




'''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()
lowercase      :   Any      =       logging.get_logger(__name__)



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      str        =			YolosConfig()

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

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

     return config



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



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

     return name



def        lowerCamelCase__				(      __lowercase     ,   __lowercase				):
     for key in orig_state_dict.copy().keys():
          snake_case			:      str        =			orig_state_dict.pop(__lowercase				)

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

     return orig_state_dict



def        lowerCamelCase__				(      ):
     snake_case			:      List[Any]        =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     snake_case			:      Tuple        =			Image.open(requests.get(__lowercase     ,   stream=__lowercase				).raw				)
     return im



@torch.no_grad()
def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase = False				):
     snake_case			:      int        =			get_yolos_config(__lowercase				)

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

     # load 🤗 model
     snake_case			:      Optional[int]        =			YolosForObjectDetection(__lowercase				)
     model.eval()
     snake_case			:      str        =			convert_state_dict(__lowercase     ,   __lowercase				)
     model.load_state_dict(__lowercase				)

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

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


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

      lowercase      :   Tuple      =       parser.parse_args()
      convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''







# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")







class 					_a :





     '''simple docstring'''



     def __init__(				self							,__a							,__a							,__a = True							,__a = False							)      ->				Dict:
          snake_case			:      List[Any]        =			scheduler
          snake_case			:      List[Any]        =			optimizers if isinstance(__a							,(list, tuple)							) else [optimizers]
          snake_case			:      List[str]        =			split_batches
          snake_case			:      List[str]        =			step_with_optimizer
          snake_case			:      int        =			GradientState()







     def 			snake_case_      (				self							,*__a							,**__a							)      ->				Optional[Any]:
          if not self.step_with_optimizer:
               # No link between scheduler and optimizer -> just step
               self.scheduler.step(*__a							,**__a							)
               return

          # Otherwise, first make sure the optimizer was stepped.
          if not self.gradient_state.sync_gradients:
               if self.gradient_state.adjust_scheduler:
                    self.scheduler._step_count += 1
               return

          for opt in self.optimizers:
               if opt.step_was_skipped:
                    return
          if self.split_batches:
               # Split batches -> the training dataloader batch size is not changed so one step per training step
               self.scheduler.step(*__a							,**__a							)
          else:
               # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
               # num_processes steps per training step
               snake_case			:      List[str]        =			AcceleratorState().num_processes
               for _ in range(__a							):
                    # Special case when using OneCycle and `drop_last` was not used
                    if hasattr(self.scheduler							,"""total_steps"""							):
                         if self.scheduler._step_count <= self.scheduler.total_steps:
                              self.scheduler.step(*__a							,**__a							)
                    else:
                         self.scheduler.step(*__a							,**__a							)







     def 			snake_case_      (				self							)      ->				Dict:
          return self.scheduler.get_last_lr()







     def 			snake_case_      (				self							)      ->				int:
          return self.scheduler.state_dict()







     def 			snake_case_      (				self							,__a							)      ->				Any:
          self.scheduler.load_state_dict(__a							)







     def 			snake_case_      (				self							)      ->				Any:
          return self.scheduler.get_lr()







     def 			snake_case_      (				self							,*__a							,**__a							)      ->				Optional[Any]:
          return self.scheduler.print_lr(*__a							,**__a							)



