


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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase	=							logging.get_logger(__name__)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False					):
		lowercase__						=				[]

		# fmt: off
		# stem:
		rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')					)
		rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')					)

		rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')					)
		rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')					)

		# backbone
		rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')					)
		rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')					)
		rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')					)

		for stage_idx in range(len(config.backbone_config.depths					)					):
				for layer_idx in range(config.backbone_config.depths[stage_idx]					):
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')					)

				rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')					)
				rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')					)
				rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')					)

		# transformer encoder
		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')					)

		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__						=				[(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'''),
				    ]					)
		# fmt: on

		return rename_keys
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False					):




		for i in range(config.num_hidden_layers					):
				if base_model:
						lowercase__						=				''''''
				else:
						lowercase__						=				'''vit.'''
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowercase__						=				state_dict.pop(f'blocks.{i}.attn.qkv.weight'					)
				lowercase__						=				state_dict.pop(f'blocks.{i}.attn.qkv.bias'					)
				# next, add query, keys and values (in that order) to the state dict
				lowercase__						=				in_proj_weight[
				    : config.hidden_size, :
				]
				lowercase__						=				in_proj_bias[: config.hidden_size]
				lowercase__						=				in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowercase__						=				in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowercase__						=				in_proj_weight[
				    -config.hidden_size :, :
				]
				lowercase__						=				in_proj_bias[-config.hidden_size :]
def       _a			(				SCREAMING_SNAKE_CASE					):
		lowercase__						=				['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):
		lowercase__						=				dct.pop(SCREAMING_SNAKE_CASE					)
		lowercase__						=				val
def       _a			(				):
		lowercase__						=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__						=				Image.open(requests.get(SCREAMING_SNAKE_CASE						,    stream=SCREAMING_SNAKE_CASE					).raw					)
		return im
@torch.no_grad()
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False					):
		lowercase__						=				BitConfig(
		    global_padding='''same'''						,    layer_type='''bottleneck'''						,    depths=(3, 4, 9)						,    out_features=['''stage3''']						,    embedding_dynamic_padding=SCREAMING_SNAKE_CASE						,    )
		lowercase__						=				ViTHybridConfig(backbone_config=SCREAMING_SNAKE_CASE						,    image_size=3_84						,    num_labels=10_00					)
		lowercase__						=				False

		# load original model from timm
		lowercase__						=				timm.create_model(SCREAMING_SNAKE_CASE						,    pretrained=SCREAMING_SNAKE_CASE					)
		timm_model.eval()

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

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

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				lowercase__						=				ViTHybridModel(SCREAMING_SNAKE_CASE					).eval()
		else:
				lowercase__						=				ViTHybridForImageClassification(SCREAMING_SNAKE_CASE					).eval()
		model.load_state_dict(SCREAMING_SNAKE_CASE					)

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

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

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

		lowercase__						=				prepare_img()
		lowercase__						=				transform(SCREAMING_SNAKE_CASE					).unsqueeze(0					)
		lowercase__						=				processor(SCREAMING_SNAKE_CASE						,    return_tensors='''pt'''					).pixel_values

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

		# verify logits
		with torch.no_grad():
				lowercase__						=				model(SCREAMING_SNAKE_CASE					)
				lowercase__						=				outputs.logits

		print('''Predicted class:'''						,    logits.argmax(-1					).item()					)
		if base_model:
				lowercase__						=				timm_model.forward_features(SCREAMING_SNAKE_CASE					)
				assert timm_pooled_output.shape == outputs.pooler_output.shape
				assert torch.allclose(SCREAMING_SNAKE_CASE						,    outputs.pooler_output						,    atol=1E-3					)
		else:
				lowercase__						=				timm_model(SCREAMING_SNAKE_CASE					)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(SCREAMING_SNAKE_CASE						,    outputs.logits						,    atol=1E-3					)
		print('''Looks ok!'''					)

		if pytorch_dump_folder_path is not None:
				Path(SCREAMING_SNAKE_CASE					).mkdir(exist_ok=SCREAMING_SNAKE_CASE					)
				print(f'Saving model {vit_name} to {pytorch_dump_folder_path}'					)
				model.save_pretrained(SCREAMING_SNAKE_CASE					)
				print(f'Saving processor to {pytorch_dump_folder_path}'					)
				processor.save_pretrained(SCREAMING_SNAKE_CASE					)

