



"""simple docstring"""






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   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			lowerCamelCase__	:	List[str]				=SwinvaConfig()
			lowerCamelCase__	:	str				=swinva_name.split('''_'''			)

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

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

			if "to" in swinva_name:
						lowerCamelCase__	:	Any				=(12, 12, 12, 6)

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

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

			lowerCamelCase__	:	List[str]				=img_size
			lowerCamelCase__	:	Optional[int]				=num_classes
			lowerCamelCase__	:	str				=embed_dim
			lowerCamelCase__	:	Tuple				=depths
			lowerCamelCase__	:	Dict				=num_heads
			lowerCamelCase__	:	str				=window_size

			return config






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

			if "head" in name:
						lowerCamelCase__	:	Dict				=name.replace('''head'''							,	'''classifier'''			)
			else:
						lowerCamelCase__	:	Tuple				='''swinv2.''' + name

			return name






def   snake_case__     (	__lowerCamelCase       :					int							,	__lowerCamelCase       :					List[Any]			):
			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						lowerCamelCase__	:	Optional[Any]				=orig_state_dict.pop(__lowerCamelCase			)

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

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

			return orig_state_dict






def   snake_case__     (	__lowerCamelCase       :					int							,	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=timm.create_model(__lowerCamelCase							,	pretrained=__lowerCamelCase			)
			timm_model.eval()

			lowerCamelCase__	:	Union[str, Any]				=get_swinva_config(__lowerCamelCase			)
			lowerCamelCase__	:	str				=SwinvaForImageClassification(__lowerCamelCase			)
			model.eval()

			lowerCamelCase__	:	List[Any]				=convert_state_dict(timm_model.state_dict()							,	__lowerCamelCase			)
			model.load_state_dict(__lowerCamelCase			)

			lowerCamelCase__	:	Dict				='''http://images.cocodataset.org/val2017/000000039769.jpg'''

			lowerCamelCase__	:	List[Any]				=AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''							,	'''-'''			)			)			)
			lowerCamelCase__	:	List[Any]				=Image.open(requests.get(__lowerCamelCase							,	stream=__lowerCamelCase			).raw			)
			lowerCamelCase__	:	Optional[Any]				=image_processor(images=__lowerCamelCase							,	return_tensors='''pt'''			)

			lowerCamelCase__	:	Union[str, Any]				=timm_model(inputs['''pixel_values''']			)
			lowerCamelCase__	:	List[str]				=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__":
		_lowercase				:				List[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."
		)

		_lowercase				:				Union[str, Any]   		=		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 transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							StableUnCLIPImgaImgPipeline
					_a         =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS
					_a         =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					_a         =							frozenset(
					    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					_a         =							frozenset([]     )



					def 	snake_case ( self							:						List[str]  )->      str:
								lowerCamelCase__	:	Dict				=32
								lowerCamelCase__	:	Optional[Any]				=embedder_hidden_size

								# image encoding components

								lowerCamelCase__	:	Dict				=CLIPImageProcessor(crop_size=32,						size=32  )

								torch.manual_seed(0  )
								lowerCamelCase__	:	List[Any]				=CLIPVisionModelWithProjection(
								    CLIPVisionConfig(
								        hidden_size=lowerCamelCase,						projection_dim=lowerCamelCase,						num_hidden_layers=5,						num_attention_heads=4,						image_size=32,						intermediate_size=37,						patch_size=1,						)  )

								# regular denoising components

								torch.manual_seed(0  )
								lowerCamelCase__	:	Optional[int]				=StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase  )
								lowerCamelCase__	:	Dict				=DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''  )

								torch.manual_seed(0  )
								lowerCamelCase__	:	Optional[int]				=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								torch.manual_seed(0  )
								lowerCamelCase__	:	Tuple				=CLIPTextModel(
								    CLIPTextConfig(
								        bos_token_id=0,						eos_token_id=2,						hidden_size=lowerCamelCase,						projection_dim=32,						intermediate_size=37,						layer_norm_eps=1E-05,						num_attention_heads=4,						num_hidden_layers=5,						pad_token_id=1,						vocab_size=1000,						)  )

								torch.manual_seed(0  )
								lowerCamelCase__	:	Dict				=UNetaDConditionModel(
								    sample_size=32,						in_channels=4,						out_channels=4,						down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D'''),						up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D'''),						block_out_channels=(32, 64),						attention_head_dim=(2, 4),						class_embed_type='''projection''',						projection_class_embeddings_input_dim=embedder_projection_dim * 2,						cross_attention_dim=lowerCamelCase,						layers_per_block=1,						upcast_attention=lowerCamelCase,						use_linear_projection=lowerCamelCase,						)

								torch.manual_seed(0  )
								lowerCamelCase__	:	Union[str, Any]				=DDIMScheduler(
								    beta_schedule='''scaled_linear''',						beta_start=0.00_085,						beta_end=0.012,						prediction_type='''v_prediction''',						set_alpha_to_one=lowerCamelCase,						steps_offset=1,						)

								torch.manual_seed(0  )
								lowerCamelCase__	:	Optional[int]				=AutoencoderKL()

