



"""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()
_lowercase : str      						=						logging.get_logger(__name__)






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






def      snake_case__   (		__lowerCamelCase				:    Optional[int]	):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[int]			=['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(__lowerCamelCase							,		__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__		:		int			=dct.pop(__lowerCamelCase	)
			lowerCamelCase__		:		int			=val






def      snake_case__   (		):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]			='''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase__		:		Tuple			=Image.open(requests.get(__lowerCamelCase							,		stream=__lowerCamelCase	).raw	)
			return im






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

    # load original model from timm
			lowerCamelCase__		:		str			=timm.create_model(__lowerCamelCase							,		pretrained=__lowerCamelCase	)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			lowerCamelCase__		:		Tuple			=timm_model.state_dict()
			if base_model:
						remove_classification_head_(__lowerCamelCase	)
			lowerCamelCase__		:		str			=create_rename_keys(__lowerCamelCase							,		__lowerCamelCase	)
			for src, dest in rename_keys:
						rename_key(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)
			read_in_q_k_v(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						lowerCamelCase__		:		Tuple			=ViTModel(__lowerCamelCase	).eval()
			else:
						lowerCamelCase__		:		Optional[Any]			=ViTForImageClassification(__lowerCamelCase	).eval()
			model.load_state_dict(__lowerCamelCase	)

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

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

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


if __name__ == "__main__":
		_lowercase : str      						=						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."
		)

		_lowercase : int      						=						parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)









"""simple docstring"""






from __future__ import annotations






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			if b == 0:
						return (1, 0)
			((lowerCamelCase__)	,   (lowerCamelCase__))		:		Any			=extended_euclid(__lowerCamelCase							,		a % b	)
			lowerCamelCase__		:		Optional[Any]			=a // b
			return (y, x - k * y)






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			((lowerCamelCase__)	,   (lowerCamelCase__))		:		Any			=extended_euclid(__lowerCamelCase							,		__lowerCamelCase	)
			lowerCamelCase__		:		List[Any]			=na * na
			lowerCamelCase__		:		Union[str, Any]			=ra * x * na + ra * y * na
			return (n % m + m) % m






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			((lowerCamelCase__)	,   (lowerCamelCase__))		:		int			=extended_euclid(__lowerCamelCase							,		__lowerCamelCase	)
			if b < 0:
						lowerCamelCase__		:		Any			=(b % n + n) % n
			return b






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__	,   lowerCamelCase__		:		Any			=invert_modulo(__lowerCamelCase							,		__lowerCamelCase	), invert_modulo(__lowerCamelCase							,		__lowerCamelCase	)
			lowerCamelCase__		:		Tuple			=na * na
			lowerCamelCase__		:		Optional[Any]			=ra * x * na + ra * y * na
			return (n % m + m) % m


if __name__ == "__main__":
		from doctest import testmod

		testmod(name="chinese_remainder_theorem", verbose=True)
		testmod(name="chinese_remainder_theorem2", verbose=True)
		testmod(name="invert_modulo", verbose=True)
		testmod(name="extended_euclid", verbose=True)




