






"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__A						:			str    							=						logging.get_logger(__name__)



def 		lowercase       (							__snake_case							:					int						,   __snake_case							:					str=False      ):
		lowercase_					:					List[Any]							=  []
		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'''),
				    ]      )

				# if just the base model, we should remove "vit" from all keys that start with "vit"
				lowercase_					:					Any							=  [(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 		lowercase       (							__snake_case							:					Dict						,   __snake_case							:					Any						,   __snake_case							:					List[str]=False      ):


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



def 		lowercase       (							__snake_case							:					List[Any]      ):
		lowercase_					:					Optional[int]							=  ['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(SCREAMING_SNAKE_CASE__						,   SCREAMING_SNAKE_CASE__      )



def 		lowercase       (							__snake_case							:					Dict						,   __snake_case							:					List[str]						,   __snake_case							:					Any      ):
		lowercase_					:					List[str]							=  dct.pop(SCREAMING_SNAKE_CASE__      )
		lowercase_					:					Any							=  val



def 		lowercase       (							):
		lowercase_					:					Optional[int]							=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase_					:					Union[str, Any]							=  Image.open(requests.get(SCREAMING_SNAKE_CASE__						,   stream=SCREAMING_SNAKE_CASE__      ).raw      )
		return im



@torch.no_grad()
def 		lowercase       (							__snake_case							:					List[Any]						,   __snake_case							:					List[str]						,   __snake_case							:					Optional[int]=True      ):
		lowercase_					:					str							=  ViTConfig()
		# patch_size
		if model_name[-1] == "8":
				lowercase_					:					Tuple							=  8
		# set labels if required
		if not base_model:
				lowercase_					:					Optional[Any]							=  1_0_0_0
				lowercase_					:					Dict							=  '''huggingface/label-files'''
				lowercase_					:					Dict							=  '''imagenet-1k-id2label.json'''
				lowercase_					:					List[str]							=  json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__						,   SCREAMING_SNAKE_CASE__						,   repo_type='''dataset'''      )						,   '''r'''      )      )
				lowercase_					:					List[Any]							=  {int(SCREAMING_SNAKE_CASE__      ): v for k, v in idalabel.items()}
				lowercase_					:					Union[str, Any]							=  idalabel
				lowercase_					:					Optional[int]							=  {v: k for k, v in idalabel.items()}
		# size of the architecture
		if model_name in ["dino_vits8", "dino_vits16"]:
				lowercase_					:					Optional[Any]							=  3_8_4
				lowercase_					:					List[str]							=  1_5_3_6
				lowercase_					:					Union[str, Any]							=  1_2
				lowercase_					:					Any							=  6

		# load original model from torch hub
		lowercase_					:					Union[str, Any]							=  torch.hub.load('''facebookresearch/dino:main'''						,   SCREAMING_SNAKE_CASE__      )
		original_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowercase_					:					Union[str, Any]							=  original_model.state_dict()
		if base_model:
				remove_classification_head_(SCREAMING_SNAKE_CASE__      )
		lowercase_					:					Optional[int]							=  create_rename_keys(SCREAMING_SNAKE_CASE__						,   base_model=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__      )

		# load HuggingFace model
		if base_model:
				lowercase_					:					Optional[int]							=  ViTModel(SCREAMING_SNAKE_CASE__						,   add_pooling_layer=SCREAMING_SNAKE_CASE__      ).eval()
		else:
				lowercase_					:					Any							=  ViTForImageClassification(SCREAMING_SNAKE_CASE__      ).eval()
		model.load_state_dict(SCREAMING_SNAKE_CASE__      )

