

'''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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_							=					logging.get_logger(__name__)






def 							_lowerCAmelCase (      __magic_name__	:    Optional[Any]      ,   __magic_name__	:    Optional[Any]=False	)							->     str:
						lowercase   :       Tuple								=[]
						for i in range(config.num_hidden_layers	):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')	)
												rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')	)
												rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')	)
												rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')	)
												rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')	)
												rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')	)
												rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')	)
												rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')	)
												rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')	)
												rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')	)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ('''cls_token''', '''vit.embeddings.cls_token'''),
						        ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
						        ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
						        ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
						    ]	)

						if base_model:
												# layernorm + pooler
												rename_keys.extend(
												    [
												        ('''norm.weight''', '''layernorm.weight'''),
												        ('''norm.bias''', '''layernorm.bias'''),
												        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
												        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
												    ]	)

												# if just the base model, we should remove "vit" from all keys that start with "vit"
												lowercase   :       str								=[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''	) else pair for pair in rename_keys]
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('''norm.weight''', '''vit.layernorm.weight'''),
												        ('''norm.bias''', '''vit.layernorm.bias'''),
												        ('''head.weight''', '''classifier.weight'''),
												        ('''head.bias''', '''classifier.bias'''),
												    ]	)

						return rename_keys






def 							_lowerCAmelCase (      __magic_name__	:    Optional[Any]      ,   __magic_name__	:    List[Any]      ,   __magic_name__	:    List[Any]=False	)							->     List[Any]:




						for i in range(config.num_hidden_layers	):
												if base_model:
																		lowercase   :       Any								=''
												else:
																		lowercase   :       Tuple								='vit.'
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												lowercase   :       Optional[Any]								=state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''	)
												lowercase   :       Any								=state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''	)
												# next, add query, keys and values (in that order) to the state dict
												lowercase   :       str								=in_proj_weight[
												    : config.hidden_size, :
												]
												lowercase   :       Optional[Any]								=in_proj_bias[: config.hidden_size]
												lowercase   :       List[Any]								=in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowercase   :       Any								=in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowercase   :       Any								=in_proj_weight[
												    -config.hidden_size :, :
												]
												lowercase   :       Union[str, Any]								=in_proj_bias[-config.hidden_size :]






def 							_lowerCAmelCase (      __magic_name__	:    Dict	)							->     Dict:
						lowercase   :       List[str]								=['head.weight', 'head.bias']
						for k in ignore_keys:
												state_dict.pop(__lowercase      ,   __lowercase	)






def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    Tuple      ,   __magic_name__	:    Optional[int]	)							->     List[str]:
						lowercase   :       Any								=dct.pop(__lowercase	)
						lowercase   :       str								=val






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






@torch.no_grad()
def 							_lowerCAmelCase (      __magic_name__	:    Tuple      ,   __magic_name__	:    str	)							->     Dict:
						lowercase   :       Optional[Any]								=ViTConfig()
						lowercase   :       str								=False
						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						if vit_name[-5:] == "in21k":
												lowercase   :       Optional[int]								=True
												lowercase   :       List[Any]								=int(vit_name[-12:-10]	)
												lowercase   :       List[str]								=int(vit_name[-9:-6]	)
						else:
												lowercase   :       Optional[Any]								=1000
												lowercase   :       str								='huggingface/label-files'
												lowercase   :       Optional[int]								='imagenet-1k-id2label.json'
												lowercase   :       Any								=json.load(open(hf_hub_download(__lowercase      ,   __lowercase      ,   repo_type='''dataset'''	)      ,   '''r'''	)	)
												lowercase   :       int								={int(__lowercase	): v for k, v in idalabel.items()}
												lowercase   :       List[Any]								=idalabel
												lowercase   :       Any								={v: k for k, v in idalabel.items()}
												lowercase   :       Optional[Any]								=int(vit_name[-6:-4]	)
												lowercase   :       Tuple								=int(vit_name[-3:]	)
						# size of the architecture
						if "deit" in vit_name:
												if vit_name[9:].startswith('''tiny'''	):
																		lowercase   :       str								=192
																		lowercase   :       List[Any]								=768
																		lowercase   :       int								=12
																		lowercase   :       Dict								=3
												elif vit_name[9:].startswith('''small'''	):
																		lowercase   :       str								=384
																		lowercase   :       List[str]								=1536
																		lowercase   :       Dict								=12
																		lowercase   :       Any								=6
												else:
																		pass
						else:
												if vit_name[4:].startswith('''small'''	):
																		lowercase   :       Union[str, Any]								=768
																		lowercase   :       Optional[Any]								=2304
																		lowercase   :       int								=8
																		lowercase   :       int								=8
												elif vit_name[4:].startswith('''base'''	):
																		pass
												elif vit_name[4:].startswith('''large'''	):
																		lowercase   :       Dict								=1024
																		lowercase   :       List[Any]								=4096
																		lowercase   :       Tuple								=24
																		lowercase   :       Union[str, Any]								=16
												elif vit_name[4:].startswith('''huge'''	):
																		lowercase   :       Tuple								=1280
																		lowercase   :       Optional[Any]								=5120
																		lowercase   :       List[Any]								=32
																		lowercase   :       Optional[Any]								=16

