
import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		Any:







	'''simple docstring'''


	__magic_name__		:		int	   =     SwinvaConfig()
	__magic_name__		:		Any	   =     swinva_name.split("_"   )

	__magic_name__		:		Optional[int]	   =     name_split[1]
	if "to" in name_split[3]:
		__magic_name__		:		Optional[int]	   =     int(name_split[3][-3:]   )
	else:
		__magic_name__		:		List[str]	   =     int(name_split[3]   )
	if "to" in name_split[2]:
		__magic_name__		:		Optional[int]	   =     int(name_split[2][-2:]   )
	else:
		__magic_name__		:		int	   =     int(name_split[2][6:]   )

	if model_size == "tiny":
		__magic_name__		:		Optional[int]	   =     96
		__magic_name__		:		Any	   =     (2, 2, 6, 2)
		__magic_name__		:		Dict	   =     (3, 6, 12, 24)
	elif model_size == "small":
		__magic_name__		:		List[str]	   =     96
		__magic_name__		:		Optional[int]	   =     (2, 2, 18, 2)
		__magic_name__		:		Dict	   =     (3, 6, 12, 24)
	elif model_size == "base":
		__magic_name__		:		Optional[Any]	   =     128
		__magic_name__		:		Dict	   =     (2, 2, 18, 2)
		__magic_name__		:		Any	   =     (4, 8, 16, 32)
	else:
		__magic_name__		:		List[Any]	   =     192
		__magic_name__		:		Any	   =     (2, 2, 18, 2)
		__magic_name__		:		Any	   =     (6, 12, 24, 48)

	if "to" in swinva_name:
		__magic_name__		:		int	   =     (12, 12, 12, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		__magic_name__		:		Tuple	   =     21841
		__magic_name__		:		Union[str, Any]	   =     "huggingface/label-files"
		__magic_name__		:		List[Any]	   =     "imagenet-22k-id2label.json"
		__magic_name__		:		List[str]	   =     json.load(open(hf_hub_download(A_      ,   A_      ,   repo_type="dataset"   )      ,   "r"   )   )
		__magic_name__		:		List[Any]	   =     {int(A_   ): v for k, v in idalabel.items()}
		__magic_name__		:		Union[str, Any]	   =     idalabel
		__magic_name__		:		Tuple	   =     {v: k for k, v in idalabel.items()}

	else:
		__magic_name__		:		Union[str, Any]	   =     1000
		__magic_name__		:		Union[str, Any]	   =     "huggingface/label-files"
		__magic_name__		:		int	   =     "imagenet-1k-id2label.json"
		__magic_name__		:		Optional[Any]	   =     json.load(open(hf_hub_download(A_      ,   A_      ,   repo_type="dataset"   )      ,   "r"   )   )
		__magic_name__		:		Any	   =     {int(A_   ): v for k, v in idalabel.items()}
		__magic_name__		:		int	   =     idalabel
		__magic_name__		:		Optional[Any]	   =     {v: k for k, v in idalabel.items()}

	__magic_name__		:		str	   =     img_size
	__magic_name__		:		Union[str, Any]	   =     num_classes
	__magic_name__		:		Union[str, Any]	   =     embed_dim
	__magic_name__		:		Optional[Any]	   =     depths
	__magic_name__		:		Optional[int]	   =     num_heads
	__magic_name__		:		int	   =     window_size

	return config






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		Union[str, Any]:







	'''simple docstring'''


