
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 							__A(lowerCAmelCase )		->				str:

		"""simple docstring"""






		_UpperCamelCase						    =	SwinvaConfig()
		_UpperCamelCase						    =	swinva_name.split("""_""" )

		_UpperCamelCase						    =	name_split[1]
		if "to" in name_split[3]:
				_UpperCamelCase						    =	int(name_split[3][-3:] )
		else:
				_UpperCamelCase						    =	int(name_split[3] )
		if "to" in name_split[2]:
				_UpperCamelCase						    =	int(name_split[2][-2:] )
		else:
				_UpperCamelCase						    =	int(name_split[2][6:] )

		if model_size == "tiny":
				_UpperCamelCase						    =	9_6
				_UpperCamelCase						    =	(2, 2, 6, 2)
				_UpperCamelCase						    =	(3, 6, 1_2, 2_4)
		elif model_size == "small":
				_UpperCamelCase						    =	9_6
				_UpperCamelCase						    =	(2, 2, 1_8, 2)
				_UpperCamelCase						    =	(3, 6, 1_2, 2_4)
		elif model_size == "base":
				_UpperCamelCase						    =	1_2_8
				_UpperCamelCase						    =	(2, 2, 1_8, 2)
				_UpperCamelCase						    =	(4, 8, 1_6, 3_2)
		else:
				_UpperCamelCase						    =	1_9_2
				_UpperCamelCase						    =	(2, 2, 1_8, 2)
				_UpperCamelCase						    =	(6, 1_2, 2_4, 4_8)

		if "to" in swinva_name:
				_UpperCamelCase						    =	(1_2, 1_2, 1_2, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				_UpperCamelCase						    =	2_1_8_4_1
				_UpperCamelCase						    =	"""huggingface/label-files"""
				_UpperCamelCase						    =	"""imagenet-22k-id2label.json"""
				_UpperCamelCase						    =	json.load(open(hf_hub_download(lowerCAmelCase						,  lowerCAmelCase						,  repo_type="""dataset""" )						,  """r""" ) )
				_UpperCamelCase						    =	{int(lowerCAmelCase ): v for k, v in idalabel.items()}
				_UpperCamelCase						    =	idalabel
				_UpperCamelCase						    =	{v: k for k, v in idalabel.items()}

		else:
				_UpperCamelCase						    =	1_0_0_0
				_UpperCamelCase						    =	"""huggingface/label-files"""
				_UpperCamelCase						    =	"""imagenet-1k-id2label.json"""
				_UpperCamelCase						    =	json.load(open(hf_hub_download(lowerCAmelCase						,  lowerCAmelCase						,  repo_type="""dataset""" )						,  """r""" ) )
				_UpperCamelCase						    =	{int(lowerCAmelCase ): v for k, v in idalabel.items()}
				_UpperCamelCase						    =	idalabel
				_UpperCamelCase						    =	{v: k for k, v in idalabel.items()}

		_UpperCamelCase						    =	img_size
		_UpperCamelCase						    =	num_classes
		_UpperCamelCase						    =	embed_dim
		_UpperCamelCase						    =	depths
		_UpperCamelCase						    =	num_heads
		_UpperCamelCase						    =	window_size

		return config


def 							__A(lowerCAmelCase )		->				Optional[Any]:

		"""simple docstring"""






		if "patch_embed.proj" in name:
				_UpperCamelCase						    =	name.replace("""patch_embed.proj"""						,  """embeddings.patch_embeddings.projection""" )
		if "patch_embed.norm" in name:
				_UpperCamelCase						    =	name.replace("""patch_embed.norm"""						,  """embeddings.norm""" )
		if "layers" in name:
				_UpperCamelCase						    =	"""encoder.""" + name
		if "attn.proj" in name:
				_UpperCamelCase						    =	name.replace("""attn.proj"""						,  """attention.output.dense""" )
		if "attn" in name:
				_UpperCamelCase						    =	name.replace("""attn"""						,  """attention.self""" )
		if "norm1" in name:
				_UpperCamelCase						    =	name.replace("""norm1"""						,  """layernorm_before""" )
		if "norm2" in name:
				_UpperCamelCase						    =	name.replace("""norm2"""						,  """layernorm_after""" )
		if "mlp.fc1" in name:
				_UpperCamelCase						    =	name.replace("""mlp.fc1"""						,  """intermediate.dense""" )
		if "mlp.fc2" in name:
				_UpperCamelCase						    =	name.replace("""mlp.fc2"""						,  """output.dense""" )
		if "q_bias" in name:
				_UpperCamelCase						    =	name.replace("""q_bias"""						,  """query.bias""" )
		if "k_bias" in name:
				_UpperCamelCase						    =	name.replace("""k_bias"""						,  """key.bias""" )
		if "v_bias" in name:
				_UpperCamelCase						    =	name.replace("""v_bias"""						,  """value.bias""" )
		if "cpb_mlp" in name:
				_UpperCamelCase						    =	name.replace("""cpb_mlp"""						,  """continuous_position_bias_mlp""" )
		if name == "norm.weight":
				_UpperCamelCase						    =	"""layernorm.weight"""
		if name == "norm.bias":
				_UpperCamelCase						    =	"""layernorm.bias"""

