


'''simple docstring'''



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 							__UpperCamelCase				(		lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    KandinskyVaaControlnetImgaImgPipeline
	A_				=    ["image_embeds", "negative_image_embeds", "image", "hint"]
	A_				=    ["image_embeds", "negative_image_embeds", "image", "hint"]
	A_				=    [
	    "generator",
	    "height",
	    "width",
	    "strength",
	    "guidance_scale",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	A_				=    False




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 32




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 32




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.time_input_dim




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.time_input_dim * 4




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 100




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)

						__a			:       Any					    =  {
						    '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,
						}

						__a			:       List[str]					    =  UNetaDConditionModel(**__a							)
						return model




	@property
	def       __UpperCAmelCase							(   self							):

						'''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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       List[str]					    =  VQModel(**self.dummy_movq_kwargs							)
						return model




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  self.dummy_unet
						__a			:       Any					    =  self.dummy_movq

						__a			:       Dict					    =  {
						    'num_train_timesteps': 1000,
						    'beta_schedule': 'linear',
						    'beta_start': 0.00085,
						    'beta_end': 0.012,
						    'clip_sample': False,
						    'set_alpha_to_one': False,
						    'steps_offset': 0,
						    'prediction_type': 'epsilon',
						    'thresholding': False,
						}

						__a			:       str					    =  DDIMScheduler(**__a							)

						__a			:       Union[str, Any]					    =  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}

						return components




	def       __UpperCAmelCase							(   self						,				__a						,				__a=0							):

						'''simple docstring'''





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

						if str(__a							).startswith('mps'							):
											__a			:       Any					    =  torch.manual_seed(__a							)
						else:
											__a			:       Tuple					    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a			:       Dict					    =  {
						    '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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  'cpu'

						__a			:       List[Any]					    =  self.get_dummy_components()

						__a			:       Any					    =  self.pipeline_class(**__a							)
						__a			:       Tuple					    =  pipe.to(__a							)

						pipe.set_progress_bar_config(disable=__a							)

						__a			:       int					    =  pipe(**self.get_dummy_inputs(__a							)							)
						__a			:       Tuple					    =  output.images

						__a			:       str					    =  pipe(
						    **self.get_dummy_inputs(__a							)						,				return_dict=__a						,				)[0]

						__a			:       int					    =  image[0, -3:, -3:, -1]
						__a			:       Union[str, Any]					    =  image_from_tuple[0, -3:, -3:, -1]

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

						__a			:       Optional[Any]					    =  np.array(
						    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]							)
						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 							__UpperCamelCase				(		unittest.TestCase					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'							)

						__a			:       List[Any]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'							)
						__a			:       Optional[int]					    =  init_image.resize((512, 512)							)

						__a			:       Union[str, Any]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/hint_image_cat.png'							)
						__a			:       Optional[Any]					    =  torch.from_numpy(np.array(__a							)							).float() / 255.0
						__a			:       Optional[int]					    =  hint.permute(2						,				0						,				1							).unsqueeze(0							)

						__a			:       Tuple					    =  'A robot, 4k photo'

						__a			:       Optional[Any]					    =  KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-prior'						,				torch_dtype=torch.floataa							)
						pipe_prior.to(__a							)

						__a			:       Optional[int]					    =  KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-controlnet-depth'						,				torch_dtype=torch.floataa							)
						__a			:       Optional[int]					    =  pipeline.to(__a							)

						pipeline.set_progress_bar_config(disable=__a							)

						__a			:       Dict					    =  torch.Generator(device='cpu'							).manual_seed(0							)

						__a    ,				__a			:       List[str]					    =  pipe_prior(
						    __a						,				image=__a						,				strength=0.85						,				generator=__a						,				negative_prompt=''						,				).to_tuple()

						__a			:       Tuple					    =  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'						,				)

						__a			:       Dict					    =  output.images[0]

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

						assert_mean_pixel_difference(__a						,				__a							)








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 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     List[str]									= SwinvaConfig()
		lowerCAmelCase_			:     List[str]									= swinva_name.split('''_''' )

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

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

		if "to" in swinva_name:
				lowerCAmelCase_			:     Union[str, Any]									= (12, 12, 12, 6)

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

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

		lowerCAmelCase_			:     Optional[int]									= img_size
		lowerCAmelCase_			:     Dict									= num_classes
		lowerCAmelCase_			:     Dict									= embed_dim
		lowerCAmelCase_			:     Optional[Any]									= depths
		lowerCAmelCase_			:     Optional[int]									= num_heads
		lowerCAmelCase_			:     Dict									= window_size

		return config


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

		if "head" in name:
				lowerCAmelCase_			:     int									= name.replace('''head'''     ,'''classifier''' )
		else:
				lowerCAmelCase_			:     Union[str, Any]									= '''swinv2.''' + name

		return name


def 			UpperCamelCase(  __UpperCamelCase							:		Any     ,__UpperCamelCase							:		Optional[int] ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_			:     Optional[int]									= orig_state_dict.pop(__UpperCamelCase )

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

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

		return orig_state_dict


def 			UpperCamelCase(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		Dict ):
		lowerCAmelCase_			:     Optional[Any]									= timm.create_model(__UpperCamelCase     ,pretrained=__UpperCamelCase )
		timm_model.eval()

		lowerCAmelCase_			:     List[str]									= get_swinva_config(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= SwinvaForImageClassification(__UpperCamelCase )
		model.eval()

		lowerCAmelCase_			:     str									= convert_state_dict(timm_model.state_dict()     ,__UpperCamelCase )
		model.load_state_dict(__UpperCamelCase )

		lowerCAmelCase_			:     List[Any]									= '''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

		lowerCAmelCase_			:     List[str]									= timm_model(inputs['''pixel_values'''] )
		lowerCAmelCase_			:     Union[str, Any]									= model(**__UpperCamelCase ).logits

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

		print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}""" )
		model.save_pretrained(__UpperCamelCase )

		print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
		image_processor.save_pretrained(__UpperCamelCase )

		model.push_to_hub(
		    repo_path_or_name=Path(__UpperCamelCase     ,__UpperCamelCase )     ,organization='''nandwalritik'''     ,commit_message='''Add model'''     ,)


if __name__ == "__main__":
						A__  :  Union[str, 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.'''
						)

						A__  :  Optional[Any]								=					parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





