 4,
             """resnet_time_scale_shift""": """scale_shift""",
             """class_embed_type""": None,
         }

         __snake_case							       =   UNetaDConditionModel(**a__      )
         return model


      @property
      def    a (self       :      Any      ):





         """simple docstring"""



         return {
             "block_out_channels": [32, 32, 64, 64],
             "down_block_types": [
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
             "vq_embed_dim": 4,
         }


      @property
      def    a (self       :      Tuple      ):





         """simple docstring"""



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


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   self.dummy_unet
         __snake_case							       =   self.dummy_movq

         __snake_case							       =   {
             """num_train_timesteps""": 1000,
             """beta_schedule""": """linear""",
             """beta_start""": 0.0_0_0_8_5,
             """beta_end""": 0.0_1_2,
             """clip_sample""": False,
             """set_alpha_to_one""": False,
             """steps_offset""": 0,
             """prediction_type""": """epsilon""",
             """thresholding""": False,
         }

         __snake_case							       =   DDIMScheduler(**a__      )

         __snake_case							       =   {
             """unet""": unet,
             """scheduler""": scheduler,
             """movq""": movq,
         }

         return components


      def    a (self       :      Optional[int]				,			a__       :      Union[str, Any]				,			a__       :      Optional[int]=0      ):





         """simple docstring"""



         __snake_case							       =   floats_tensor((1, self.text_embedder_hidden_size)				,			rng=random.Random(a__      )      ).to(a__      )
         __snake_case							       =   floats_tensor((1, self.text_embedder_hidden_size)				,			rng=random.Random(seed + 1      )      ).to(
             a__      )
         # create init_image
         __snake_case							       =   floats_tensor((1, 3, 64, 64)				,			rng=random.Random(a__      )      ).to(a__      )
         __snake_case							       =   image.cpu().permute(0				,			2				,			3				,			1      )[0]
         __snake_case							       =   Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((256, 256)      )
         # create hint
         __snake_case							       =   floats_tensor((1, 3, 64, 64)				,			rng=random.Random(a__      )      ).to(a__      )

         if str(a__      ).startswith('''mps'''      ):
            __snake_case							       =   torch.manual_seed(a__      )
         else:
            __snake_case							       =   torch.Generator(device=a__      ).manual_seed(a__      )
         __snake_case							       =   {
             """image""": init_image,
             """image_embeds""": image_embeds,
             """negative_image_embeds""": negative_image_embeds,
             """hint""": hint,
             """generator""": generator,
             """height""": 64,
             """width""": 64,
             """num_inference_steps""": 10,
             """guidance_scale""": 7.0,
             """strength""": 0.2,
             """output_type""": """np""",
         }
         return inputs


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   """cpu"""

         __snake_case							       =   self.get_dummy_components()

         __snake_case							       =   self.pipeline_class(**a__      )
         __snake_case							       =   pipe.to(a__      )

         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   pipe(**self.get_dummy_inputs(a__      )      )
         __snake_case							       =   output.images

         __snake_case							       =   pipe(
             **self.get_dummy_inputs(a__      )				,			return_dict=a__				,			)[0]

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

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

         __snake_case							       =   np.array(
             [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]      )
         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()}"""






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


      def    a (self       :      List[str]      ):





         """simple docstring"""



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


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''      )

         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
         __snake_case							       =   init_image.resize((512, 512)      )

         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinskyv22/hint_image_cat.png'''      )
         __snake_case							       =   torch.from_numpy(np.array(a__      )      ).float() / 255.0
         __snake_case							       =   hint.permute(2				,			0				,			1      ).unsqueeze(0      )

         __snake_case							       =   """A robot, 4k photo"""

         __snake_case							       =   KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-prior'''				,			torch_dtype=torch.floataa      )
         pipe_prior.to(a__      )

         __snake_case							       =   KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-controlnet-depth'''				,			torch_dtype=torch.floataa      )
         __snake_case							       =   pipeline.to(a__      )

         pipeline.set_progress_bar_config(disable=a__      )

         __snake_case							       =   torch.Generator(device='''cpu'''      ).manual_seed(0      )

         __snake_case							       =   pipe_prior(
             a__				,			image=a__				,			strength=0.8_5				,			generator=a__				,			negative_prompt=''''''				,			).to_tuple()