	if "patch_embed.proj" in name:
		__magic_name__		:		Optional[int]	   =     name.replace("patch_embed.proj"      ,   "embeddings.patch_embeddings.projection"   )
	if "patch_embed.norm" in name:
		__magic_name__		:		int	   =     name.replace("patch_embed.norm"      ,   "embeddings.norm"   )
	if "layers" in name:
		__magic_name__		:		Optional[Any]	   =     "encoder." + name
	if "attn.proj" in name:
		__magic_name__		:		Union[str, Any]	   =     name.replace("attn.proj"      ,   "attention.output.dense"   )
	if "attn" in name:
		__magic_name__		:		List[Any]	   =     name.replace("attn"      ,   "attention.self"   )
	if "norm1" in name:
		__magic_name__		:		List[str]	   =     name.replace("norm1"      ,   "layernorm_before"   )
	if "norm2" in name:
		__magic_name__		:		int	   =     name.replace("norm2"      ,   "layernorm_after"   )
	if "mlp.fc1" in name:
		__magic_name__		:		int	   =     name.replace("mlp.fc1"      ,   "intermediate.dense"   )
	if "mlp.fc2" in name:
		__magic_name__		:		Optional[Any]	   =     name.replace("mlp.fc2"      ,   "output.dense"   )
	if "q_bias" in name:
		__magic_name__		:		Dict	   =     name.replace("q_bias"      ,   "query.bias"   )
	if "k_bias" in name:
		__magic_name__		:		Any	   =     name.replace("k_bias"      ,   "key.bias"   )
	if "v_bias" in name:
		__magic_name__		:		List[str]	   =     name.replace("v_bias"      ,   "value.bias"   )
	if "cpb_mlp" in name:
		__magic_name__		:		Tuple	   =     name.replace("cpb_mlp"      ,   "continuous_position_bias_mlp"   )
	if name == "norm.weight":
		__magic_name__		:		int	   =     "layernorm.weight"
	if name == "norm.bias":
		__magic_name__		:		int	   =     "layernorm.bias"

	if "head" in name:
		__magic_name__		:		List[Any]	   =     name.replace("head"      ,   "classifier"   )
	else:
		__magic_name__		:		List[Any]	   =     "swinv2." + name

	return name






def   lowerCAmelCase_						(					_snake_case      :  int      ,   _snake_case      :  Tuple   )				->		Union[str, Any]:







	'''simple docstring'''


	for key in orig_state_dict.copy().keys():
		__magic_name__		:		List[Any]	   =     orig_state_dict.pop(A_   )

		if "mask" in key:
			continue
		elif "qkv" in key:
			__magic_name__		:		List[str]	   =     key.split("."   )
			__magic_name__		:		Optional[int]	   =     int(key_split[1]   )
			__magic_name__		:		Union[str, Any]	   =     int(key_split[3]   )
			__magic_name__		:		Tuple	   =     model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				__magic_name__		:		int	   =     val[:dim, :]
				__magic_name__		:		Union[str, Any]	   =     val[dim : dim * 2, :]
				__magic_name__		:		List[str]	   =     val[-dim:, :]
			else:
				__magic_name__		:		str	   =     val[:dim]
				__magic_name__		:		str	   =     val[
				    dim : dim * 2
				]
				__magic_name__		:		str	   =     val[-dim:]
		else:
			__magic_name__		:		List[Any]	   =     val

	return orig_state_dict






def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  int   )				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     timm.create_model(A_      ,   pretrained=A_   )
	timm_model.eval()

	__magic_name__		:		str	   =     get_swinva_config(A_   )
	__magic_name__		:		Dict	   =     SwinvaForImageClassification(A_   )
	model.eval()

	__magic_name__		:		Tuple	   =     convert_state_dict(timm_model.state_dict()      ,   A_   )
	model.load_state_dict(A_   )

	__magic_name__		:		Dict	   =     "http://images.cocodataset.org/val2017/000000039769.jpg"

	__magic_name__		:		Optional[Any]	   =     AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"      ,   "-"   )   )   )
	__magic_name__		:		Optional[int]	   =     Image.open(requests.get(A_      ,   stream=A_   ).raw   )
	__magic_name__		:		int	   =     image_processor(images=A_      ,   return_tensors="pt"   )

	__magic_name__		:		Tuple	   =     timm_model(inputs["pixel_values"]   )
	__magic_name__		:		Optional[Any]	   =     model(**A_   ).logits

	assert torch.allclose(A_      ,   A_      ,   atol=1E-3   )

	print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''   )
	model.save_pretrained(A_   )

	print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
	image_processor.save_pretrained(A_   )

	model.push_to_hub(
	    repo_path_or_name=Path(A_      ,   A_   )      ,   organization="nandwalritik"      ,   commit_message="Add model"      ,   )


if __name__ == "__main__":
				snake_case :		Any							      =  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--swinv2_name",
				    default="swinv2_tiny_patch4_window8_256",
				    type=str,
				    help="Name of the Swinv2 timm model you'd like to convert.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
				)

				snake_case :		Optional[int]							      =  parser.parse_args()
				convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)




