





"""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_			=      logging.get_logger(__name__)







def 		__lowercase					(       snake_case_  :  str						)       ->YolosConfig:



    '''simple docstring'''







    __A   :						List[Any]									=					YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        __A   :						str									=					192
        __A   :						Any									=					768
        __A   :						Dict									=					12
        __A   :						Tuple									=					3
        __A   :						Union[str, Any]									=					[800, 1333]
        __A   :						Tuple									=					False
    elif yolos_name == "yolos_s_dWr":
        __A   :						Dict									=					330
        __A   :						Dict									=					14
        __A   :						List[str]									=					6
        __A   :						List[str]									=					1320
    elif "yolos_s" in yolos_name:
        __A   :						str									=					384
        __A   :						Tuple									=					1536
        __A   :						Dict									=					12
        __A   :						Tuple									=					6
    elif "yolos_b" in yolos_name:
        __A   :						Union[str, Any]									=					[800, 1344]

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

    return config







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







def 		__lowercase					(       snake_case_  :  str						)       ->str:



    '''simple docstring'''







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

    return name







def 		__lowercase					(       snake_case_  :  dict    ,snake_case_  :  YolosForObjectDetection						)       ->dict:



    '''simple docstring'''







    for key in orig_state_dict.copy().keys():
        __A   :						List[Any]									=					orig_state_dict.pop(snake_case_						)

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

    return orig_state_dict







def 		__lowercase					(       )       ->torch.Tensor:



    '''simple docstring'''







    __A   :						Union[str, Any]									=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __A   :						Dict									=					Image.open(requests.get(snake_case_    ,stream=snake_case_						).raw						)
    return im







@torch.no_grad()
def 		__lowercase					(       snake_case_  :  str    ,snake_case_  :  str    ,snake_case_  :  str    ,snake_case_  :  bool = False						)       ->Optional[Any]:



    '''simple docstring'''







    __A   :						Optional[Any]									=					get_yolos_config(snake_case_						)

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

    # load 🤗 model
    __A   :						List[str]									=					YolosForObjectDetection(snake_case_						)
    model.eval()
    __A   :						List[Any]									=					convert_state_dict(snake_case_    ,snake_case_						)
    model.load_state_dict(snake_case_						)

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

    __A			,	__A   :						Optional[Any]									=					None, None
    if yolos_name == "yolos_ti":
        __A   :						str									=					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]]						)
        __A   :						int									=					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":
        __A   :						List[Any]									=					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]]						)
        __A   :						Any									=					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":
        __A   :						Union[str, 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]]						)
        __A   :						Optional[int]									=					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":
        __A   :						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]]						)
        __A   :						Optional[int]									=					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":
        __A   :						int									=					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]]						)
        __A   :						Union[str, Any]									=					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]    ,snake_case_    ,atol=1e-4						)
    assert torch.allclose(pred_boxes[0, :3, :3]    ,snake_case_    ,atol=1e-4						)

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

    if push_to_hub:
        __A   :						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...'''						)
        __A   :						List[Any]									=					model_mapping[yolos_name]
        image_processor.push_to_hub(snake_case_    ,organization='''hustvl'''						)
        model.push_to_hub(snake_case_    ,organization='''hustvl'''						)


if __name__ == "__main__":
       a_			=      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_			=      parser.parse_args()
       convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











"""simple docstring"""



from math import factorial







def 		__lowercase					(       snake_case_  :  int    ,snake_case_  :  int						)       ->int:



    '''simple docstring'''







    if n < k or k < 0:
        raise ValueError('''Please enter positive integers for n and k where n >= k'''						)
    return factorial(snake_case_						) // (factorial(snake_case_						) * factorial(n - k						))


if __name__ == "__main__":
       print(
           """The number of five-card hands possible from a standard""",
           f'''fifty-two card deck is: {combinations(52, 5)}\n''',
       )

       print(
           """If a class of 40 students must be arranged into groups of""",
           f'''4 for group projects, there are {combinations(40, 4)} ways''',
           """to arrange them.\n""",
       )

       print(
           """If 10 teams are competing in a Formula One race, there""",
           f'''are {combinations(10, 3)} ways that first, second and''',
           """third place can be awarded.""",
       )