		# Check outputs on an image, prepared by ViTImageProcessor
		lowercase_					:					List[str]							=  ViTImageProcessor()
		lowercase_					:					Any							=  image_processor(images=prepare_img()						,   return_tensors='''pt'''      )
		lowercase_					:					Tuple							=  encoding['''pixel_values''']
		lowercase_					:					Tuple							=  model(SCREAMING_SNAKE_CASE__      )

		if base_model:
				lowercase_					:					str							=  original_model(SCREAMING_SNAKE_CASE__      )
				assert torch.allclose(SCREAMING_SNAKE_CASE__						,   outputs.last_hidden_state[:, 0, :]						,   atol=1e-1      )
		else:
				lowercase_					:					Tuple							=  original_model(SCREAMING_SNAKE_CASE__      )
				assert logits.shape == outputs.logits.shape
				assert torch.allclose(SCREAMING_SNAKE_CASE__						,   outputs.logits						,   atol=1e-3      )

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


if __name__ == "__main__":
						__A						:			Optional[int]    							=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--model_name''',
						    default='''dino_vitb16''',
						    type=str,
						    help='''Name of the model trained with DINO 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(
						    '''--base_model''',
						    action='''store_true''',
						    help='''Whether to only convert the base model (no projection head weights).''',
						)

						parser.set_defaults(base_model=True)
						__A						:			Any    							=						parser.parse_args()
						convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""yolos"""



		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Optional[Any]=768  ,	UpperCamelCase__				:			Any=12  ,	UpperCamelCase__				:			Optional[int]=12  ,	UpperCamelCase__				:			Union[str, Any]=3072  ,	UpperCamelCase__				:			str="gelu"  ,	UpperCamelCase__				:			Union[str, Any]=0.0  ,	UpperCamelCase__				:			Union[str, Any]=0.0  ,	UpperCamelCase__				:			Union[str, Any]=0.02  ,	UpperCamelCase__				:			List[Any]=1e-1_2  ,	UpperCamelCase__				:			List[Any]=[512, 864]  ,	UpperCamelCase__				:			int=16  ,	UpperCamelCase__				:			Union[str, Any]=3  ,	UpperCamelCase__				:			Union[str, Any]=True  ,	UpperCamelCase__				:			int=100  ,	UpperCamelCase__				:			Optional[Any]=True  ,	UpperCamelCase__				:			int=False  ,	UpperCamelCase__				:			Dict=1  ,	UpperCamelCase__				:			Optional[int]=5  ,	UpperCamelCase__				:			List[str]=2  ,	UpperCamelCase__				:			Union[str, Any]=5  ,	UpperCamelCase__				:			Union[str, Any]=2  ,	UpperCamelCase__				:			int=0.1  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Any:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       List[str]     			=						hidden_size
				snake_case  :       Union[str, Any]     			=						num_hidden_layers
				snake_case  :       List[str]     			=						num_attention_heads
				snake_case  :       Any     			=						intermediate_size
				snake_case  :       int     			=						hidden_act
				snake_case  :       Optional[Any]     			=						hidden_dropout_prob
				snake_case  :       int     			=						attention_probs_dropout_prob
				snake_case  :       Union[str, Any]     			=						initializer_range
				snake_case  :       List[Any]     			=						layer_norm_eps
				snake_case  :       Tuple     			=						image_size
				snake_case  :       Any     			=						patch_size
				snake_case  :       str     			=						num_channels
				snake_case  :       Optional[int]     			=						qkv_bias
				snake_case  :       Optional[Any]     			=						num_detection_tokens
				snake_case  :       Dict     			=						use_mid_position_embeddings
				snake_case  :       List[Any]     			=						auxiliary_loss
				# Hungarian matcher
				snake_case  :       List[str]     			=						class_cost
				snake_case  :       Optional[Any]     			=						bbox_cost
				snake_case  :       Tuple     			=						giou_cost
				# Loss coefficients
				snake_case  :       Union[str, Any]     			=						bbox_loss_coefficient
				snake_case  :       List[Any]     			=						giou_loss_coefficient
				snake_case  :       Tuple     			=						eos_coefficient







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			float:






				"""simple docstring"""







				return 1e-4



		@property
		def       lowerCAmelCase				(      self				:			Tuple      )						->			int:






				"""simple docstring"""







				return 12


