
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  if "backbone.0.body" in orig_key:
    _lowerCamelCase      =						orig_key.replace('''backbone.0.body'''			,   '''backbone.conv_encoder.model'''  )
  if "transformer" in orig_key:
    _lowerCamelCase      =						orig_key.replace('''transformer.'''			,   ''''''  )
  if "norm1" in orig_key:
    if "encoder" in orig_key:
      _lowerCamelCase      =						orig_key.replace('''norm1'''			,   '''self_attn_layer_norm'''  )
    else:
      _lowerCamelCase      =						orig_key.replace('''norm1'''			,   '''encoder_attn_layer_norm'''  )
  if "norm2" in orig_key:
    if "encoder" in orig_key:
      _lowerCamelCase      =						orig_key.replace('''norm2'''			,   '''final_layer_norm'''  )
    else:
      _lowerCamelCase      =						orig_key.replace('''norm2'''			,   '''self_attn_layer_norm'''  )
  if "norm3" in orig_key:
    _lowerCamelCase      =						orig_key.replace('''norm3'''			,   '''final_layer_norm'''  )
  if "linear1" in orig_key:
    _lowerCamelCase      =						orig_key.replace('''linear1'''			,   '''fc1'''  )
  if "linear2" in orig_key:
    _lowerCamelCase      =						orig_key.replace('''linear2'''			,   '''fc2'''  )
  if "query_embed" in orig_key:
    _lowerCamelCase      =						orig_key.replace('''query_embed'''			,   '''query_position_embeddings'''  )
  if "cross_attn" in orig_key:
    _lowerCamelCase      =						orig_key.replace('''cross_attn'''			,   '''encoder_attn'''  )

  return orig_key


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  for i in range(6  ):
    # read in weights + bias of input projection layer of self-attention
    _lowerCamelCase      =						state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''  )
    _lowerCamelCase      =						state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_bias'''  )
    # next, add query, keys and values (in that order) to the state dict
    _lowerCamelCase      =						in_proj_weight[:2_5_6, :]
    _lowerCamelCase      =						in_proj_bias[:2_5_6]
    _lowerCamelCase      =						in_proj_weight[2_5_6:5_1_2, :]
    _lowerCamelCase      =						in_proj_bias[2_5_6:5_1_2]
    _lowerCamelCase      =						in_proj_weight[-2_5_6:, :]
    _lowerCamelCase      =						in_proj_bias[-2_5_6:]


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _lowerCamelCase      =						Image.open(requests.get(_UpperCamelCase			,   stream=_UpperCamelCase  ).raw  )

  return im


@torch.no_grad()
def 		_lowerCamelCase       (   _a			,   _a			,   _a			,   _a			,   _a			,   _a			,   _a			,   ):
  """simple docstring"""




  _lowerCamelCase      =						DeformableDetrConfig()
  # set config attributes
  if single_scale:
    _lowerCamelCase      =						1
  _lowerCamelCase      =						dilation
  _lowerCamelCase      =						with_box_refine
  _lowerCamelCase      =						two_stage
  # set labels
  _lowerCamelCase      =						9_1
  _lowerCamelCase      =						'''huggingface/label-files'''
  _lowerCamelCase      =						'''coco-detection-id2label.json'''
  _lowerCamelCase      =						json.load(open(cached_download(hf_hub_url(_UpperCamelCase			,   _UpperCamelCase			,   repo_type='''dataset'''  )  )			,   '''r'''  )  )
  _lowerCamelCase      =						{int(_UpperCamelCase  ): v for k, v in idalabel.items()}
  _lowerCamelCase      =						idalabel
  _lowerCamelCase      =						{v: k for k, v in idalabel.items()}

  # load image processor
  _lowerCamelCase      =						DeformableDetrImageProcessor(format='''coco_detection'''  )

  # prepare image
  _lowerCamelCase      =						prepare_img()
  _lowerCamelCase      =						image_processor(images=_UpperCamelCase			,   return_tensors='''pt'''  )
  _lowerCamelCase      =						encoding['''pixel_values''']

  logger.info('''Converting model...'''  )

