"""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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_a					:					List[Any]=			logging.get_logger(__name__)
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str  )							->      YolosConfig:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			YolosConfig()

	# size of the architecture
	if "yolos_ti" in yolos_name:
		__snake_case      :							Optional[Any]						=			1_92
		__snake_case      :							Optional[Any]						=			7_68
		__snake_case      :							int						=			12
		__snake_case      :							Dict						=			3
		__snake_case      :							Tuple						=			[8_00, 13_33]
		__snake_case      :							Tuple						=			False
	elif yolos_name == "yolos_s_dWr":
		__snake_case      :							List[Any]						=			3_30
		__snake_case      :							List[str]						=			14
		__snake_case      :							Union[str, Any]						=			6
		__snake_case      :							Dict						=			13_20
	elif "yolos_s" in yolos_name:
		__snake_case      :							Optional[int]						=			3_84
		__snake_case      :							Tuple						=			15_36
		__snake_case      :							str						=			12
		__snake_case      :							int						=			6
	elif "yolos_b" in yolos_name:
		__snake_case      :							Optional[int]						=			[8_00, 13_44]

	__snake_case      :							str						=			91
	__snake_case      :							int						=			'huggingface/label-files'
	__snake_case      :							List[Any]						=			'coco-detection-id2label.json'
	__snake_case      :							int						=			json.load(open(hf_hub_download(UpperCAmelCase_      ,							UpperCAmelCase_      ,							repo_type='dataset'  )      ,							'r'  )  )
	__snake_case      :							Optional[Any]						=			{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
	__snake_case      :							Union[str, Any]						=			idalabel
	__snake_case      :							Union[str, Any]						=			{v: k for k, v in idalabel.items()}

	return config
def   __UpperCAmelCase    (   UpperCAmelCase_   :    dict      ,							UpperCAmelCase_   :    YolosConfig      ,							UpperCAmelCase_   :    bool = False  )							->      str:

	'''simple docstring'''





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

	'''simple docstring'''





	if "backbone" in name:
		__snake_case      :							Dict						=			name.replace('backbone'      ,							'vit'  )
	if "cls_token" in name:
		__snake_case      :							Any						=			name.replace('cls_token'      ,							'embeddings.cls_token'  )
	if "det_token" in name:
		__snake_case      :							Optional[int]						=			name.replace('det_token'      ,							'embeddings.detection_tokens'  )
	if "mid_pos_embed" in name:
		__snake_case      :							List[str]						=			name.replace('mid_pos_embed'      ,							'encoder.mid_position_embeddings'  )
	if "pos_embed" in name:
		__snake_case      :							Any						=			name.replace('pos_embed'      ,							'embeddings.position_embeddings'  )
	if "patch_embed.proj" in name:
		__snake_case      :							str						=			name.replace('patch_embed.proj'      ,							'embeddings.patch_embeddings.projection'  )
	if "blocks" in name:
		__snake_case      :							List[Any]						=			name.replace('blocks'      ,							'encoder.layer'  )
	if "attn.proj" in name:
		__snake_case      :							Any						=			name.replace('attn.proj'      ,							'attention.output.dense'  )
	if "attn" in name:
		__snake_case      :							Dict						=			name.replace('attn'      ,							'attention.self'  )
	if "norm1" in name:
		__snake_case      :							Optional[int]						=			name.replace('norm1'      ,							'layernorm_before'  )
	if "norm2" in name:
		__snake_case      :							Optional[Any]						=			name.replace('norm2'      ,							'layernorm_after'  )
	if "mlp.fc1" in name:
		__snake_case      :							List[str]						=			name.replace('mlp.fc1'      ,							'intermediate.dense'  )
	if "mlp.fc2" in name:
		__snake_case      :							Optional[Any]						=			name.replace('mlp.fc2'      ,							'output.dense'  )
	if "class_embed" in name:
		__snake_case      :							Optional[Any]						=			name.replace('class_embed'      ,							'class_labels_classifier'  )
	if "bbox_embed" in name:
		__snake_case      :							int						=			name.replace('bbox_embed'      ,							'bbox_predictor'  )
	if "vit.norm" in name:
		__snake_case      :							Any						=			name.replace('vit.norm'      ,							'vit.layernorm'  )

	return name
def   __UpperCAmelCase    (   UpperCAmelCase_   :    dict      ,							UpperCAmelCase_   :    YolosForObjectDetection  )							->      dict:

	'''simple docstring'''





	for key in orig_state_dict.copy().keys():
		__snake_case      :							int						=			orig_state_dict.pop(UpperCAmelCase_  )

		if "qkv" in key:
			__snake_case      :							str						=			key.split('.'  )
			__snake_case      :							int						=			int(key_split[2]  )
			__snake_case      :							Tuple						=			model.vit.encoder.layer[layer_num].attention.attention.all_head_size
			if "weight" in key:
				__snake_case      :							Any						=			val[:dim, :]
				__snake_case      :							Union[str, Any]						=			val[
				    dim : dim * 2, :
				]
				__snake_case      :							Any						=			val[-dim:, :]
			else:
				__snake_case      :							Optional[int]						=			val[:dim]
				__snake_case      :							List[str]						=			val[dim : dim * 2]
				__snake_case      :							List[Any]						=			val[-dim:]
		else:
			__snake_case      :							Tuple						=			val

	return orig_state_dict
def   __UpperCAmelCase    (   )							->      torch.Tensor:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			'http://images.cocodataset.org/val2017/000000039769.jpg'
	__snake_case      :							List[Any]						=			Image.open(requests.get(UpperCAmelCase_      ,							stream=UpperCAmelCase_  ).raw  )
	return im
@torch.no_grad()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    bool = False  )							->      int:

	'''simple docstring'''





	__snake_case      :							Optional[Any]						=			get_yolos_config(UpperCAmelCase_  )

	# load original state_dict
	__snake_case      :							Union[str, Any]						=			torch.load(UpperCAmelCase_      ,							map_location='cpu'  )['model']

	# load 🤗 model
	__snake_case      :							Optional[Any]						=			YolosForObjectDetection(UpperCAmelCase_  )
	model.eval()
	__snake_case      :							int						=			convert_state_dict(UpperCAmelCase_      ,							UpperCAmelCase_  )
	model.load_state_dict(UpperCAmelCase_  )

	# Check outputs on an image, prepared by YolosImageProcessor
	__snake_case      :							Optional[Any]						=			8_00 if yolos_name != 'yolos_ti' else 5_12
	__snake_case      :							Dict						=			YolosImageProcessor(format='coco_detection'      ,							size=UpperCAmelCase_  )
	__snake_case      :							int						=			image_processor(images=prepare_img()      ,							return_tensors='pt'  )
	__snake_case      :							int						=			model(**UpperCAmelCase_  )
	__snake_case		,    __snake_case      :							List[str]						=			outputs.logits, outputs.pred_boxes

	__snake_case		,    __snake_case      :							Dict						=			None, None
	if yolos_name == "yolos_ti":
		__snake_case      :							Dict						=			torch.tensor(
		    [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]]  )
		__snake_case      :							Tuple						=			torch.tensor(
		    [[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]]  )
	elif yolos_name == "yolos_s_200_pre":
		__snake_case      :							Tuple						=			torch.tensor(
		    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]  )
		__snake_case      :							Tuple						=			torch.tensor(
		    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]  )
	elif yolos_name == "yolos_s_300_pre":
		__snake_case      :							Optional[Any]						=			torch.tensor(
		    [[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]]  )
		__snake_case      :							Tuple						=			torch.tensor(
		    [[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]]  )
	elif yolos_name == "yolos_s_dWr":
		__snake_case      :							str						=			torch.tensor(
		    [[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]]  )
		__snake_case      :							Tuple						=			torch.tensor(
		    [[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]]  )
	elif yolos_name == "yolos_base":
		__snake_case      :							str						=			torch.tensor(
		    [[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]]  )
		__snake_case      :							Dict						=			torch.tensor(
		    [[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]]  )
	else:
		raise ValueError(F"Unknown yolos_name: {yolos_name}"  )

	assert torch.allclose(logits[0, :3, :3]      ,							UpperCAmelCase_      ,							atol=1E-4  )
	assert torch.allclose(pred_boxes[0, :3, :3]      ,							UpperCAmelCase_      ,							atol=1E-4  )

