




'''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()
a  :				List[Any]									=  logging.get_logger(__name__)


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

					'''simple docstring'''

					snake_case_									=				[]

					# 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"
										snake_case_									=				[(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 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase=False	)		->					Optional[Any]:

					'''simple docstring'''

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


def 		__magic_name__    (	__UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				['''head.weight''', '''head.bias''']
					for k in ignore_keys:
										state_dict.pop(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				dct.pop(__UpperCAmelCase	)
					snake_case_									=				val


def 		__magic_name__    (	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					snake_case_									=				Image.open(requests.get(__UpperCAmelCase,      stream=__UpperCAmelCase	).raw	)
					return im


@torch.no_grad()
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase=False	)		->					int:

					'''simple docstring'''

					snake_case_									=				BitConfig(
					    global_padding='''same''',      layer_type='''bottleneck''',      depths=(3, 4, 9),      out_features=['''stage3'''],      embedding_dynamic_padding=__UpperCAmelCase,      )
					snake_case_									=				ViTHybridConfig(backbone_config=__UpperCAmelCase,      image_size=384,      num_labels=1000	)
					snake_case_									=				False

					# load original model from timm
					snake_case_									=				timm.create_model(__UpperCAmelCase,      pretrained=__UpperCAmelCase	)
					timm_model.eval()

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

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

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

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

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

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

					snake_case_									=				prepare_img()
					snake_case_									=				transform(__UpperCAmelCase	).unsqueeze(0	)
					snake_case_									=				processor(__UpperCAmelCase,      return_tensors='''pt'''	).pixel_values

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

					# verify logits
					with torch.no_grad():
										snake_case_									=				model(__UpperCAmelCase	)
										snake_case_									=				outputs.logits

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

					if pytorch_dump_folder_path is not None:
										Path(__UpperCAmelCase	).mkdir(exist_ok=__UpperCAmelCase	)
										print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"	)
										model.save_pretrained(__UpperCAmelCase	)
										print(F"Saving processor to {pytorch_dump_folder_path}"	)
										processor.save_pretrained(__UpperCAmelCase	)

					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__":
							a  :				Optional[Any]									=  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.'
							)

							a  :				Optional[int]									=  parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""


import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
       import ctypes
       import msvcrt  # noqa


       class 					snake_case		(							ctypes.Structure						):


          '''simple docstring'''

          A_						:	List[str]						 = [("size", ctypes.c_int), ("visible", ctypes.c_byte)]



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  if os.name == "nt":
    __A									= CursorInfo()
    __A									= ctypes.windll.kernelaa.GetStdHandle(-1_1 )
    ctypes.windll.kernelaa.GetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
    __A									= False
    ctypes.windll.kernelaa.SetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
  elif os.name == "posix":
    sys.stdout.write('''\033[?25l''' )
    sys.stdout.flush()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  if os.name == "nt":
    __A									= CursorInfo()
    __A									= ctypes.windll.kernelaa.GetStdHandle(-1_1 )
    ctypes.windll.kernelaa.GetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
    __A									= True
    ctypes.windll.kernelaa.SetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
  elif os.name == "posix":
    sys.stdout.write('''\033[?25h''' )
    sys.stdout.flush()





@contextmanager
def 					lowerCAmelCase				(    ):




  """simple docstring"""







  try:
    hide_cursor()
    yield
  finally:
    show_cursor()