		if push_to_hub:
				print(f'Pushing model and processor to the hub {vit_name}'					)
				model.push_to_hub(f'ybelkada/{vit_name}'					)
				processor.push_to_hub(f'ybelkada/{vit_name}'					)


if __name__ == "__main__":
			lowerCAmelCase	=							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--vit_name',
			    default='vit_base_r50_s16_384',
			    type=str,
			    help='Name of the hybrid 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.'
			)
			parser.add_argument(
			    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
			)

			lowerCAmelCase	=							parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
lowerCAmelCase	=							logging.getLogger(__name__)
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE					)
		lowercase__						=				{
		    '''repo_id''': str(SCREAMING_SNAKE_CASE					),
		    '''repo_sha''': str(repo.head.object.hexsha					),
		    '''repo_branch''': str(repo.active_branch					),
		}

		with open(os.path.join(SCREAMING_SNAKE_CASE						,    '''git_log.json'''					)						,    '''w'''					) as f:
				json.dump(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    indent=4					)
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if params.n_gpu <= 0:
				lowercase__						=				0
				lowercase__						=				-1
				lowercase__						=				True
				lowercase__						=				False
				return

		assert torch.cuda.is_available()

		logger.info('''Initializing GPUs'''					)
		if params.n_gpu > 1:
				assert params.local_rank != -1

				lowercase__						=				int(os.environ['''WORLD_SIZE''']					)
				lowercase__						=				int(os.environ['''N_GPU_NODE''']					)
				lowercase__						=				int(os.environ['''RANK''']					)

				# number of nodes / node ID
				lowercase__						=				params.world_size // params.n_gpu_per_node
				lowercase__						=				params.global_rank // params.n_gpu_per_node
				lowercase__						=				True

				assert params.n_nodes == int(os.environ['''N_NODES''']					)
				assert params.node_id == int(os.environ['''NODE_RANK''']					)

		# local job (single GPU)
		else:
				assert params.local_rank == -1

				lowercase__						=				1
				lowercase__						=				0
				lowercase__						=				0
				lowercase__						=				0
				lowercase__						=				1
				lowercase__						=				1
				lowercase__						=				False

		# sanity checks
		assert params.n_nodes >= 1
		assert 0 <= params.node_id < params.n_nodes
		assert 0 <= params.local_rank <= params.global_rank < params.world_size
		assert params.world_size == params.n_nodes * params.n_gpu_per_node

		# define whether this is the master process / if we are in multi-node distributed mode
		lowercase__						=				params.node_id == 0 and params.local_rank == 0
		lowercase__						=				params.n_nodes > 1

		# summary
		lowercase__						=				f'--- Global rank: {params.global_rank} - '
		logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes					)
		logger.info(PREFIX + '''Node ID        : %i''' % params.node_id					)
		logger.info(PREFIX + '''Local rank     : %i''' % params.local_rank					)
		logger.info(PREFIX + '''World size     : %i''' % params.world_size					)
		logger.info(PREFIX + '''GPUs per node  : %i''' % params.n_gpu_per_node					)
		logger.info(PREFIX + '''Master         : %s''' % str(params.is_master					)					)
		logger.info(PREFIX + '''Multi-node     : %s''' % str(params.multi_node					)					)
		logger.info(PREFIX + '''Multi-GPU      : %s''' % str(params.multi_gpu					)					)
		logger.info(PREFIX + '''Hostname       : %s''' % socket.gethostname()					)

		# set GPU device
		torch.cuda.set_device(params.local_rank					)

		# initialize multi-GPU
		if params.multi_gpu:
				logger.info('''Initializing PyTorch distributed'''					)
				torch.distributed.init_process_group(
				    init_method='''env://'''						,    backend='''nccl'''						,    )
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		np.random.seed(args.seed					)
		torch.manual_seed(args.seed					)
		if args.n_gpu > 0:
				torch.cuda.manual_seed_all(args.seed					)


