
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()
__lowerCAmelCase    :	List[Any]						=						logging.get_logger(__name__)


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Optional[Any]:
    __lowercase						:	Optional[Any]					=				YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        __lowercase						:	List[str]					=				192
        __lowercase						:	Union[str, Any]					=				768
        __lowercase						:	Dict					=				12
        __lowercase						:	Dict					=				3
        __lowercase						:	Optional[int]					=				[800, 1_333]
        __lowercase						:	str					=				False
    elif yolos_name == "yolos_s_dWr":
        __lowercase						:	Union[str, Any]					=				330
        __lowercase						:	Dict					=				14
        __lowercase						:	Any					=				6
        __lowercase						:	int					=				1_320
    elif "yolos_s" in yolos_name:
        __lowercase						:	List[Any]					=				384
        __lowercase						:	Any					=				1_536
        __lowercase						:	Dict					=				12
        __lowercase						:	Optional[Any]					=				6
    elif "yolos_b" in yolos_name:
        __lowercase						:	int					=				[800, 1_344]

    __lowercase						:	Tuple					=				91
    __lowercase						:	Tuple					=				"huggingface/label-files"
    __lowercase						:	Dict					=				"coco-detection-id2label.json"
    __lowercase						:	List[str]					=				json.load(open(hf_hub_download(__lowerCAmelCase		,    __lowerCAmelCase		,    repo_type='''dataset'''		)		,    '''r'''		)		)
    __lowercase						:	Tuple					=				{int(__lowerCAmelCase		): v for k, v in idalabel.items()}
    __lowercase						:	int					=				idalabel
    __lowercase						:	Tuple					=				{v: k for k, v in idalabel.items()}

    return config


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = False		)       ->  Union[str, Any]:



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


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Union[str, Any]:



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

    return name


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		)       ->  Optional[Any]:



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

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

    return orig_state_dict


def 			UpperCAmelCase_     (  )       ->  List[Any]:
    __lowercase						:	str					=				"http://images.cocodataset.org/val2017/000000039769.jpg"
    __lowercase						:	int					=				Image.open(requests.get(__lowerCAmelCase		,    stream=__lowerCAmelCase		).raw		)
    return im


@torch.no_grad()
def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = False		)       ->  List[str]:
    __lowercase						:	List[str]					=				get_yolos_config(__lowerCAmelCase		)

    # load original state_dict
    __lowercase						:	Optional[Any]					=				torch.load(__lowerCAmelCase		,    map_location='''cpu'''		)["model"]

    # load 🤗 model
    __lowercase						:	Optional[Any]					=				YolosForObjectDetection(__lowerCAmelCase		)
    model.eval()
    __lowercase						:	Any					=				convert_state_dict(__lowerCAmelCase		,    __lowerCAmelCase		)
    model.load_state_dict(__lowerCAmelCase		)

    # Check outputs on an image, prepared by YolosImageProcessor
    __lowercase						:	Tuple					=				800 if yolos_name != "yolos_ti" else 512
    __lowercase						:	Union[str, Any]					=				YolosImageProcessor(format='''coco_detection'''		,    size=__lowerCAmelCase		)
    __lowercase						:	Optional[Any]					=				image_processor(images=prepare_img()		,    return_tensors='''pt'''		)
    __lowercase						:	List[Any]					=				model(**__lowerCAmelCase		)
    __lowercase						:	Optional[Any]					=				outputs.logits, outputs.pred_boxes

    __lowercase						:	List[Any]					=				None, None
    if yolos_name == "yolos_ti":
        __lowercase						:	Any					=				torch.tensor(
            [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]]		)
        __lowercase						:	Union[str, Any]					=				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						:	str					=				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]]		)
        __lowercase						:	Tuple					=				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						:	Dict					=				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]]		)
        __lowercase						:	Optional[int]					=				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						:	Union[str, Any]					=				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]]		)
        __lowercase						:	Optional[Any]					=				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						:	List[Any]					=				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]]		)
        __lowercase						:	Tuple					=				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]		,    __lowerCAmelCase		,    atol=1E-4		)
    assert torch.allclose(pred_boxes[0, :3, :3]		,    __lowerCAmelCase		,    atol=1E-4		)

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

    if push_to_hub:
        __lowercase						:	Dict					=				{
            "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						:	int					=				model_mapping[yolos_name]
        image_processor.push_to_hub(__lowerCAmelCase		,    organization='''hustvl'''		)
        model.push_to_hub(__lowerCAmelCase		,    organization='''hustvl'''		)


if __name__ == "__main__":
 __lowerCAmelCase    :	Tuple						=						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."
 )

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









def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    raise ValueError("Input series is not valid, valid series - [2, 4, 6]"						)
  if len(SCREAMING_SNAKE_CASE						) == 0:
    raise ValueError("Input list must be a non empty list"						)
  if len(SCREAMING_SNAKE_CASE						) == 1:
    return True
  a__	:					Union[str, Any]										=series[1] - series[0]
  for index in range(len(SCREAMING_SNAKE_CASE						) - 1						):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True
def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    raise ValueError("Input series is not valid, valid series - [2, 4, 6]"						)
  if len(SCREAMING_SNAKE_CASE						) == 0:
    raise ValueError("Input list must be a non empty list"						)
  a__	:					Any										=0
  for val in series:
    answer += val
  return answer / len(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






