




"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_						=				{
    '''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', '''M2M100OnnxConfig'''],
    '''tokenization_m2m_100''': ['''M2M100Tokenizer'''],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_						=				[
         '''M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''M2M100ForConditionalGeneration''',
         '''M2M100Model''',
         '''M2M100PreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
     from .tokenization_mam_aaa import MaMaaaTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mam_aaa import (
              M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaMaaaForConditionalGeneration,
              MaMaaaModel,
              MaMaaaPreTrainedModel,
          )


else:
     import sys

     A_						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








"""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_						=				logging.get_logger(__name__)


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  YolosConfig()

  # size of the architecture
  if "yolos_ti" in yolos_name:
    _snake_case							:		Tuple								=  1_92
    _snake_case							:		Any								=  7_68
    _snake_case							:		Any								=  12
    _snake_case							:		List[Any]								=  3
    _snake_case							:		int								=  [8_00, 13_33]
    _snake_case							:		Tuple								=  False
  elif yolos_name == "yolos_s_dWr":
    _snake_case							:		Tuple								=  3_30
    _snake_case							:		List[str]								=  14
    _snake_case							:		List[str]								=  6
    _snake_case							:		Union[str, Any]								=  13_20
  elif "yolos_s" in yolos_name:
    _snake_case							:		Union[str, Any]								=  3_84
    _snake_case							:		List[str]								=  15_36
    _snake_case							:		Any								=  12
    _snake_case							:		Optional[int]								=  6
  elif "yolos_b" in yolos_name:
    _snake_case							:		Dict								=  [8_00, 13_44]

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

  return config


def 					UpperCAmelCase__       (snake_case__							:					dict		,   snake_case__							:					YolosConfig		,   snake_case__							:					bool = False    ):
  """simple docstring"""

  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							:		int								=  state_dict.pop(F"blocks.{i}.attn.qkv.weight"    )
    _snake_case							:		Union[str, 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							:		Any								=  in_proj_weight[: config.hidden_size, :]
    _snake_case							:		Optional[Any]								=  in_proj_bias[: config.hidden_size]
    _snake_case							:		Optional[int]								=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case							:		int								=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case							:		Tuple								=  in_proj_weight[-config.hidden_size :, :]
    _snake_case							:		List[Any]								=  in_proj_bias[-config.hidden_size :]


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

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

  return name


def 					UpperCAmelCase__       (snake_case__							:					dict		,   snake_case__							:					YolosForObjectDetection    ):
  """simple docstring"""

  for key in orig_state_dict.copy().keys():
    _snake_case							:		List[str]								=  orig_state_dict.pop(snake_case__    )

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

  return orig_state_dict


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		str								=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _snake_case							:		Union[str, Any]								=  Image.open(requests.get(snake_case__		,   stream=snake_case__    ).raw    )
  return im


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					str		,   snake_case__							:					str		,   snake_case__							:					bool = False    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  get_yolos_config(snake_case__    )

  # load original state_dict
  _snake_case							:		Optional[int]								=  torch.load(snake_case__		,   map_location="""cpu"""    )["""model"""]

  # load 🤗 model
  _snake_case							:		Optional[Any]								=  YolosForObjectDetection(snake_case__    )
  model.eval()
  _snake_case							:		Optional[Any]								=  convert_state_dict(snake_case__		,   snake_case__    )
  model.load_state_dict(snake_case__    )

  # Check outputs on an image, prepared by YolosImageProcessor
  _snake_case							:		List[str]								=  8_00 if yolos_name != """yolos_ti""" else 5_12
  _snake_case							:		Optional[int]								=  YolosImageProcessor(format="""coco_detection"""		,   size=snake_case__    )
  _snake_case							:		Optional[Any]								=  image_processor(images=prepare_img()		,   return_tensors="""pt"""    )
  _snake_case							:		Optional[Any]								=  model(**snake_case__    )
  _snake_case		,					_snake_case							:		Optional[int]								=  outputs.logits, outputs.pred_boxes

  _snake_case		,					_snake_case							:		Dict								=  None, None
  if yolos_name == "yolos_ti":
    _snake_case							:		Optional[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]]    )
    _snake_case							:		Tuple								=  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":
    _snake_case							:		List[str]								=  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]]    )
    _snake_case							:		List[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":
    _snake_case							:		Dict								=  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]]    )
    _snake_case							:		Union[str, Any]								=  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":
    _snake_case							:		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]]    )
    _snake_case							:		Optional[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":
    _snake_case							:		int								=  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]]    )
    _snake_case							:		Optional[int]								=  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:
    _snake_case							:		Dict								=  {
        """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							:		str								=  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_						=				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_						=				parser.parse_args()
     convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


