


'''simple docstring'''


__magic_name__								=   [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages



'''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()
UpperCAmelCase					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=YolosConfig()

     # size of the architecture
     if "yolos_ti" in yolos_name:
          __lowercase      		=1_92
          __lowercase      		=7_68
          __lowercase      		=12
          __lowercase      		=3
          __lowercase      		=[8_00, 13_33]
          __lowercase      		=False
     elif yolos_name == "yolos_s_dWr":
          __lowercase      		=3_30
          __lowercase      		=14
          __lowercase      		=6
          __lowercase      		=13_20
     elif "yolos_s" in yolos_name:
          __lowercase      		=3_84
          __lowercase      		=15_36
          __lowercase      		=12
          __lowercase      		=6
     elif "yolos_b" in yolos_name:
          __lowercase      		=[8_00, 13_44]

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

     return config

def 					__UpperCamelCase				(						lowercase__    :	dict,       lowercase__    :	YolosConfig,       lowercase__    :	bool = False			):






     '''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)
          __lowercase      		=state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''			)
          __lowercase      		=state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''			)
          # next, add query, keys and values (in that order) to the state dict
          __lowercase      		=in_proj_weight[: config.hidden_size, :]
          __lowercase      		=in_proj_bias[: config.hidden_size]
          __lowercase      		=in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __lowercase      		=in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __lowercase      		=in_proj_weight[-config.hidden_size :, :]
          __lowercase      		=in_proj_bias[-config.hidden_size :]

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





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

     return name

def 					__UpperCamelCase				(						lowercase__    :	dict,       lowercase__    :	YolosForObjectDetection			):






     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          __lowercase      		=orig_state_dict.pop(lowercase__			)

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

     return orig_state_dict

def 					__UpperCamelCase				(						):






     '''simple docstring'''





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

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str,       lowercase__    :	str,       lowercase__    :	bool = False			):






     '''simple docstring'''





     __lowercase      		=get_yolos_config(lowercase__			)

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

     # load 🤗 model
     __lowercase      		=YolosForObjectDetection(lowercase__			)
     model.eval()
     __lowercase      		=convert_state_dict(lowercase__,       lowercase__			)
     model.load_state_dict(lowercase__			)

     # Check outputs on an image, prepared by YolosImageProcessor
     __lowercase      		=8_00 if yolos_name != 'yolos_ti' else 5_12
     __lowercase      		=YolosImageProcessor(format='coco_detection',       size=lowercase__			)
     __lowercase      		=image_processor(images=prepare_img(),       return_tensors='pt'			)
     __lowercase      		=model(**lowercase__			)
     __lowercase		,  __lowercase      		=outputs.logits, outputs.pred_boxes

     __lowercase		,  __lowercase      		=None, None
     if yolos_name == "yolos_ti":
          __lowercase      		=torch.tensor(
              [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]			)
          __lowercase      		=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":
          __lowercase      		=torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]			)
          __lowercase      		=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":
          __lowercase      		=torch.tensor(
              [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]			)
          __lowercase      		=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":
          __lowercase      		=torch.tensor(
              [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]			)
          __lowercase      		=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":
          __lowercase      		=torch.tensor(
              [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]			)
          __lowercase      		=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],       lowercase__,       atol=1E-4			)
     assert torch.allclose(pred_boxes[0, :3, :3],       lowercase__,       atol=1E-4			)

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

     if push_to_hub:
          __lowercase      		={
              '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...'			)
          __lowercase      		=model_mapping[yolos_name]
          image_processor.push_to_hub(lowercase__,       organization='hustvl'			)
          model.push_to_hub(lowercase__,       organization='hustvl'			)


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

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


