






from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowerCamelCase			:			Any     =							{
    """microsoft/trocr-base-handwritten""": (
        """https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"""
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}

class 		lowerCAmelCase  (			a_      ):

					'''simple docstring'''

					_A    :      int					       =   '''trocr'''
					_A    :      Optional[Any]					       =   ['''past_key_values''']
					_A    :      str					       =   {
					    '''num_attention_heads''': '''decoder_attention_heads''',
					    '''hidden_size''': '''d_model''',
					    '''num_hidden_layers''': '''decoder_layers''',
					}



					def __init__(	self					:						List[Any]		,  __a					:						Tuple=50265		,  __a					:						int=1024		,  __a					:						str=12		,  __a					:						Dict=16		,  __a					:						Dict=4096		,  __a					:						List[Any]="gelu"		,  __a					:						List[Any]=512		,  __a					:						Tuple=0.1		,  __a					:						Any=0.0		,  __a					:						List[Any]=0.0		,  __a					:						int=2		,  __a					:						str=0.02		,  __a					:						Optional[int]=0.0		,  __a					:						List[Any]=True		,  __a					:						int=False		,  __a					:						Tuple=True		,  __a					:						int=True		,  __a					:						Union[str, Any]=1		,  __a					:						List[str]=0		,  __a					:						Optional[Any]=2		,  **__a					:						Optional[Any]		,  )      ->	Optional[int]:


												"""simple docstring"""

												__lowercase   :				int						      =  vocab_size
												__lowercase   :				Union[str, Any]						      =  d_model
												__lowercase   :				str						      =  decoder_layers
												__lowercase   :				int						      =  decoder_attention_heads
												__lowercase   :				str						      =  decoder_ffn_dim
												__lowercase   :				Any						      =  activation_function
												__lowercase   :				Tuple						      =  max_position_embeddings
												__lowercase   :				Optional[int]						      =  dropout
												__lowercase   :				str						      =  attention_dropout
												__lowercase   :				Optional[int]						      =  activation_dropout
												__lowercase   :				Optional[int]						      =  init_std
												__lowercase   :				Union[str, Any]						      =  decoder_layerdrop
												__lowercase   :				Tuple						      =  use_cache
												__lowercase   :				Any						      =  scale_embedding
												__lowercase   :				List[str]						      =  use_learned_position_embeddings
												__lowercase   :				Union[str, Any]						      =  layernorm_embedding

												super().__init__(
												    pad_token_id=_a		,  bos_token_id=_a		,  eos_token_id=_a		,  decoder_start_token_id=_a		,  **_a		,  )

"""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     :			Optional[int]  						=			logging.get_logger(__name__)


def     __magic_name__      ( __snake_case							:			str    )					->							YolosConfig:
       lowercase       :							int									=				YolosConfig()

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

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

       return config


def     __magic_name__      ( __snake_case							:			dict	,					__snake_case							:			YolosConfig	,					__snake_case							:			bool = False    )					->							int:
       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       :							Dict									=				state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""    )
              lowercase       :							int									=				state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""    )
              # next, add query, keys and values (in that order) to the state dict
              lowercase       :							Union[str, Any]									=				in_proj_weight[: config.hidden_size, :]
              lowercase       :							int									=				in_proj_bias[: config.hidden_size]
              lowercase       :							str									=				in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowercase       :							List[Any]									=				in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowercase       :							Dict									=				in_proj_weight[-config.hidden_size :, :]
              lowercase       :							List[Any]									=				in_proj_bias[-config.hidden_size :]


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

       return name


def     __magic_name__      ( __snake_case							:			dict	,					__snake_case							:			YolosForObjectDetection    )					->							dict:
       for key in orig_state_dict.copy().keys():
              lowercase       :							List[Any]									=				orig_state_dict.pop(__snake_case    )

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

       return orig_state_dict


def     __magic_name__      ( )					->							torch.Tensor:
       lowercase       :							Optional[int]									=				"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowercase       :							Tuple									=				Image.open(requests.get(__snake_case	,					stream=__snake_case    ).raw    )
       return im


@torch.no_grad()
def     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			str	,					__snake_case							:			str	,					__snake_case							:			bool = False    )					->							Dict:
       lowercase       :							Optional[int]									=				get_yolos_config(__snake_case    )

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

       # load 🤗 model
       lowercase       :							str									=				YolosForObjectDetection(__snake_case    )
       model.eval()
       lowercase       :							Dict									=				convert_state_dict(__snake_case	,					__snake_case    )
       model.load_state_dict(__snake_case    )

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

       lowercase						,       lowercase       :							int									=				None, None
       if yolos_name == "yolos_ti":
              lowercase       :							int									=				torch.tensor(
                  [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]    )
              lowercase       :							List[Any]									=				torch.tensor(
                  [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]    )
       elif yolos_name == "yolos_s_200_pre":
              lowercase       :							List[Any]									=				torch.tensor(
                  [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]    )
              lowercase       :							List[Any]									=				torch.tensor(
                  [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]    )
       elif yolos_name == "yolos_s_300_pre":
              lowercase       :							Optional[int]									=				torch.tensor(
                  [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]    )
              lowercase       :							Tuple									=				torch.tensor(
                  [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]    )
       elif yolos_name == "yolos_s_dWr":
              lowercase       :							Tuple									=				torch.tensor(
                  [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]    )
              lowercase       :							List[str]									=				torch.tensor(
                  [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]    )
       elif yolos_name == "yolos_base":
              lowercase       :							Optional[Any]									=				torch.tensor(
                  [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]    )
              lowercase       :							List[str]									=				torch.tensor(
                  [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]    )
       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:
              lowercase       :							Optional[int]									=				{
                  "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       :							Optional[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     :			List[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     :			Any  						=			parser.parse_args()
   convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


