




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()
_snake_case										=       logging.get_logger(__name__)







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Union[str, Any]			=						YolosConfig()

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

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

      return config







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







def 				lowerCAmelCase_	(     snake_case_     ):
      if "backbone" in name:
            _A							:     str			=						name.replace("""backbone""","""vit"""     )
      if "cls_token" in name:
            _A							:     Any			=						name.replace("""cls_token""","""embeddings.cls_token"""     )
      if "det_token" in name:
            _A							:     int			=						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							:     Optional[int]			=						name.replace("""pos_embed""","""embeddings.position_embeddings"""     )
      if "patch_embed.proj" in name:
            _A							:     str			=						name.replace("""patch_embed.proj""","""embeddings.patch_embeddings.projection"""     )
      if "blocks" in name:
            _A							:     Union[str, Any]			=						name.replace("""blocks""","""encoder.layer"""     )
      if "attn.proj" in name:
            _A							:     Tuple			=						name.replace("""attn.proj""","""attention.output.dense"""     )
      if "attn" in name:
            _A							:     str			=						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							:     Optional[int]			=						name.replace("""mlp.fc1""","""intermediate.dense"""     )
      if "mlp.fc2" in name:
            _A							:     Dict			=						name.replace("""mlp.fc2""","""output.dense"""     )
      if "class_embed" in name:
            _A							:     Union[str, Any]			=						name.replace("""class_embed""","""class_labels_classifier"""     )
      if "bbox_embed" in name:
            _A							:     Any			=						name.replace("""bbox_embed""","""bbox_predictor"""     )
      if "vit.norm" in name:
            _A							:     Union[str, Any]			=						name.replace("""vit.norm""","""vit.layernorm"""     )

      return name







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      for key in orig_state_dict.copy().keys():
            _A							:     Tuple			=						orig_state_dict.pop(snake_case_     )

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

      return orig_state_dict







def 				lowerCAmelCase_	(     ):
      _A							:     Any			=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _A							:     Optional[Any]			=						Image.open(requests.get(snake_case_,stream=snake_case_     ).raw     )
      return im







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_ = False     ):
      _A							:     Optional[int]			=						get_yolos_config(snake_case_     )

      # load original state_dict
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )["""model"""]

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

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

      _A			,   _A							:     int			=						None, None
      if yolos_name == "yolos_ti":
            _A							:     Any			=						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]]     )
            _A							:     Optional[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":
            _A							:     Union[str, 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]]     )
            _A							:     str			=						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":
            _A							:     str			=						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]]     )
            _A							:     Optional[int]			=						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":
            _A							:     str			=						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]]     )
            _A							:     Any			=						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":
            _A							:     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]]     )
            _A							:     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:
            _A							:     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..."""     )
            _A							:     int			=						model_mapping[yolos_name]
            image_processor.push_to_hub(snake_case_,organization="""hustvl"""     )
            model.push_to_hub(snake_case_,organization="""hustvl"""     )


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

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







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case										=       logging.get_logger(__name__)

_snake_case										=       [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 				lowerCAmelCase_	(     snake_case_     ):
      if k == "embeddings.weight":
            return "shared.weight"

      for parlai_name, hf_name in PATTERNS:
            _A							:     str			=						k.replace(snake_case_,snake_case_     )

      if k.startswith("""encoder"""     ):
            _A							:     Optional[Any]			=						k.replace(""".attn""",""".self_attn"""     )
            _A							:     Dict			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Optional[Any]			=						k.replace("""norm2""","""final_layer_norm"""     )
      elif k.startswith("""decoder"""     ):
            _A							:     str			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Any			=						k.replace("""norm2""","""encoder_attn_layer_norm"""     )
            _A							:     Optional[int]			=						k.replace("""norm3""","""final_layer_norm"""     )
      return k







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[Any]			=						[
          """model.encoder.layernorm_embedding.weight""",
          """model.encoder.layernorm_embedding.bias""",
          """model.decoder.layernorm_embedding.weight""",
          """model.decoder.layernorm_embedding.bias""",
      ]
      for k in keys:
            _A							:     str			=						sd.pop(snake_case_     )
            _A							:     Optional[int]			=						k.replace("""layernorm_embedding""","""layer_norm"""     )
            assert new_k not in sd
            _A							:     Optional[int]			=						v


_snake_case										=       ["START"]







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )
      _A							:     List[Any]			=						model["""model"""]
      _A							:     Optional[Any]			=						BlenderbotConfig.from_json_file(snake_case_     )
      _A							:     List[str]			=						BlenderbotForConditionalGeneration(snake_case_     )
      _A							:     Tuple			=						m.model.state_dict().keys()
      _A							:     Any			=						[]
      _A							:     Dict			=						{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

            _A							:     Optional[int]			=						rename_state_dict_key(snake_case_     )
            if new_k not in valid_keys:
                  failures.append([k, new_k]     )
            else:
                  _A							:     Dict			=						v
      if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
            rename_layernorm_keys(snake_case_     )
      m.model.load_state_dict(snake_case_,strict=snake_case_     )
      m.half()
      m.save_pretrained(snake_case_     )


if __name__ == "__main__":
  _snake_case										=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
  parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
  parser.add_argument(
      "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
  )
  _snake_case										=       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