         __snake_case							       =   pipeline(
             image=a__				,			image_embeds=a__				,			negative_image_embeds=a__				,			hint=a__				,			generator=a__				,			num_inference_steps=100				,			height=512				,			width=512				,			strength=0.5				,			output_type='''np'''				,			)

         __snake_case							       =   output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(a__				,			a__      )






'''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   :			List[Any]					  =    logging.get_logger(__name__)

a   :			Union[str, Any]					  =    torch.device("""cpu""")



def       __lowerCamelCase  (					)      ->			Any:
							UpperCAmelCase     :		Optional[Any]							     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase     :		Dict							     =		Image.open(requests.get(_lowercase	,				stream=_lowercase		).raw		)
							return im



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]		)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]		)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]		)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			str:
							UpperCAmelCase     :		Union[str, Any]							     =		dct.pop(_lowercase		)
							UpperCAmelCase     :		str							     =		val



def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		Tuple							     =		[]
							for k in state_dict.keys():
														UpperCAmelCase     :		Dict							     =		k
														if ".pwconv" in k:
																					UpperCAmelCase     :		Union[str, Any]							     =		k_new.replace(""".pwconv"""	,				""".point_wise_conv"""		)
														if ".dwconv" in k:
																					UpperCAmelCase     :		Dict							     =		k_new.replace(""".dwconv"""	,				""".depth_wise_conv"""		)
														if ".Proj." in k:
																					UpperCAmelCase     :		str							     =		k_new.replace(""".Proj."""	,				""".proj."""		)
														if "patch_embed" in k_new:
																					UpperCAmelCase     :		Dict							     =		k_new.replace("""patch_embed"""	,				"""swiftformer.patch_embed.patch_embedding"""		)
														if "network" in k_new:
																					UpperCAmelCase     :		Optional[Any]							     =		k_new.split("""."""		)
																					if ls[2].isdigit():
																												UpperCAmelCase     :		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       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[Any]							     =		SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							UpperCAmelCase     :		List[Any]							     =		1_0_0_0
							UpperCAmelCase     :		List[str]							     =		"""huggingface/label-files"""
							UpperCAmelCase     :		Tuple							     =		"""imagenet-1k-id2label.json"""
							UpperCAmelCase     :		Dict							     =		json.load(open(hf_hub_download(_lowercase	,				_lowercase	,				repo_type="""dataset"""		)	,				"""r"""		)		)
							UpperCAmelCase     :		Tuple							     =		{int(_lowercase		): v for k, v in idalabel.items()}
							UpperCAmelCase     :		Tuple							     =		idalabel
							UpperCAmelCase     :		List[Any]							     =		{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														UpperCAmelCase     :		List[Any]							     =		[3, 3, 6, 4]
														UpperCAmelCase     :		int							     =		[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														UpperCAmelCase     :		str							     =		[3, 3, 9, 6]
														UpperCAmelCase     :		str							     =		[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														UpperCAmelCase     :		List[Any]							     =		[4, 3, 1_0, 5]
														UpperCAmelCase     :		Union[str, Any]							     =		[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														UpperCAmelCase     :		Any							     =		[4, 4, 1_2, 6]
														UpperCAmelCase     :		List[Any]							     =		[6_4, 1_2_8, 3_2_0, 5_1_2]

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

							UpperCAmelCase     :		Tuple							     =		create_rename_keys(_lowercase		)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(_lowercase	,				_lowercase	,				_lowercase		)

							# load HuggingFace model
							UpperCAmelCase     :		str							     =		SwiftFormerForImageClassification(_lowercase		).eval()
							hf_model.load_state_dict(_lowercase		)

							# prepare test inputs
							UpperCAmelCase     :		Any							     =		prepare_img()
							UpperCAmelCase     :		List[Any]							     =		ViTImageProcessor.from_pretrained("""preprocessor_config"""		)
							UpperCAmelCase     :		List[str]							     =		processor(images=_lowercase	,				return_tensors="""pt"""		)

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

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]		)
							assert torch.allclose(hf_logits[0, 0:5]	,				_lowercase	,				atol=1e-3		)

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


if __name__ == "__main__":
							a   :			Tuple					  =    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   :			Union[str, Any]					  =    parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