								lowerCamelCase__	:	int				={
								    # image encoding components
								    '''feature_extractor''': feature_extractor,
								    '''image_encoder''': image_encoder.eval(),
								    # image noising components
								    '''image_normalizer''': image_normalizer.eval(),
								    '''image_noising_scheduler''': image_noising_scheduler,
								    # regular denoising components
								    '''tokenizer''': tokenizer,
								    '''text_encoder''': text_encoder.eval(),
								    '''unet''': unet.eval(),
								    '''scheduler''': scheduler,
								    '''vae''': vae.eval(),
								}

								return components



					def 	snake_case ( self							:						str,						lowerCamelCase							:						Dict,						lowerCamelCase							:						Any=0,						lowerCamelCase							:						str=True  )->      List[str]:
								if str(lowerCamelCase  ).startswith('''mps'''  ):
											lowerCamelCase__	:	List[Any]				=torch.manual_seed(lowerCamelCase  )
								else:
											lowerCamelCase__	:	Any				=torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )

								lowerCamelCase__	:	Dict				=floats_tensor((1, 3, 32, 32),						rng=random.Random(lowerCamelCase  )  ).to(lowerCamelCase  )

								if pil_image:
											lowerCamelCase__	:	int				=input_image * 0.5 + 0.5
											lowerCamelCase__	:	Dict				=input_image.clamp(0,						1  )
											lowerCamelCase__	:	List[str]				=input_image.cpu().permute(0,						2,						3,						1  ).float().numpy()
											lowerCamelCase__	:	Dict				=DiffusionPipeline.numpy_to_pil(lowerCamelCase  )[0]

								return {
								    "prompt": "An anime racoon running a marathon",
								    "image": input_image,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "np",
								}



					@skip_mps
					def 	snake_case ( self							:						List[str]  )->      Optional[Any]:
								lowerCamelCase__	:	Dict				='''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase__	:	str				=self.get_dummy_components()
								lowerCamelCase__	:	int				=StableUnCLIPImgaImgPipeline(**lowerCamelCase  )
								lowerCamelCase__	:	Any				=sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowerCamelCase__	:	Dict				=self.get_dummy_inputs(lowerCamelCase  )
								inputs.update({'''image_embeds''': None}  )
								lowerCamelCase__	:	Any				=sd_pipe(**lowerCamelCase  ).images
								lowerCamelCase__	:	List[Any]				=image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCamelCase__	:	Union[str, Any]				=np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



					def 	snake_case ( self							:						int  )->      Tuple:
								lowerCamelCase__	:	Tuple				=torch_device in ['''cpu''', '''mps''']

								self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase  )



					def 	snake_case ( self							:						int  )->      Optional[Any]:
								lowerCamelCase__	:	List[Any]				=torch_device in ['''cpu''', '''mps''']

								self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase  )




					@unittest.skipIf(
					    torch_device != '''cuda''' or not is_xformers_available(),						reason='''XFormers attention is only available with CUDA and `xformers` installed''',						)
					def 	snake_case ( self							:						List[str]  )->      List[str]:
								self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCamelCase  )





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


					'''simple docstring'''





					def 	snake_case ( self							:						List[Any]  )->      Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



					def 	snake_case ( self							:						Optional[int]  )->      int:
								lowerCamelCase__	:	Tuple				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''  )

								lowerCamelCase__	:	Optional[int]				=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'''  )

								lowerCamelCase__	:	Optional[Any]				=StableUnCLIPImgaImgPipeline.from_pretrained(
								    '''fusing/stable-unclip-2-1-l-img2img''',						torch_dtype=torch.floataa  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								# stable unclip will oom when integration tests are run on a V100,
								# so turn on memory savings
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								lowerCamelCase__	:	int				=torch.Generator(device='''cpu'''  ).manual_seed(0  )
								lowerCamelCase__	:	Any				=pipe(lowerCamelCase,						'''anime turle''',						generator=lowerCamelCase,						output_type='''np'''  )

								lowerCamelCase__	:	List[Any]				=output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						Optional[int]  )->      Tuple:
								lowerCamelCase__	:	Any				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''  )

								lowerCamelCase__	:	str				=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'''  )

								lowerCamelCase__	:	Optional[int]				=StableUnCLIPImgaImgPipeline.from_pretrained(
								    '''fusing/stable-unclip-2-1-h-img2img''',						torch_dtype=torch.floataa  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								# stable unclip will oom when integration tests are run on a V100,
								# so turn on memory savings
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								lowerCamelCase__	:	str				=torch.Generator(device='''cpu'''  ).manual_seed(0  )
								lowerCamelCase__	:	Tuple				=pipe(lowerCamelCase,						'''anime turle''',						generator=lowerCamelCase,						output_type='''np'''  )

								lowerCamelCase__	:	Tuple				=output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(lowerCamelCase,						lowerCamelCase  )




					def 	snake_case ( self							:						Optional[int]  )->      List[str]:
								lowerCamelCase__	:	int				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''  )

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

								lowerCamelCase__	:	Any				=StableUnCLIPImgaImgPipeline.from_pretrained(
								    '''fusing/stable-unclip-2-1-h-img2img''',						torch_dtype=torch.floataa  )
								lowerCamelCase__	:	Optional[Any]				=pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								lowerCamelCase__	:	List[Any]				=pipe(
								    lowerCamelCase,						'''anime turtle''',						num_inference_steps=2,						output_type='''np''',						)

								lowerCamelCase__	:	Optional[int]				=torch.cuda.max_memory_allocated()
								# make sure that less than 7 GB is allocated
								assert mem_bytes < 7 * 10**9




