



from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_pt_objects import *  # noqa F403
else:
							from .scheduling_consistency_models import CMStochasticIterativeScheduler
							from .scheduling_ddim import DDIMScheduler
							from .scheduling_ddim_inverse import DDIMInverseScheduler
							from .scheduling_ddim_parallel import DDIMParallelScheduler
							from .scheduling_ddpm import DDPMScheduler
							from .scheduling_ddpm_parallel import DDPMParallelScheduler
							from .scheduling_deis_multistep import DEISMultistepScheduler
							from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
							from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
							from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
							from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
							from .scheduling_euler_discrete import EulerDiscreteScheduler
							from .scheduling_heun_discrete import HeunDiscreteScheduler
							from .scheduling_ipndm import IPNDMScheduler
							from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
							from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
							from .scheduling_karras_ve import KarrasVeScheduler
							from .scheduling_pndm import PNDMScheduler
							from .scheduling_repaint import RePaintScheduler
							from .scheduling_sde_ve import ScoreSdeVeScheduler
							from .scheduling_sde_vp import ScoreSdeVpScheduler
							from .scheduling_unclip import UnCLIPScheduler
							from .scheduling_unipc_multistep import UniPCMultistepScheduler
							from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
							from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_flax_objects import *  # noqa F403
else:
							from .scheduling_ddim_flax import FlaxDDIMScheduler
							from .scheduling_ddpm_flax import FlaxDDPMScheduler
							from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
							from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
							from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
							from .scheduling_pndm_flax import FlaxPNDMScheduler
							from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
							from .scheduling_utils_flax import (
							    FlaxKarrasDiffusionSchedulers,
							    FlaxSchedulerMixin,
							    FlaxSchedulerOutput,
							    broadcast_to_shape_from_left,
							)


try:
							if not (is_torch_available() and is_scipy_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
							from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
							if not (is_torch_available() and is_torchsde_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
							from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler









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()
SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


def A						(       __UpperCamelCase					)						->      YolosConfig:
			A__       =    YolosConfig()

			# size of the architecture
			if "yolos_ti" in yolos_name:
						A__       =    192
						A__       =    768
						A__       =    12
						A__       =    3
						A__       =    [800, 1_333]
						A__       =    False
			elif yolos_name == "yolos_s_dWr":
						A__       =    330
						A__       =    14
						A__       =    6
						A__       =    1_320
			elif "yolos_s" in yolos_name:
						A__       =    384
						A__       =    1_536
						A__       =    12
						A__       =    6
			elif "yolos_b" in yolos_name:
						A__       =    [800, 1_344]

			A__       =    91
			A__       =    'huggingface/label-files'
			A__       =    'coco-detection-id2label.json'
			A__       =    json.load(open(hf_hub_download(__UpperCamelCase ,	__UpperCamelCase ,	repo_type='dataset'					) ,	'r'					)					)
			A__       =    {int(__UpperCamelCase					): v for k, v in idalabel.items()}
			A__       =    idalabel
			A__       =    {v: k for k, v in idalabel.items()}

			return config


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = False					)						->      str:
			for i in range(config.num_hidden_layers					):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						A__       =    state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''					)
						A__       =    state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''					)
						# next, add query, keys and values (in that order) to the state dict
						A__       =    in_proj_weight[: config.hidden_size, :]
						A__       =    in_proj_bias[: config.hidden_size]
						A__       =    in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						A__       =    in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						A__       =    in_proj_weight[-config.hidden_size :, :]
						A__       =    in_proj_bias[-config.hidden_size :]