"""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  (  _a    ,_a    ,_a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       StableDiffusionInpaintPipeline
    UpperCAmelCase       :							Optional[Any]	=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    UpperCAmelCase       :							Any	=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase       :							Union[str, Any]	=       frozenset(
        []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    UpperCAmelCase       :							int	=       frozenset([]  )






    def 	__snake_case							(					self				:    Dict):
      torch.manual_seed(0)
      a						:		List[Any]      							=			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=__UpperCAmelCase				, )
      a						:		Tuple      							=			PNDMScheduler(skip_prk_steps=__UpperCAmelCase)
      torch.manual_seed(0)
      a						:		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				, sample_size=128				, )
      torch.manual_seed(0)
      a						:		List[str]      							=			CLIPTextConfig(
          bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1e-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, hidden_act="gelu"				, projection_dim=512				, )
      a						:		Any      							=			CLIPTextModel(__UpperCAmelCase)
      a						:		List[str]      							=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

      a						:		Optional[Any]      							=			{
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "safety_checker": None,
          "feature_extractor": None,
      }
      return components






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    Union[str, Any]=0):
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      a						:		Dict      							=			floats_tensor((1, 3, 32, 32)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a						:		List[str]      							=			image.cpu().permute(0				, 2				, 3				, 1)[0]
      a						:		Union[str, Any]      							=			Image.fromarray(np.uinta(__UpperCAmelCase)).convert("RGB").resize((64, 64))
      a						:		Dict      							=			Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Tuple      							=			torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		Tuple      							=			torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		Optional[Any]      							=			{
          "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 	__snake_case							(					self				:    List[str]):
      a						:		Union[str, Any]      							=			"cpu"  # ensure determinism for the device-dependent torch.Generator
      a						:		Tuple      							=			self.get_dummy_components()
      a						:		Optional[int]      							=			StableDiffusionInpaintPipeline(**__UpperCAmelCase)
      a						:		int      							=			sd_pipe.to(__UpperCAmelCase)
      sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Any      							=			self.get_dummy_inputs(__UpperCAmelCase)
      a						:		Optional[int]      							=			sd_pipe(**__UpperCAmelCase).images
      a						:		Optional[int]      							=			image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      a						:		int      							=			np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476])

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






    def 	__snake_case							(					self				:    str):
      super().test_inference_batch_single_identical(expected_max_diff=3e-3)






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







    """simple docstring"""




    def 	__snake_case							(					self				:    Union[str, Any]):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def 	__snake_case							(					self				:    Dict):
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
      a						:		List[Any]      							=			load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
          "/yellow_cat_sitting_on_a_park_bench.npy")

      a						:		Tuple      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Optional[Any]      							=			StableDiffusionInpaintPipeline.from_pretrained(__UpperCAmelCase				, safety_checker=__UpperCAmelCase)
      pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)
      pipe.enable_attention_slicing()

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

      a						:		str      							=			torch.manual_seed(0)
      a						:		Union[str, Any]      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, output_type="np"				, )
      a						:		List[str]      							=			output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image).max() < 9e-3






    def 	__snake_case							(					self				:    Any):
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		Optional[Any]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
      a						:		str      							=			load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
          "/yellow_cat_sitting_on_a_park_bench_fp16.npy")

      a						:		Optional[Any]      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Any      							=			StableDiffusionInpaintPipeline.from_pretrained(
          __UpperCAmelCase				, torch_dtype=torch.floataa				, safety_checker=__UpperCAmelCase				, )
      pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)
      pipe.enable_attention_slicing()

      a						:		Optional[int]      							=			"Face of a yellow cat, high resolution, sitting on a park bench"

      a						:		Dict      							=			torch.manual_seed(0)
      a						:		List[Any]      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, output_type="np"				, )
      a						:		Optional[Any]      							=			output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image).max() < 5e-1






    def 	__snake_case							(					self				:    int):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      a						:		Tuple      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		Optional[int]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")

      a						:		Optional[Any]      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Optional[int]      							=			PNDMScheduler.from_pretrained(__UpperCAmelCase				, subfolder="scheduler")
      a						:		int      							=			StableDiffusionInpaintPipeline.from_pretrained(
          __UpperCAmelCase				, safety_checker=__UpperCAmelCase				, scheduler=__UpperCAmelCase				, torch_dtype=torch.floataa				, )
      pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)
      pipe.enable_attention_slicing(1)
      pipe.enable_sequential_cpu_offload()

      a						:		Optional[int]      							=			"Face of a yellow cat, high resolution, sitting on a park bench"

      a						:		Optional[int]      							=			torch.manual_seed(0)
      a						:		str      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=2				, output_type="np"				, )

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