  # load original state dict
  _lowerCamelCase      =						torch.load(_UpperCamelCase			,   map_location='''cpu'''  )['''model''']
  # rename keys
  for key in state_dict.copy().keys():
    _lowerCamelCase      =						state_dict.pop(_UpperCamelCase  )
    _lowerCamelCase      =						val
  # query, key and value matrices need special treatment
  read_in_q_k_v(_UpperCamelCase  )
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  _lowerCamelCase      =						'''model.'''
  for key in state_dict.copy().keys():
    if not key.startswith('''class_embed'''  ) and not key.startswith('''bbox_embed'''  ):
      _lowerCamelCase      =						state_dict.pop(_UpperCamelCase  )
      _lowerCamelCase      =						val
    # finally, create HuggingFace model and load state dict
  _lowerCamelCase      =						DeformableDetrForObjectDetection(_UpperCamelCase  )
  model.load_state_dict(_UpperCamelCase  )
  model.eval()

  _lowerCamelCase      =						'''cuda''' if torch.cuda.is_available() else '''cpu'''
  model.to(_UpperCamelCase  )
  # verify our conversion
  _lowerCamelCase      =						model(pixel_values.to(_UpperCamelCase  )  )

  _lowerCamelCase      =						torch.tensor(
      [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]  )
  _lowerCamelCase      =						torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]  )

  if single_scale:
    _lowerCamelCase      =						torch.tensor(
        [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]  )
    _lowerCamelCase      =						torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]  )

  if single_scale and dilation:
    _lowerCamelCase      =						torch.tensor(
        [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]  )
    _lowerCamelCase      =						torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]  )

  if with_box_refine:
    _lowerCamelCase      =						torch.tensor(
        [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]  )
    _lowerCamelCase      =						torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]  )

  if with_box_refine and two_stage:
    _lowerCamelCase      =						torch.tensor(
        [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]  )
    _lowerCamelCase      =						torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]  )

  print('''Logits:'''			,   outputs.logits[0, :3, :3]  )

  assert torch.allclose(outputs.logits[0, :3, :3]			,   expected_logits.to(_UpperCamelCase  )			,   atol=1E-4  )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,   expected_boxes.to(_UpperCamelCase  )			,   atol=1E-4  )

  print('''Everything ok!'''  )

  # Save model and image processor
  logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''  )
  Path(_UpperCamelCase  ).mkdir(exist_ok=_UpperCamelCase  )
  model.save_pretrained(_UpperCamelCase  )
  image_processor.save_pretrained(_UpperCamelCase  )

  # Push to hub
  if push_to_hub:
    _lowerCamelCase      =						'''deformable-detr'''
    model_name += "-single-scale" if single_scale else ""
    model_name += "-dc5" if dilation else ""
    model_name += "-with-box-refine" if with_box_refine else ""
    model_name += "-two-stage" if two_stage else ""
    print('''Pushing model to hub...'''  )
    model.push_to_hub(repo_path_or_name=_UpperCamelCase			,   organization='''nielsr'''			,   commit_message='''Add model'''  )


if __name__ == "__main__":
  _UpperCAmelCase    			=					argparse.ArgumentParser()

  parser.add_argument(
      "--checkpoint_path",
      type=str,
      default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
      help="Path to Pytorch checkpoint (.pth file) you\'d like to convert.",
  )
  parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
  parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
  parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
  parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default=None,
      type=str,
      required=True,
      help="Path to the folder to output PyTorch model.",
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )
  _UpperCAmelCase    			=					parser.parse_args()
  convert_deformable_detr_checkpoint(
      args.checkpoint_path,
      args.single_scale,
      args.dilation,
      args.with_box_refine,
      args.two_stage,
      args.pytorch_dump_folder_path,
      args.push_to_hub,
  )







from typing import TYPE_CHECKING

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


__A     						=      {'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''HubertForCTC''',
     '''HubertForSequenceClassification''',
     '''HubertModel''',
     '''HubertPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFHubertForCTC''',
     '''TFHubertModel''',
     '''TFHubertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

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

