



"""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()
UpperCAmelCase						:		List[str]			= logging.get_logger(__name__)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						YolosConfig:







  '''simple docstring'''


  lowercase_								= YolosConfig()

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

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

  return config


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase = False							) ->						int:







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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						str:







  '''simple docstring'''


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

  return name


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						dict:







  '''simple docstring'''


  for key in orig_state_dict.copy().keys():
    lowercase_								= orig_state_dict.pop(__lowerCAmelCase							)

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

  return orig_state_dict


def 			_SCREAMING_SNAKE_CASE						() ->						torch.Tensor:







  '''simple docstring'''


  lowercase_								= """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase_								= Image.open(requests.get(__lowerCAmelCase ,  stream=__lowerCAmelCase							).raw							)
  return im


@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase = False							) ->						Optional[Any]:







  '''simple docstring'''


  lowercase_								= get_yolos_config(__lowerCAmelCase							)

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

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

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

  lowercase_					,    lowercase_								= None, None
  if yolos_name == "yolos_ti":
    lowercase_								= torch.tensor(
        [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]]							)
    lowercase_								= torch.tensor(
        [[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]]							)
  elif yolos_name == "yolos_s_200_pre":
    lowercase_								= torch.tensor(
        [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]							)
    lowercase_								= torch.tensor(
        [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]							)
  elif yolos_name == "yolos_s_300_pre":
    lowercase_								= torch.tensor(
        [[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]]							)
    lowercase_								= torch.tensor(
        [[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]]							)
  elif yolos_name == "yolos_s_dWr":
    lowercase_								= torch.tensor(
        [[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]]							)
    lowercase_								= torch.tensor(
        [[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]]							)
  elif yolos_name == "yolos_base":
    lowercase_								= torch.tensor(
        [[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]]							)
    lowercase_								= torch.tensor(
        [[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]]							)
  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:
    lowercase_								= {
        """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_								= model_mapping[yolos_name]
    image_processor.push_to_hub(__lowerCAmelCase ,  organization="""hustvl"""							)
    model.push_to_hub(__lowerCAmelCase ,  organization="""hustvl"""							)


if __name__ == "__main__":
       UpperCAmelCase						:		Union[str, 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."
       )

       UpperCAmelCase						:		Optional[int]			= parser.parse_args()
       convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase						:		Optional[Any]			= {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "WavLMForAudioFrameClassification",
           "WavLMForCTC",
           "WavLMForSequenceClassification",
           "WavLMForXVector",
           "WavLMModel",
           "WavLMPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavlm import (
                  WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavLMForAudioFrameClassification,
                  WavLMForCTC,
                  WavLMForSequenceClassification,
                  WavLMForXVector,
                  WavLMModel,
                  WavLMPreTrainedModel,
              )

else:
       import sys

       UpperCAmelCase						:		Optional[Any]			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