		if "head" in name:
				_UpperCamelCase						    =	name.replace("""head"""						,  """classifier""" )
		else:
				_UpperCamelCase						    =	"""swinv2.""" + name

		return name


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				Union[str, Any]:

		"""simple docstring"""






		for key in orig_state_dict.copy().keys():
				_UpperCamelCase						    =	orig_state_dict.pop(lowerCAmelCase )

				if "mask" in key:
						continue
				elif "qkv" in key:
						_UpperCamelCase						    =	key.split(""".""" )
						_UpperCamelCase						    =	int(key_split[1] )
						_UpperCamelCase						    =	int(key_split[3] )
						_UpperCamelCase						    =	model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								_UpperCamelCase						    =	val[:dim, :]
								_UpperCamelCase						    =	val[dim : dim * 2, :]
								_UpperCamelCase						    =	val[-dim:, :]
						else:
								_UpperCamelCase						    =	val[:dim]
								_UpperCamelCase						    =	val[
								    dim : dim * 2
								]
								_UpperCamelCase						    =	val[-dim:]
				else:
						_UpperCamelCase						    =	val

		return orig_state_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	timm.create_model(lowerCAmelCase						,  pretrained=lowerCAmelCase )
		timm_model.eval()

		_UpperCamelCase						    =	get_swinva_config(lowerCAmelCase )
		_UpperCamelCase						    =	SwinvaForImageClassification(lowerCAmelCase )
		model.eval()

		_UpperCamelCase						    =	convert_state_dict(timm_model.state_dict()						,  lowerCAmelCase )
		model.load_state_dict(lowerCAmelCase )

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

		_UpperCamelCase						    =	AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""						,  """-""" ) ) )
		_UpperCamelCase						    =	Image.open(requests.get(lowerCAmelCase						,  stream=lowerCAmelCase ).raw )
		_UpperCamelCase						    =	image_processor(images=lowerCAmelCase						,  return_tensors="""pt""" )

		_UpperCamelCase						    =	timm_model(inputs["""pixel_values"""] )
		_UpperCamelCase						    =	model(**lowerCAmelCase ).logits

		assert torch.allclose(lowerCAmelCase						,  lowerCAmelCase						,  atol=1e-3 )

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

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

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


if __name__ == "__main__":
		lowerCamelCase__	=							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."
		)

		lowerCamelCase__	=							parser.parse_args()
		convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





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        lowerCAmelCase__      (   __lowercase		,	__lowercase		,	__lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: List[Any]	=		StableDiffusionInpaintPipeline
						UpperCamelCase_							: int	=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						UpperCamelCase_							: Union[str, Any]	=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						UpperCamelCase_							: Tuple	=		frozenset(
						    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						UpperCamelCase_							: int	=		frozenset([]	)

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


								'''simple docstring'''

								torch.manual_seed(0				)
								_UpperCamelCase						    =	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       ,							)
								_UpperCamelCase						    =	PNDMScheduler(skip_prk_steps=a				)
								torch.manual_seed(0				)
								_UpperCamelCase						    =	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				)
								_UpperCamelCase						    =	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=10_00       ,							hidden_act="""gelu"""       ,							projection_dim=5_12       ,							)
								_UpperCamelCase						    =	CLIPTextModel(a				)
								_UpperCamelCase						    =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

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

						def  A_				(			self       ,							a       ,							a=0				)					->      List[Any]:


								'''simple docstring'''

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


								'''simple docstring'''

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

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

								assert image.shape == (1, 64, 64, 3)
								_UpperCamelCase						    =	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]				)

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







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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







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

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


								'''simple docstring'''

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

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

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

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

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

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

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

						def  A_				(			self				)					->      str:


								'''simple docstring'''

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

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	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()

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

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

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







						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

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

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

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	PNDMScheduler.from_pretrained(a       ,							subfolder="""scheduler"""				)
								_UpperCamelCase						    =	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()

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

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

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



