





'''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__			:					str       						=						logging.get_logger(__name__)
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str )							->     YolosConfig:

       """simple docstring"""


       UpperCAmelCase  =						YolosConfig()

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

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

       return config
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			dict			,	SCREAMING_SNAKE_CASE_ :			YolosConfig			,	SCREAMING_SNAKE_CASE_ :			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)
              UpperCAmelCase  =						state_dict.pop(f"blocks.{i}.attn.qkv.weight" )
              UpperCAmelCase  =						state_dict.pop(f"blocks.{i}.attn.qkv.bias" )
              # next, add query, keys and values (in that order) to the state dict
              UpperCAmelCase  =						in_proj_weight[: config.hidden_size, :]
              UpperCAmelCase  =						in_proj_bias[: config.hidden_size]
              UpperCAmelCase  =						in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              UpperCAmelCase  =						in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              UpperCAmelCase  =						in_proj_weight[-config.hidden_size :, :]
              UpperCAmelCase  =						in_proj_bias[-config.hidden_size :]
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str )							->     str:

       """simple docstring"""


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

       return name
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			dict			,	SCREAMING_SNAKE_CASE_ :			YolosForObjectDetection )							->     dict:

       """simple docstring"""


       for key in orig_state_dict.copy().keys():
              UpperCAmelCase  =						orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )

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

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

       """simple docstring"""


       UpperCAmelCase  =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       UpperCAmelCase  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,	stream=SCREAMING_SNAKE_CASE_ ).raw )
       return im
@torch.no_grad()
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			bool = False )							->     str:

       """simple docstring"""


       UpperCAmelCase  =						get_yolos_config(SCREAMING_SNAKE_CASE_ )

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

       # load 🤗 model
       UpperCAmelCase  =						YolosForObjectDetection(SCREAMING_SNAKE_CASE_ )
       model.eval()
       UpperCAmelCase  =						convert_state_dict(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )
       model.load_state_dict(SCREAMING_SNAKE_CASE_ )

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

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

       Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
       print(f"Saving model {yolos_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 push_to_hub:
              UpperCAmelCase  =						{
                  '''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...''' )
              UpperCAmelCase  =						model_mapping[yolos_name]
              image_processor.push_to_hub(SCREAMING_SNAKE_CASE_			,	organization='''hustvl''' )
              model.push_to_hub(SCREAMING_SNAKE_CASE_			,	organization='''hustvl''' )


if __name__ == "__main__":
    a__			:					Dict       						=						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__			:					Optional[Any]       						=						parser.parse_args()
    convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float(moles / volume				) * nfactor				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((moles * 0.0_821 * temperature) / (volume)				)				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((moles * 0.0_821 * temperature) / (pressure)				)				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((pressure * volume) / (0.0_821 * moles)				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



