
"""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()
lowerCAmelCase__							=				logging.get_logger(__name__)



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = '''huggingface/label-files'''
		SCREAMING_SNAKE_CASE_						 = '''imagenet-1k-id2label.json'''
		SCREAMING_SNAKE_CASE_						 = json.load(open(hf_hub_download(__a,							__a,							repo_type='''dataset'''    ),							'''r'''    )    )
		SCREAMING_SNAKE_CASE_						 = {int(__a    ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_						 = {v: k for k, v in idalabel.items()}

		SCREAMING_SNAKE_CASE_						 = '''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"
		SCREAMING_SNAKE_CASE_						 = BitConfig(
		    conv_layer=__a,							num_labels=1_000,							idalabel=__a,							labelaid=__a,							)

		return config



def        _lowerCamelCase ( __a    ):
		if "stem.conv" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''stem.conv''',							'''bit.embedder.convolution'''    )
		if "blocks" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''blocks''',							'''layers'''    )
		if "head.fc" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''head.fc''',							'''classifier.1'''    )
		if name.startswith('''norm'''    ):
				SCREAMING_SNAKE_CASE_						 = '''bit.''' + name
		if "bit" not in name and "classifier" not in name:
				SCREAMING_SNAKE_CASE_						 = '''bit.encoder.''' + name

		return name



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_						 = Image.open(requests.get(__a,							stream=__a    ).raw    )
		return im



@torch.no_grad()
def        _lowerCamelCase ( __a,							__a,							__a=False    ):
		SCREAMING_SNAKE_CASE_						 = get_config(__a    )

		# load original model from timm
		SCREAMING_SNAKE_CASE_						 = create_model(__a,							pretrained=__a    )
		timm_model.eval()

		# load state_dict of original model
		SCREAMING_SNAKE_CASE_						 = timm_model.state_dict()
		for key in state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_						 = state_dict.pop(__a    )
				SCREAMING_SNAKE_CASE_						 = val.squeeze() if '''head''' in key else val

		# load HuggingFace model
		SCREAMING_SNAKE_CASE_						 = BitForImageClassification(__a    )
		model.eval()
		model.load_state_dict(__a    )

		# create image processor
		SCREAMING_SNAKE_CASE_						 = create_transform(**resolve_data_config({},							model=__a    )    )
		SCREAMING_SNAKE_CASE_						 = transform.transforms

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

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

		SCREAMING_SNAKE_CASE_						 = prepare_img()
		SCREAMING_SNAKE_CASE_						 = transform(__a    ).unsqueeze(0    )
		SCREAMING_SNAKE_CASE_						 = processor(__a,							return_tensors='''pt'''    ).pixel_values

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

		# verify logits
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_						 = model(__a    )
				SCREAMING_SNAKE_CASE_						 = outputs.logits

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

		if pytorch_dump_folder_path is not None:
				Path(__a    ).mkdir(exist_ok=__a    )
				print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'    )
				model.save_pretrained(__a    )
				processor.save_pretrained(__a    )

		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__":
						lowerCAmelCase__							=				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.',
						)

						lowerCAmelCase__							=				parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''glpn'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]       ,       SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]       ,       SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]       ,       SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]       ,       SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1e-6       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=-1       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = num_encoder_blocks
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = sr_ratios
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = patch_sizes
									SCREAMING_SNAKE_CASE_						 = strides
									SCREAMING_SNAKE_CASE_						 = mlp_ratios
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = decoder_hidden_size
									SCREAMING_SNAKE_CASE_						 = max_depth
									SCREAMING_SNAKE_CASE_						 = head_in_index