






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     : Optional[Any]   	=      logging.get_logger(__name__)


def 					snake_case__				(				UpperCAmelCase				:	str							):
    lowerCAmelCase__ :Union[str, Any]   				=					YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        lowerCAmelCase__ :Tuple   				=					1_9_2
        lowerCAmelCase__ :Tuple   				=					7_6_8
        lowerCAmelCase__ :List[Any]   				=					1_2
        lowerCAmelCase__ :Optional[Any]   				=					3
        lowerCAmelCase__ :Union[str, Any]   				=					[8_0_0, 1_3_3_3]
        lowerCAmelCase__ :List[str]   				=					False
    elif yolos_name == "yolos_s_dWr":
        lowerCAmelCase__ :Dict   				=					3_3_0
        lowerCAmelCase__ :Optional[Any]   				=					1_4
        lowerCAmelCase__ :Any   				=					6
        lowerCAmelCase__ :Any   				=					1_3_2_0
    elif "yolos_s" in yolos_name:
        lowerCAmelCase__ :Tuple   				=					3_8_4
        lowerCAmelCase__ :Union[str, Any]   				=					1_5_3_6
        lowerCAmelCase__ :Any   				=					1_2
        lowerCAmelCase__ :int   				=					6
    elif "yolos_b" in yolos_name:
        lowerCAmelCase__ :List[str]   				=					[8_0_0, 1_3_4_4]

    lowerCAmelCase__ :Tuple   				=					9_1
    lowerCAmelCase__ :Tuple   				=					"huggingface/label-files"
    lowerCAmelCase__ :Union[str, Any]   				=					"coco-detection-id2label.json"
    lowerCAmelCase__ :Dict   				=					json.load(open(hf_hub_download(lowercase__     ,      lowercase__     ,      repo_type="dataset"							)     ,      "r"							)							)
    lowerCAmelCase__ :str   				=					{int(lowercase__							): v for k, v in idalabel.items()}
    lowerCAmelCase__ :int   				=					idalabel
    lowerCAmelCase__ :List[Any]   				=					{v: k for k, v in idalabel.items()}

    return config


def 					snake_case__				(				UpperCAmelCase				:	dict     ,      UpperCAmelCase				:	YolosConfig     ,      UpperCAmelCase				:	bool = False							):





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


def 					snake_case__				(				UpperCAmelCase				:	str							):





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

    return name


def 					snake_case__				(				UpperCAmelCase				:	dict     ,      UpperCAmelCase				:	YolosForObjectDetection							):





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

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

    return orig_state_dict


def 					snake_case__				(				):
    lowerCAmelCase__ :Tuple   				=					"http://images.cocodataset.org/val2017/000000039769.jpg"
    lowerCAmelCase__ :str   				=					Image.open(requests.get(lowercase__     ,      stream=lowercase__							).raw							)
    return im


@torch.no_grad()
def 					snake_case__				(				UpperCAmelCase				:	str     ,      UpperCAmelCase				:	str     ,      UpperCAmelCase				:	str     ,      UpperCAmelCase				:	bool = False							):
    lowerCAmelCase__ :Optional[Any]   				=					get_yolos_config(lowercase__							)

    # load original state_dict
    lowerCAmelCase__ :str   				=					torch.load(lowercase__     ,      map_location="cpu"							)["model"]

    # load 🤗 model
    lowerCAmelCase__ :Optional[int]   				=					YolosForObjectDetection(lowercase__							)
    model.eval()
    lowerCAmelCase__ :Tuple   				=					convert_state_dict(lowercase__     ,      lowercase__							)
    model.load_state_dict(lowercase__							)

    # Check outputs on an image, prepared by YolosImageProcessor
    lowerCAmelCase__ :str   				=					8_0_0 if yolos_name != "yolos_ti" else 5_1_2
    lowerCAmelCase__ :int   				=					YolosImageProcessor(format="coco_detection"     ,      size=lowercase__							)
    lowerCAmelCase__ :Optional[Any]   				=					image_processor(images=prepare_img()     ,      return_tensors="pt"							)
    lowerCAmelCase__ :List[Any]   				=					model(**lowercase__							)
    lowerCAmelCase__				,lowerCAmelCase__ :Optional[Any]   				=					outputs.logits, outputs.pred_boxes

    lowerCAmelCase__				,lowerCAmelCase__ :List[Any]   				=					None, None
    if yolos_name == "yolos_ti":
        lowerCAmelCase__ :Optional[int]   				=					torch.tensor(
            [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]							)
        lowerCAmelCase__ :int   				=					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":
        lowerCAmelCase__ :Tuple   				=					torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]							)
        lowerCAmelCase__ :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":
        lowerCAmelCase__ :Union[str, Any]   				=					torch.tensor(
            [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]							)
        lowerCAmelCase__ :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":
        lowerCAmelCase__ :Dict   				=					torch.tensor(
            [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]							)
        lowerCAmelCase__ :str   				=					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":
        lowerCAmelCase__ :Tuple   				=					torch.tensor(
            [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]							)
        lowerCAmelCase__ :Dict   				=					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:
        lowerCAmelCase__ :List[str]   				=					{
            "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..."							)
        lowerCAmelCase__ :Optional[Any]   				=					model_mapping[yolos_name]
        image_processor.push_to_hub(lowercase__     ,      organization="hustvl"							)
        model.push_to_hub(lowercase__     ,      organization="hustvl"							)


if __name__ == "__main__":
 _a     : Any   	=      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     : int   	=      parser.parse_args()
 convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''

def 					__UpperCamelCase				(						lowercase__    :	int			):






     '''simple docstring'''





     if upper_limit < 0:
          raise ValueError('Limit for the Catalan sequence must be ≥ 0'			)

     __lowercase      		=[0] * (upper_limit + 1)

     # Base case: C(0) = C(1) = 1
     __lowercase      		=1
     if upper_limit > 0:
          __lowercase      		=1

     # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
     for i in range(2,       upper_limit + 1			):
          for j in range(lowercase__			):
               catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

     return catalan_list


if __name__ == "__main__":
       print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
       print('''\n*** Enter -1 at any time to quit ***''')
       print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
       try:
              while True:
                     UpperCAmelCase					 =	int(input().strip())
                     if N < 0:
                            print('''\n********* Goodbye!! ************''')
                            break
                     else:
                            print(F'''The Catalan numbers from 0 through {N} are:''')
                            print(catalan_numbers(N))
                            print('''Try another upper limit for the sequence: ''', end='''''')
       except (NameError, ValueError):
              print('''\n********* Invalid input, goodbye! ************\n''')

       import doctest

       doctest.testmod()