	Path(UpperCAmelCase_  ).mkdir(exist_ok=UpperCAmelCase_  )
	print(F"Saving model {yolos_name} to {pytorch_dump_folder_path}"  )
	model.save_pretrained(UpperCAmelCase_  )
	print(F"Saving image processor to {pytorch_dump_folder_path}"  )
	image_processor.save_pretrained(UpperCAmelCase_  )

	if push_to_hub:
		__snake_case      :							Any						=			{
		    'yolos_ti': 'yolos-tiny',
		    'yolos_s_200_pre': 'yolos-small',
		    'yolos_s_300_pre': 'yolos-small-300',
		    'yolos_s_dWr': 'yolos-small-dwr',
		    'yolos_base': 'yolos-base',
		}

		print('Pushing to the hub...'  )
		__snake_case      :							Optional[int]						=			model_mapping[yolos_name]
		image_processor.push_to_hub(UpperCAmelCase_      ,							organization='hustvl'  )
		model.push_to_hub(UpperCAmelCase_      ,							organization='hustvl'  )


if __name__ == "__main__":
	_a					:					Any=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--yolos_name",
	    default="yolos_s_200_pre",
	    type=str,
	    help=(
	        "Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
	        " 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
	    ),
	)
	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
	)
	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 or not to push the converted model to the 🤗 hub."
	)

	_a					:					str=			parser.parse_args()
	convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_a					:					Tuple=			{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				str   =		"""albert"""



				def __init__(self    :				Union[str, Any]     ,  _A    :				int=3_00_00     ,  _A    :				Any=1_28     ,  _A    :				Tuple=40_96     ,  _A    :				int=12     ,  _A    :				Tuple=1     ,  _A    :				int=64     ,  _A    :				Optional[Any]=1_63_84     ,  _A    :				Optional[Any]=1     ,  _A    :				List[str]="gelu_new"     ,  _A    :				Any=0     ,  _A    :				Optional[Any]=0     ,  _A    :				List[Any]=5_12     ,  _A    :				List[Any]=2     ,  _A    :				Dict=0.02     ,  _A    :				Union[str, Any]=1E-12     ,  _A    :				Tuple=0.1     ,  _A    :				int="absolute"     ,  _A    :				List[Any]=0     ,  _A    :				str=2     ,  _A    :				int=3     ,  **_A    :				Tuple     ,  )   ->     Union[str, Any]:
					super().__init__(pad_token_id=_A     ,  bos_token_id=_A     ,  eos_token_id=_A     ,  **_A)

					__snake_case      :							str						=			vocab_size
					__snake_case      :							List[Any]						=			embedding_size
					__snake_case      :							Tuple						=			hidden_size
					__snake_case      :							Optional[Any]						=			num_hidden_layers
					__snake_case      :							List[str]						=			num_hidden_groups
					__snake_case      :							Dict						=			num_attention_heads
					__snake_case      :							Any						=			inner_group_num
					__snake_case      :							Union[str, Any]						=			hidden_act
					__snake_case      :							List[Any]						=			intermediate_size
					__snake_case      :							List[str]						=			hidden_dropout_prob
					__snake_case      :							int						=			attention_probs_dropout_prob
					__snake_case      :							Any						=			max_position_embeddings
					__snake_case      :							Optional[int]						=			type_vocab_size
					__snake_case      :							Optional[Any]						=			initializer_range
					__snake_case      :							List[Any]						=			layer_norm_eps
					__snake_case      :							Optional[Any]						=			classifier_dropout_prob
					__snake_case      :							List[str]						=			position_embedding_type







class 		UpperCamelCase      (   lowercase						):



				@property
				def  _lowercase					(self    :				Tuple)   ->     Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						__snake_case      :							int						=			{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						__snake_case      :							Optional[Any]						=			{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					        ('token_type_ids', dynamic_axis),
					    ])