    # load original model from timm
						lowercase   :       Dict								=timm.create_model(__lowercase      ,   pretrained=__lowercase	)
						timm_model.eval()

						# load state_dict of original model, remove and rename some keys
						lowercase   :       List[Any]								=timm_model.state_dict()
						if base_model:
												remove_classification_head_(__lowercase	)
						lowercase   :       Optional[int]								=create_rename_keys(__lowercase      ,   __lowercase	)
						for src, dest in rename_keys:
												rename_key(__lowercase      ,   __lowercase      ,   __lowercase	)
						read_in_q_k_v(__lowercase      ,   __lowercase      ,   __lowercase	)

						# load HuggingFace model
						if vit_name[-5:] == "in21k":
												lowercase   :       Union[str, Any]								=ViTModel(__lowercase	).eval()
						else:
												lowercase   :       Any								=ViTForImageClassification(__lowercase	).eval()
						model.load_state_dict(__lowercase	)

						# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
						if "deit" in vit_name:
												lowercase   :       Optional[Any]								=DeiTImageProcessor(size=config.image_size	)
						else:
												lowercase   :       Tuple								=ViTImageProcessor(size=config.image_size	)
						lowercase   :       List[str]								=image_processor(images=prepare_img()      ,   return_tensors='''pt'''	)
						lowercase   :       List[Any]								=encoding['pixel_values']
						lowercase   :       Union[str, Any]								=model(__lowercase	)

						if base_model:
												lowercase   :       int								=timm_model.forward_features(__lowercase	)
												assert timm_pooled_output.shape == outputs.pooler_output.shape
												assert torch.allclose(__lowercase      ,   outputs.pooler_output      ,   atol=1E-3	)
						else:
												lowercase   :       Union[str, Any]								=timm_model(__lowercase	)
												assert timm_logits.shape == outputs.logits.shape
												assert torch.allclose(__lowercase      ,   outputs.logits      ,   atol=1E-3	)

						Path(__lowercase	).mkdir(exist_ok=__lowercase	)
						print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''	)
						model.save_pretrained(__lowercase	)
						print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
						image_processor.save_pretrained(__lowercase	)


if __name__ == "__main__":
							UpperCamelCase_							=					argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--vit_name""",
							    default="""vit_base_patch16_224""",
							    type=str,
							    help="""Name of the ViT 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."""
							)

							UpperCamelCase_							=					parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








'''simple docstring'''





import math






def 							_lowerCAmelCase (      __magic_name__	:    float      ,   __magic_name__	:    float	)							->     float:
						if (
						    not isinstance(__magic_name__      ,   (int, float)	)
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError('''power_factor must be a valid float value between -1 and 1.'''	)
						return apparent_power * power_factor






def 							_lowerCAmelCase (      __magic_name__	:    float      ,   __magic_name__	:    float	)							->     float:
						if (
						    not isinstance(__magic_name__      ,   (int, float)	)
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError('''power_factor must be a valid float value between -1 and 1.'''	)
						return apparent_power * math.sqrt(1 - power_factor**2	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





