


"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__				:List[Any]			   =	logging.get_logger(__name__)





def 				A_       ( snake_case__		)					->  List[Any]:
			_UpperCamelCase					:Optional[int]					     =	'''huggingface/label-files'''
			_UpperCamelCase					:Optional[int]					     =	'''imagenet-1k-id2label.json'''
			_UpperCamelCase					:Optional[int]					     =	json.load(open(hf_hub_download(snake_case__       ,					snake_case__       ,					repo_type='''dataset'''		)       ,					'''r'''		)		)
			_UpperCamelCase					:List[str]					     =	{int(snake_case__		): v for k, v in idalabel.items()}
			_UpperCamelCase					:List[str]					     =	{v: k for k, v in idalabel.items()}

			_UpperCamelCase					:Union[str, Any]					     =	'''std_conv''' if '''bit''' in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			_UpperCamelCase					:Optional[Any]					     =	BitConfig(
			    conv_layer=snake_case__       ,					num_labels=10_00       ,					idalabel=snake_case__       ,					labelaid=snake_case__       ,					)

			return config





def 				A_       ( snake_case__		)					->  str:
			if "stem.conv" in name:
						_UpperCamelCase					:int					     =	name.replace('''stem.conv'''       ,					'''bit.embedder.convolution'''		)
			if "blocks" in name:
						_UpperCamelCase					:int					     =	name.replace('''blocks'''       ,					'''layers'''		)
			if "head.fc" in name:
						_UpperCamelCase					:Dict					     =	name.replace('''head.fc'''       ,					'''classifier.1'''		)
			if name.startswith('''norm'''		):
						_UpperCamelCase					:List[str]					     =	'''bit.''' + name
			if "bit" not in name and "classifier" not in name:
						_UpperCamelCase					:List[Any]					     =	'''bit.encoder.''' + name

			return name





def 				A_       ( )					->  List[Any]:
			_UpperCamelCase					:Optional[int]					     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			_UpperCamelCase					:List[Any]					     =	Image.open(requests.get(snake_case__       ,					stream=snake_case__		).raw		)
			return im





@torch.no_grad()
def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__=False		)					->  int:
			_UpperCamelCase					:str					     =	get_config(snake_case__		)

			# load original model from timm
			_UpperCamelCase					:List[Any]					     =	create_model(snake_case__       ,					pretrained=snake_case__		)
			timm_model.eval()

			# load state_dict of original model
			_UpperCamelCase					:Any					     =	timm_model.state_dict()
			for key in state_dict.copy().keys():
						_UpperCamelCase					:str					     =	state_dict.pop(snake_case__		)
						_UpperCamelCase					:Dict					     =	val.squeeze() if '''head''' in key else val

			# load HuggingFace model
			_UpperCamelCase					:Tuple					     =	BitForImageClassification(snake_case__		)
			model.eval()
			model.load_state_dict(snake_case__		)

			# create image processor
			_UpperCamelCase					:Union[str, Any]					     =	create_transform(**resolve_data_config({}       ,					model=snake_case__		)		)
			_UpperCamelCase					:Dict					     =	transform.transforms

			_UpperCamelCase					:Dict					     =	{
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

			_UpperCamelCase					:int					     =	BitImageProcessor(
			    do_resize=snake_case__       ,					size={'''shortest_edge''': timm_transforms[0].size}       ,					resample=pillow_resamplings[timm_transforms[0].interpolation.value]       ,					do_center_crop=snake_case__       ,					crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}       ,					do_normalize=snake_case__       ,					image_mean=timm_transforms[-1].mean.tolist()       ,					image_std=timm_transforms[-1].std.tolist()       ,					)

			_UpperCamelCase					:List[str]					     =	prepare_img()
			_UpperCamelCase					:Any					     =	transform(snake_case__		).unsqueeze(0		)
			_UpperCamelCase					:int					     =	processor(snake_case__       ,					return_tensors='''pt'''		).pixel_values

			# verify pixel values
			assert torch.allclose(snake_case__       ,					snake_case__		)

			# verify logits
			with torch.no_grad():
						_UpperCamelCase					:int					     =	model(snake_case__		)
						_UpperCamelCase					:Optional[int]					     =	outputs.logits

			print('''Logits:'''       ,					logits[0, :3]		)
			print('''Predicted class:'''       ,					model.config.idalabel[logits.argmax(-1		).item()]		)
			_UpperCamelCase					:str					     =	timm_model(snake_case__		)
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(snake_case__       ,					outputs.logits       ,					atol=1E-3		)
			print('''Looks ok!'''		)

			if pytorch_dump_folder_path is not None:
						Path(snake_case__		).mkdir(exist_ok=snake_case__		)
						print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}"		)
						model.save_pretrained(snake_case__		)
						processor.save_pretrained(snake_case__		)

			if push_to_hub:
						print(f"Pushing model {model_name} and processor to the hub"		)
						model.push_to_hub(f"ybelkada/{model_name}"		)
						processor.push_to_hub(f"ybelkada/{model_name}"		)


if __name__ == "__main__":
					UpperCamelCase__				:Optional[int]			   =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""resnetv2_50x1_bitm""",
					    type=str,
					    help="""Name of the BiT 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."""
					)
					parser.add_argument(
					    """--push_to_hub""",
					    action="""store_true""",
					    help="""Whether to push the model to the hub.""",
					)

					UpperCamelCase__				:str			   =	parser.parse_args()
					convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class      A(  unittest.TestCase		):

		"""simple docstring"""





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	UNetaDModel(
					    block_out_channels=(32, 64)					,				layers_per_block=2					,				sample_size=32					,				in_channels=3					,				out_channels=3					,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,				)
					return model





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.dummy_uncond_unet
					_UpperCamelCase					:List[Any]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[Any]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:List[Any]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:Dict					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''					,				return_dict=SCREAMING_SNAKE_CASE__   )[0]

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCamelCase					:List[Any]					     =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

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






@slow
@require_torch
class      A(  unittest.TestCase		):

		"""simple docstring"""





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

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/ncsnpp-celebahq-256'''
					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[int]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=20					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_56, 2_56, 3)
					_UpperCamelCase					:Any					     =	np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

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

