
'''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 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()
_lowercase			:  List[str]   							=	logging.get_logger(__name__)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[int]=False							):



						"""simple docstring"""





						lowercase_     :					int													=	[]

						# 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_     :					Dict													=	[(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  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Tuple=False							):



						"""simple docstring"""





						for i in range(config.num_hidden_layers							):
												if base_model:
																		lowercase_     :					str													=	''''''
												else:
																		lowercase_     :					List[Any]													=	'''vit.'''
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												lowercase_     :					Union[str, 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_     :					Any													=	in_proj_weight[
												    : config.hidden_size, :
												]
												lowercase_     :					Tuple													=	in_proj_bias[: config.hidden_size]
												lowercase_     :					Optional[Any]													=	in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowercase_     :					Optional[int]													=	in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowercase_     :					List[str]													=	in_proj_weight[
												    -config.hidden_size :, :
												]
												lowercase_     :					int													=	in_proj_bias[-config.hidden_size :]
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	['''head.weight''', '''head.bias''']
						for k in ignore_keys:
												state_dict.pop(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			List[str]				,				__SCREAMING_SNAKE_CASE :			List[str]							):



						"""simple docstring"""





						lowercase_     :					Dict													=	dct.pop(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Tuple													=	val
def  snake_case_		(    ):



						"""simple docstring"""





						lowercase_     :					Any													=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowercase_     :					Any													=	Image.open(requests.get(__SCREAMING_SNAKE_CASE				,				stream=__SCREAMING_SNAKE_CASE							).raw							)
						return im
@torch.no_grad()
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			int=False							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	BitConfig(
						    global_padding='''same'''				,				layer_type='''bottleneck'''				,				depths=(3, 4, 9)				,				out_features=['''stage3''']				,				embedding_dynamic_padding=__SCREAMING_SNAKE_CASE				,				)
						lowercase_     :					Optional[int]													=	ViTHybridConfig(backbone_config=__SCREAMING_SNAKE_CASE				,				image_size=384				,				num_labels=1000							)
						lowercase_     :					Union[str, Any]													=	False

						# load original model from timm
						lowercase_     :					Any													=	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_     :					Optional[Any]													=	timm_model.state_dict()
						if base_model:
												remove_classification_head_(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Any													=	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_     :					str													=	'''huggingface/label-files'''
						lowercase_     :					List[Any]													=	'''imagenet-1k-id2label.json'''
						lowercase_     :					List[str]													=	json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				repo_type='''dataset'''							)				,				'''r'''							)							)
						lowercase_     :					Tuple													=	{int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
						lowercase_     :					str													=	idalabel
						lowercase_     :					Any													=	{v: k for k, v in idalabel.items()}

						# load HuggingFace model
						if vit_name[-5:] == "in21k":
												lowercase_     :					Tuple													=	ViTHybridModel(__SCREAMING_SNAKE_CASE							).eval()
						else:
												lowercase_     :					Optional[int]													=	ViTHybridForImageClassification(__SCREAMING_SNAKE_CASE							).eval()
						model.load_state_dict(__SCREAMING_SNAKE_CASE							)

						# create image processor
						lowercase_     :					Dict													=	create_transform(**resolve_data_config({}				,				model=__SCREAMING_SNAKE_CASE							)							)
						lowercase_     :					Optional[int]													=	transform.transforms

						lowercase_     :					Optional[Any]													=	{
						    '''bilinear''': PILImageResampling.BILINEAR,
						    '''bicubic''': PILImageResampling.BICUBIC,
						    '''nearest''': PILImageResampling.NEAREST,
						}

						lowercase_     :					Tuple													=	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_     :					List[Any]													=	prepare_img()
						lowercase_     :					int													=	transform(__SCREAMING_SNAKE_CASE							).unsqueeze(0							)
						lowercase_     :					int													=	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_     :					Optional[int]													=	model(__SCREAMING_SNAKE_CASE							)
												lowercase_     :					List[str]													=	outputs.logits

						print('''Predicted class:'''				,				logits.argmax(-1							).item()							)
						if base_model:
												lowercase_     :					Optional[Any]													=	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_     :					int													=	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__":
							_lowercase			:  Optional[int]   							=	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."
							)

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





'''simple docstring'''







import sys

_lowercase			:  Union[str, Any]   							=	(
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					Any													=	1
						for digit in s:
												product *= int(__SCREAMING_SNAKE_CASE							)
						return product
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str = N							):



						"""simple docstring"""





						lowercase_     :					Any													=	-sys.maxsize - 1
						lowercase_     :					List[Any]													=	n[:13]
						lowercase_     :					List[str]													=	13
						while cur_index < len(__SCREAMING_SNAKE_CASE							) - 13:
												if int(n[cur_index]							) >= int(substr[0]							):
																		lowercase_     :					Tuple													=	substr[1:] + n[cur_index]
																		cur_index += 1
												else:
																		lowercase_     :					str													=	max(__SCREAMING_SNAKE_CASE				,				str_eval(__SCREAMING_SNAKE_CASE							)							)
																		lowercase_     :					List[Any]													=	n[cur_index : cur_index + 13]
																		cur_index += 13
						return largest_product


if __name__ == "__main__":
							print(f"""{solution() = }""")



