





'''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()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str		)		->  YolosConfig:
   snake_case 						=   YolosConfig()

   # size of the architecture
   if "yolos_ti" in yolos_name:
      snake_case 						=   1_92
      snake_case 						=   7_68
      snake_case 						=   12
      snake_case 						=   3
      snake_case 						=   [8_00, 13_33]
      snake_case 						=   False
   elif yolos_name == "yolos_s_dWr":
      snake_case 						=   3_30
      snake_case 						=   14
      snake_case 						=   6
      snake_case 						=   13_20
   elif "yolos_s" in yolos_name:
      snake_case 						=   3_84
      snake_case 						=   15_36
      snake_case 						=   12
      snake_case 						=   6
   elif "yolos_b" in yolos_name:
      snake_case 						=   [8_00, 13_44]

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

   return config





def 	__lowerCamelCase			(      __lowerCAmelCase       :							dict      ,    __lowerCAmelCase       :							YolosConfig      ,    __lowerCAmelCase       :							bool = False		)		->  Optional[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)
      snake_case 						=   state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''		)
      snake_case 						=   state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''		)
      # next, add query, keys and values (in that order) to the state dict
      snake_case 						=   in_proj_weight[: config.hidden_size, :]
      snake_case 						=   in_proj_bias[: config.hidden_size]
      snake_case 						=   in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      snake_case 						=   in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      snake_case 						=   in_proj_weight[-config.hidden_size :, :]
      snake_case 						=   in_proj_bias[-config.hidden_size :]





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

   return name





def 	__lowerCamelCase			(      __lowerCAmelCase       :							dict      ,    __lowerCAmelCase       :							YolosForObjectDetection		)		->  dict:
   for key in orig_state_dict.copy().keys():
      snake_case 						=   orig_state_dict.pop(__lowerCAmelCase		)

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

   return orig_state_dict





def 	__lowerCamelCase			(      )		->  torch.Tensor:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)
   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							bool = False		)		->  str:
   snake_case 						=   get_yolos_config(__lowerCAmelCase		)

   # load original state_dict
   snake_case 						=   torch.load(__lowerCAmelCase      ,    map_location="""cpu"""		)["""model"""]

   # load 🤗 model
   snake_case 						=   YolosForObjectDetection(__lowerCAmelCase		)
   model.eval()
   snake_case 						=   convert_state_dict(__lowerCAmelCase      ,    __lowerCAmelCase		)
   model.load_state_dict(__lowerCAmelCase		)

   # Check outputs on an image, prepared by YolosImageProcessor
   snake_case 						=   8_00 if yolos_name != """yolos_ti""" else 5_12
   snake_case 						=   YolosImageProcessor(format="""coco_detection"""      ,    size=__lowerCAmelCase		)
   snake_case 						=   image_processor(images=prepare_img()      ,    return_tensors="""pt"""		)
   snake_case 						=   model(**__lowerCAmelCase		)
   snake_case						,    snake_case 						=   outputs.logits, outputs.pred_boxes

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


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			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."
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''




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


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "trajectory_transformer"
     snake_case_						      =   ["past_key_values"]
     snake_case_						      =   {
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }



     def __init__(   self							:			Optional[Any]   ,       __snake_case							:			int=1_00   ,       __snake_case							:			List[str]=5   ,       __snake_case							:			str=1   ,       __snake_case							:			Union[str, Any]=1   ,       __snake_case							:			Union[str, Any]=2_49   ,       __snake_case							:			List[Any]=6   ,       __snake_case							:			Optional[int]=17   ,       __snake_case							:			Optional[Any]=25   ,       __snake_case							:			Union[str, Any]=4   ,       __snake_case							:			List[Any]=4   ,       __snake_case							:			Optional[int]=1_28   ,       __snake_case							:			Optional[Any]=0.1   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			int=0.1   ,       __snake_case							:			Union[str, Any]=0.00_06   ,       __snake_case							:			Tuple=5_12   ,       __snake_case							:			int=0.02   ,       __snake_case							:			Any=1e-12   ,       __snake_case							:			Optional[Any]=1   ,       __snake_case							:			List[Any]=True   ,       __snake_case							:			Dict=1   ,       __snake_case							:			Dict=5_02_56   ,       __snake_case							:			Union[str, Any]=5_02_56   ,       **__snake_case							:			Tuple   ,       )->	Optional[int]:
        snake_case 						=   vocab_size
        snake_case 						=   action_weight
        snake_case 						=   reward_weight
        snake_case 						=   value_weight
        snake_case 						=   max_position_embeddings
        snake_case 						=   block_size
        snake_case 						=   action_dim
        snake_case 						=   observation_dim
        snake_case 						=   transition_dim
        snake_case 						=   learning_rate
        snake_case 						=   n_layer
        snake_case 						=   n_head
        snake_case 						=   n_embd
        snake_case 						=   embd_pdrop
        snake_case 						=   attn_pdrop
        snake_case 						=   resid_pdrop
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   kaiming_initializer_range
        snake_case 						=   use_cache
        super().__init__(pad_token_id=__snake_case   ,       bos_token_id=__snake_case   ,       eos_token_id=__snake_case   ,       **__snake_case						)

