import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[]
						embed.append(
						    (
						        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
						        f"""stage{idx}.patch_embed.proj.weight""",
						    )		)
						embed.append(
						    (
						        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
						        f"""stage{idx}.patch_embed.proj.bias""",
						    )		)
						embed.append(
						    (
						        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
						        f"""stage{idx}.patch_embed.norm.weight""",
						    )		)
						embed.append(
						    (
						        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
						        f"""stage{idx}.patch_embed.norm.bias""",
						    )		)
						return embed
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[]
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
						    )		)
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
						    )		)
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")		)
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")		)
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")		)
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")		)
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")		)
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")		)
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")		)
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")		)
						return attention_weights
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					int           =							[]
						token.append((f"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''')		)
						return token
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Any           =							[]
						head.append(('''layernorm.weight''', '''norm.weight''')		)
						head.append(('''layernorm.bias''', '''norm.bias''')		)
						head.append(('''classifier.weight''', '''head.weight''')		)
						head.append(('''classifier.bias''', '''head.bias''')		)
						return head
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[str]           =							'''imagenet-1k-id2label.json'''
						SCREAMING_SNAKE_CASE							:					str           =							1_000

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''huggingface/label-files'''
						SCREAMING_SNAKE_CASE							:					List[Any]           =							num_labels
						SCREAMING_SNAKE_CASE							:					List[str]           =							json.load(open(cached_download(hf_hub_url(_lowercase	,						_lowercase	,						repo_type='''dataset'''		)		)	,						'''r'''		)		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							{int(_lowercase		): v for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE							:					List[Any]           =							idalabel
						SCREAMING_SNAKE_CASE							:					Any           =							{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE							:					int           =							CvtConfig(num_labels=_lowercase	,						idalabel=_lowercase	,						labelaid=_lowercase		)

						# For depth size 13 (13 = 1+2+10)
						if cvt_model.rsplit('''/'''	,						1		)[-1][4:6] == "13":
												SCREAMING_SNAKE_CASE							:					int           =							[1, 2, 10]

						# For depth size 21 (21 = 1+4+16)
						elif cvt_model.rsplit('''/'''	,						1		)[-1][4:6] == "21":
												SCREAMING_SNAKE_CASE							:					Dict           =							[1, 4, 16]

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												SCREAMING_SNAKE_CASE							:					Any           =							[2, 2, 20]
												SCREAMING_SNAKE_CASE							:					List[str]           =							[3, 12, 16]
												SCREAMING_SNAKE_CASE							:					int           =							[192, 768, 1_024]

						SCREAMING_SNAKE_CASE							:					Any           =							CvtForImageClassification(_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''		)
						SCREAMING_SNAKE_CASE							:					Any           =							image_size
						SCREAMING_SNAKE_CASE							:					Any           =							torch.load(_lowercase	,						map_location=torch.device('''cpu'''		)		)

						SCREAMING_SNAKE_CASE							:					str           =							OrderedDict()
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[]

						for idx in range(len(config.depth		)		):
												if config.cls_token[idx]:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							list_of_state_dict + cls_token(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							list_of_state_dict + embeddings(_lowercase		)
												for cnt in range(config.depth[idx]		):
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							list_of_state_dict + attention(_lowercase	,						_lowercase		)

						SCREAMING_SNAKE_CASE							:					Any           =							list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(_lowercase		)
						for i in range(len(_lowercase		)		):
												SCREAMING_SNAKE_CASE							:					Tuple           =							original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(_lowercase		)
						model.save_pretrained(_lowercase		)
						image_processor.save_pretrained(_lowercase		)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
		__UpperCamelCase	: Union[str, Any]    		=  argparse.ArgumentParser()
		parser.add_argument(
		    '--cvt_model',
		    default='cvt-w24',
		    type=str,
		    help='Name of the cvt model you\'d like to convert.',
		)
		parser.add_argument(
		    '--image_size',
		    default=384,
		    type=int,
		    help='Input Image Size',
		)
		parser.add_argument(
		    '--cvt_file_name',
		    default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
		    type=str,
		    help='Input Image Size',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)

		__UpperCamelCase	: Tuple    		=  parser.parse_args()
		convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






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	: str    		=  logging.get_logger(__name__)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							'''huggingface/label-files'''
						SCREAMING_SNAKE_CASE							:					Any           =							'''imagenet-1k-id2label.json'''
						SCREAMING_SNAKE_CASE							:					Any           =							json.load(open(hf_hub_download(_lowercase	,						_lowercase	,						repo_type='''dataset'''		)	,						'''r'''		)		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{int(_lowercase		): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE							:					Dict           =							{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE							:					str           =							'''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							:					Optional[int]           =							BitConfig(
						    conv_layer=_lowercase	,						num_labels=1_000	,						idalabel=_lowercase	,						labelaid=_lowercase	,						)

						return config
def    A			(					_lowercase		):
						if "stem.conv" in name:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							name.replace('''stem.conv'''	,						'''bit.embedder.convolution'''		)
						if "blocks" in name:
												SCREAMING_SNAKE_CASE							:					Tuple           =							name.replace('''blocks'''	,						'''layers'''		)
						if "head.fc" in name:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							name.replace('''head.fc'''	,						'''classifier.1'''		)
						if name.startswith('''norm'''		):
												SCREAMING_SNAKE_CASE							:					str           =							'''bit.''' + name
						if "bit" not in name and "classifier" not in name:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''bit.encoder.''' + name

						return name
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Any           =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						SCREAMING_SNAKE_CASE							:					List[str]           =							Image.open(requests.get(_lowercase	,						stream=_lowercase		).raw		)
						return im
@torch.no_grad()
def    A			(					_lowercase	,						_lowercase	,						_lowercase=False		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							get_config(_lowercase		)

						# load original model from timm
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							create_model(_lowercase	,						pretrained=_lowercase		)
						timm_model.eval()

						# load state_dict of original model
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							timm_model.state_dict()
						for key in state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:					Dict           =							state_dict.pop(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							val.squeeze() if '''head''' in key else val

						# load HuggingFace model
						SCREAMING_SNAKE_CASE							:					str           =							BitForImageClassification(_lowercase		)
						model.eval()
						model.load_state_dict(_lowercase		)

						# create image processor
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							create_transform(**resolve_data_config({}	,						model=_lowercase		)		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							transform.transforms

						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{
						    '''bilinear''': PILImageResampling.BILINEAR,
						    '''bicubic''': PILImageResampling.BICUBIC,
						    '''nearest''': PILImageResampling.NEAREST,
						}

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

						SCREAMING_SNAKE_CASE							:					Any           =							prepare_img()
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							transform(_lowercase		).unsqueeze(0		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							processor(_lowercase	,						return_tensors='''pt'''		).pixel_values

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

						# verify logits
						with torch.no_grad():
												SCREAMING_SNAKE_CASE							:					Dict           =							model(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							outputs.logits

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

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

						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	: Any    		=  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	: Optional[int]    		=  parser.parse_args()
					convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