def A						(       __UpperCamelCase					)						->      str:
			if "backbone" in name:
						A__       =    name.replace('backbone' ,	'vit'					)
			if "cls_token" in name:
						A__       =    name.replace('cls_token' ,	'embeddings.cls_token'					)
			if "det_token" in name:
						A__       =    name.replace('det_token' ,	'embeddings.detection_tokens'					)
			if "mid_pos_embed" in name:
						A__       =    name.replace('mid_pos_embed' ,	'encoder.mid_position_embeddings'					)
			if "pos_embed" in name:
						A__       =    name.replace('pos_embed' ,	'embeddings.position_embeddings'					)
			if "patch_embed.proj" in name:
						A__       =    name.replace('patch_embed.proj' ,	'embeddings.patch_embeddings.projection'					)
			if "blocks" in name:
						A__       =    name.replace('blocks' ,	'encoder.layer'					)
			if "attn.proj" in name:
						A__       =    name.replace('attn.proj' ,	'attention.output.dense'					)
			if "attn" in name:
						A__       =    name.replace('attn' ,	'attention.self'					)
			if "norm1" in name:
						A__       =    name.replace('norm1' ,	'layernorm_before'					)
			if "norm2" in name:
						A__       =    name.replace('norm2' ,	'layernorm_after'					)
			if "mlp.fc1" in name:
						A__       =    name.replace('mlp.fc1' ,	'intermediate.dense'					)
			if "mlp.fc2" in name:
						A__       =    name.replace('mlp.fc2' ,	'output.dense'					)
			if "class_embed" in name:
						A__       =    name.replace('class_embed' ,	'class_labels_classifier'					)
			if "bbox_embed" in name:
						A__       =    name.replace('bbox_embed' ,	'bbox_predictor'					)
			if "vit.norm" in name:
						A__       =    name.replace('vit.norm' ,	'vit.layernorm'					)

			return name


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      dict:
			for key in orig_state_dict.copy().keys():
						A__       =    orig_state_dict.pop(__UpperCamelCase					)

						if "qkv" in key:
									A__       =    key.split('.'					)
									A__       =    int(key_split[2]					)
									A__       =    model.vit.encoder.layer[layer_num].attention.attention.all_head_size
									if "weight" in key:
												A__       =    val[:dim, :]
												A__       =    val[
												    dim : dim * 2, :
												]
												A__       =    val[-dim:, :]
									else:
												A__       =    val[:dim]
												A__       =    val[dim : dim * 2]
												A__       =    val[-dim:]
						else:
									A__       =    val

			return orig_state_dict


def A						(       )						->      torch.Tensor:
			A__       =    'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__       =    Image.open(requests.get(__UpperCamelCase ,	stream=__UpperCamelCase					).raw					)
			return im


@torch.no_grad()
def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = False					)						->      List[str]:
			A__       =    get_yolos_config(__UpperCamelCase					)

			# load original state_dict
			A__       =    torch.load(__UpperCamelCase ,	map_location='cpu'					)['model']

			# load 🤗 model
			A__       =    YolosForObjectDetection(__UpperCamelCase					)
			model.eval()
			A__       =    convert_state_dict(__UpperCamelCase ,	__UpperCamelCase					)
			model.load_state_dict(__UpperCamelCase					)

			# Check outputs on an image, prepared by YolosImageProcessor
			A__       =    800 if yolos_name != 'yolos_ti' else 512
			A__       =    YolosImageProcessor(format='coco_detection' ,	size=__UpperCamelCase					)
			A__       =    image_processor(images=prepare_img() ,	return_tensors='pt'					)
			A__       =    model(**__UpperCamelCase					)
			A__     , A__       =    outputs.logits, outputs.pred_boxes

			A__     , A__       =    None, None
			if yolos_name == "yolos_ti":
						A__       =    torch.tensor(
						    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]					)
						A__       =    torch.tensor(
						    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]					)
			elif yolos_name == "yolos_s_200_pre":
						A__       =    torch.tensor(
						    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]					)
						A__       =    torch.tensor(
						    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]					)
			elif yolos_name == "yolos_s_300_pre":
						A__       =    torch.tensor(
						    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]					)
						A__       =    torch.tensor(
						    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]					)
			elif yolos_name == "yolos_s_dWr":
						A__       =    torch.tensor(
						    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]					)
						A__       =    torch.tensor(
						    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]					)
			elif yolos_name == "yolos_base":
						A__       =    torch.tensor(
						    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]					)
						A__       =    torch.tensor(
						    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]					)
			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:
						A__       =    {
						    '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...'					)
						A__       =    model_mapping[yolos_name]
						image_processor.push_to_hub(__UpperCamelCase ,	organization='hustvl'					)
						model.push_to_hub(__UpperCamelCase ,	organization='hustvl'					)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      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.'''
							)

							SCREAMING_SNAKE_CASE__       =      parser.parse_args()
							convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




