


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        UpperCamelCase_(				_A   :int				,     _A   :Optional[Any]=False  )->		List[Any]:
			UpperCamelCase__					=       []
			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"
						UpperCamelCase__					=       [(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        UpperCamelCase_(				_A   :List[Any]				,     _A   :Tuple				,     _A   :int=False  )->		Union[str, Any]:
			for i in range(config.num_hidden_layers  ):
						if base_model:
									UpperCamelCase__					=       ""
						else:
									UpperCamelCase__					=       "vit."
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						UpperCamelCase__					=       state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''  )
						UpperCamelCase__					=       state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''  )
						# next, add query, keys and values (in that order) to the state dict
						UpperCamelCase__					=       in_proj_weight[
						    : config.hidden_size, :
						]
						UpperCamelCase__					=       in_proj_bias[: config.hidden_size]
						UpperCamelCase__					=       in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCamelCase__					=       in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCamelCase__					=       in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCamelCase__					=       in_proj_bias[-config.hidden_size :]
def        UpperCamelCase_(				_A   :int  )->		Dict:
			UpperCamelCase__					=       ["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(_A				,     _A  )
def        UpperCamelCase_(				_A   :List[Any]				,     _A   :Union[str, Any]				,     _A   :int  )->		str:
			UpperCamelCase__					=       dct.pop(_A  )
			UpperCamelCase__					=       val
def        UpperCamelCase_(				)->		List[str]:
			UpperCamelCase__					=       "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCamelCase__					=       Image.open(requests.get(_A				,     stream=_A  ).raw  )
			return im
@torch.no_grad()
def        UpperCamelCase_(				_A   :int				,     _A   :Optional[Any]  )->		Dict:
			UpperCamelCase__					=       ViTConfig()
			UpperCamelCase__					=       False
			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			if vit_name[-5:] == "in21k":
						UpperCamelCase__					=       True
						UpperCamelCase__					=       int(vit_name[-12:-10]  )
						UpperCamelCase__					=       int(vit_name[-9:-6]  )
			else:
						UpperCamelCase__					=       10_00
						UpperCamelCase__					=       "huggingface/label-files"
						UpperCamelCase__					=       "imagenet-1k-id2label.json"
						UpperCamelCase__					=       json.load(open(hf_hub_download(_A				,     _A				,     repo_type="dataset"  )				,     "r"  )  )
						UpperCamelCase__					=       {int(_A  ): v for k, v in idalabel.items()}
						UpperCamelCase__					=       idalabel
						UpperCamelCase__					=       {v: k for k, v in idalabel.items()}
						UpperCamelCase__					=       int(vit_name[-6:-4]  )
						UpperCamelCase__					=       int(vit_name[-3:]  )
			# size of the architecture
			if "deit" in vit_name:
						if vit_name[9:].startswith("tiny"  ):
									UpperCamelCase__					=       1_92
									UpperCamelCase__					=       7_68
									UpperCamelCase__					=       12
									UpperCamelCase__					=       3
						elif vit_name[9:].startswith("small"  ):
									UpperCamelCase__					=       3_84
									UpperCamelCase__					=       15_36
									UpperCamelCase__					=       12
									UpperCamelCase__					=       6
						else:
									pass
			else:
						if vit_name[4:].startswith("small"  ):
									UpperCamelCase__					=       7_68
									UpperCamelCase__					=       23_04
									UpperCamelCase__					=       8
									UpperCamelCase__					=       8
						elif vit_name[4:].startswith("base"  ):
									pass
						elif vit_name[4:].startswith("large"  ):
									UpperCamelCase__					=       10_24
									UpperCamelCase__					=       40_96
									UpperCamelCase__					=       24
									UpperCamelCase__					=       16
						elif vit_name[4:].startswith("huge"  ):
									UpperCamelCase__					=       12_80
									UpperCamelCase__					=       51_20
									UpperCamelCase__					=       32
									UpperCamelCase__					=       16

    # load original model from timm
			UpperCamelCase__					=       timm.create_model(_A				,     pretrained=_A  )
			timm_model.eval()

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

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						UpperCamelCase__					=       ViTModel(_A  ).eval()
			else:
						UpperCamelCase__					=       ViTForImageClassification(_A  ).eval()
			model.load_state_dict(_A  )

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

			if base_model:
						UpperCamelCase__					=       timm_model.forward_features(_A  )
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(_A				,     outputs.pooler_output				,     atol=1e-3  )
			else:
						UpperCamelCase__					=       timm_model(_A  )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(_A				,     outputs.logits				,     atol=1e-3  )

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


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)






from __future__ import annotations

import numpy as np
def        UpperCamelCase_(				_A   :list[float]  )->		Union[str, Any]:
			return np.maximum(0				,     _A  )


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


