


"""simple docstring"""


import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification






def      a__ (				__lowercase   )	->	int:
				_A					  =			[]
				embed.append(
				    (
				        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
				        f"""stage{idx}.patch_embed.proj.weight""",
				    )   )
				embed.append(
				    (
				        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
				        f"""stage{idx}.patch_embed.proj.bias""",
				    )   )
				embed.append(
				    (
				        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
				        f"""stage{idx}.patch_embed.norm.weight""",
				    )   )
				embed.append(
				    (
				        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
				        f"""stage{idx}.patch_embed.norm.bias""",
				    )   )
				return embed






def      a__ (				__lowercase     ,			__lowercase   )	->	str:
				_A					  =			[]
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
				        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
				        f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
				        f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
				        f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
				        f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
				        f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
				        f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
				        f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
				    )   )
				attention_weights.append(
				    (
				        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
				        f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
				    )   )
				attention_weights.append(
				    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")   )
				attention_weights.append(
				    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")   )
				attention_weights.append(
				    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")   )
				attention_weights.append(
				    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")   )
				attention_weights.append(
				    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")   )
				attention_weights.append(
				    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")   )
				attention_weights.append(
				    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")   )
				attention_weights.append(
				    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")   )
				return attention_weights






def      a__ (				__lowercase   )	->	Dict:
				_A					  =			[]
				token.append((f"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")   )
				return token






def      a__ (				)	->	Dict:
				_A					  =			[]
				head.append(("layernorm.weight", "norm.weight")   )
				head.append(("layernorm.bias", "norm.bias")   )
				head.append(("classifier.weight", "head.weight")   )
				head.append(("classifier.bias", "head.bias")   )
				return head






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )	->	Optional[Any]:
				_A					  =			"imagenet-1k-id2label.json"
				_A					  =			1000

				_A					  =			"huggingface/label-files"
				_A					  =			num_labels
				_A					  =			json.load(open(cached_download(hf_hub_url(__lowercase     ,			__lowercase     ,			repo_type="dataset"   )   )     ,			"r"   )   )
				_A					  =			{int(__lowercase   ): v for k, v in idalabel.items()}

				_A					  =			idalabel
				_A					  =			{v: k for k, v in idalabel.items()}

				_A					  =			_A					  =			CvtConfig(num_labels=__lowercase     ,			idalabel=__lowercase     ,			labelaid=__lowercase   )

				# For depth size 13 (13 = 1+2+10)
				if cvt_model.rsplit("/"     ,			1   )[-1][4:6] == "13":
								_A					  =			[1, 2, 10]

				# For depth size 21 (21 = 1+4+16)
				elif cvt_model.rsplit("/"     ,			1   )[-1][4:6] == "21":
								_A					  =			[1, 4, 16]

				# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
				else:
								_A					  =			[2, 2, 20]
								_A					  =			[3, 12, 16]
								_A					  =			[192, 768, 1024]

				_A					  =			CvtForImageClassification(__lowercase   )
				_A					  =			AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"   )
				_A					  =			image_size
				_A					  =			torch.load(__lowercase     ,			map_location=torch.device("cpu"   )   )

				_A					  =			OrderedDict()
				_A					  =			[]

				for idx in range(len(config.depth   )   ):
								if config.cls_token[idx]:
												_A					  =			list_of_state_dict + cls_token(__lowercase   )
								_A					  =			list_of_state_dict + embeddings(__lowercase   )
								for cnt in range(config.depth[idx]   ):
												_A					  =			list_of_state_dict + attention(__lowercase     ,			__lowercase   )

				_A					  =			list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(__lowercase   )
				for i in range(len(__lowercase   )   ):
								_A					  =			original_weights[list_of_state_dict[i][1]]

				model.load_state_dict(__lowercase   )
				model.save_pretrained(__lowercase   )
				image_processor.save_pretrained(__lowercase   )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser()
		parser.add_argument(
		    "--cvt_model",
		    default="cvt-w24",
		    type=str,
		    help="Name of the cvt model you'd like to convert.",
		)
		parser.add_argument(
		    "--image_size",
		    default=3_84,
		    type=int,
		    help="Input Image Size",
		)
		parser.add_argument(
		    "--cvt_file_name",
		    default=r"cvtmodels\CvT-w24-384x384-IN-22k.pth",
		    type=str,
		    help="Input Image Size",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)

		a_       							=    parser.parse_args()
		convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_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 snake_case      (  _UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			StableDiffusionInpaintPipeline
		__UpperCamelCase           =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
		__UpperCamelCase           =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__UpperCamelCase           =			frozenset(
		    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		__UpperCamelCase           =			frozenset([])







		def  a_ (					self      :       Dict		)       ->						Optional[int]:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			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=a__    ,   )
						_A					  =			PNDMScheduler(skip_prk_steps=a__		)
						torch.manual_seed(0		)
						_A					  =			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		)
						_A					  =			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    ,   )
						_A					  =			CLIPTextModel(a__		)
						_A					  =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

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







		def  a_ (					self      :       Optional[Any]    ,   a__      :       List[str]    ,   a__      :       Tuple=0		)       ->						int:





						'''simple docstring'''







						_A					  =			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(a__		)		).to(a__		)
						_A					  =			image.cpu().permute(0    ,   2    ,   3    ,   1		)[0]
						_A					  =			Image.fromarray(np.uinta(a__		)		).convert("RGB"		).resize((64, 64)		)
						_A					  =			Image.fromarray(np.uinta(image + 4		)		).convert("RGB"		).resize((64, 64)		)
						if str(a__		).startswith("mps"		):
										_A					  =			torch.manual_seed(a__		)
						else:
										_A					  =			torch.Generator(device=a__		).manual_seed(a__		)
						_A					  =			{
						    "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  a_ (					self      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







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

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

						assert image.shape == (1, 64, 64, 3)
						_A					  =			np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]		)

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







		def  a_ (					self      :       str		)       ->						Union[str, Any]:





						'''simple docstring'''







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





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







		def  a_ (					self      :       List[Any]		)       ->						Any:





						'''simple docstring'''







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







		def  a_ (					self      :       Union[str, Any]		)       ->						Tuple:





						'''simple docstring'''







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

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

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

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   output_type="np"    ,   )
						_A					  =			output.images[0]

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







		def  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"		)
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)
						_A					  =			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					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			StableDiffusionInpaintPipeline.from_pretrained(
						    a__    ,   torch_dtype=torch.floataa    ,   safety_checker=a__    ,   )
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)
						pipe.enable_attention_slicing()

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

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   output_type="np"    ,   )
						_A					  =			output.images[0]

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







		def  a_ (					self      :       List[Any]		)       ->						Union[str, Any]:





						'''simple docstring'''







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

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

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

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

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   num_inference_steps=2    ,   output_type="np"    ,   )

						_A					  =			torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.6_5 * 10**9