

"""simple docstring"""






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase						=		[]
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
 )
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
 rename_keys.append(
     (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
 )
 rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
 )
 rename_keys.append(
     (
         F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
         F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
     )
 )
 rename_keys.append(
     (
         F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
         F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
     )
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

 # q, k, v projections in self/cross-attention in decoder for conditional DETR
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
 )

 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
 )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[str]  =    state_dict.pop(_lowerCamelCase   )
    _lowerCAmelCase       :   Tuple  =    val

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _lowerCAmelCase       :   Dict  =    key.replace('backbone.0.body' ,   'backbone.conv_encoder.model'   )
            _lowerCAmelCase       :   Dict  =    value
        else:
            _lowerCAmelCase       :   Any  =    value

    return new_state_dict

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=False   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[Any]  =    ''
    if is_panoptic:
        _lowerCAmelCase       :   Optional[Any]  =    'conditional_detr.'

    # first: transformer encoder
    for i in range(6   ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _lowerCAmelCase       :   Optional[int]  =    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
        _lowerCAmelCase       :   Tuple  =    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _lowerCAmelCase       :   Dict  =    in_proj_weight[:256, :]
        _lowerCAmelCase       :   List[Any]  =    in_proj_bias[:256]
        _lowerCAmelCase       :   Dict  =    in_proj_weight[256:512, :]
        _lowerCAmelCase       :   Union[str, Any]  =    in_proj_bias[256:512]
        _lowerCAmelCase       :   Any  =    in_proj_weight[-256:, :]
        _lowerCAmelCase       :   Any  =    in_proj_bias[-256:]

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowerCAmelCase       :   Union[str, Any]  =    Image.open(requests.get(_lowerCamelCase ,   stream=_lowerCamelCase   ).raw   )

    return im

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[Any]  =    ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        _lowerCAmelCase       :   Dict  =    'resnet101'
    if "dc5" in model_name:
        _lowerCAmelCase       :   List[Any]  =    True
    _lowerCAmelCase       :   Dict  =    'panoptic' in model_name
    if is_panoptic:
        _lowerCAmelCase       :   List[str]  =    250
    else:
        _lowerCAmelCase       :   str  =    91
        _lowerCAmelCase       :   Any  =    'huggingface/label-files'
        _lowerCAmelCase       :   List[str]  =    'coco-detection-id2label.json'
        _lowerCAmelCase       :   int  =    json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type='dataset'   ) ,   'r'   )   )
        _lowerCAmelCase       :   str  =    {int(_lowerCamelCase   ): v for k, v in idalabel.items()}
        _lowerCAmelCase       :   Union[str, Any]  =    idalabel
        _lowerCAmelCase       :   Any  =    {v: k for k, v in idalabel.items()}

    # load image processor
    _lowerCAmelCase       :   Optional[int]  =    'coco_panoptic' if is_panoptic else 'coco_detection'
    _lowerCAmelCase       :   str  =    ConditionalDetrImageProcessor(format=_lowerCamelCase   )

    # prepare image
    _lowerCAmelCase       :   List[str]  =    prepare_img()
    _lowerCAmelCase       :   int  =    image_processor(images=_lowerCamelCase ,   return_tensors='pt'   )
    _lowerCAmelCase       :   Dict  =    encoding['pixel_values']

    logger.info(f"""Converting model {model_name}..."""   )

    # load original model from torch hub
    _lowerCAmelCase       :   Any  =    torch.hub.load('DeppMeng/ConditionalDETR' ,   _lowerCamelCase ,   pretrained=_lowerCamelCase   ).eval()
    _lowerCAmelCase       :   Union[str, Any]  =    conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            _lowerCAmelCase       :   Optional[Any]  =    'conditional_detr.' + src
        rename_key(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    rename_backbone_keys(_lowerCamelCase   )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_lowerCamelCase ,   is_panoptic=_lowerCamelCase   )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _lowerCAmelCase       :   Dict  =    'conditional_detr.model.' if is_panoptic else 'model.'
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('conditional_detr'   )
                and not key.startswith('class_labels_classifier'   )
                and not key.startswith('bbox_predictor'   )
            ):
                _lowerCAmelCase       :   List[Any]  =    state_dict.pop(_lowerCamelCase   )
                _lowerCAmelCase       :   Tuple  =    val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                _lowerCAmelCase       :   Any  =    state_dict.pop(_lowerCamelCase   )
                _lowerCAmelCase       :   Dict  =    val
            elif key.startswith('bbox_attention'   ) or key.startswith('mask_head'   ):
                continue
            else:
                _lowerCAmelCase       :   str  =    state_dict.pop(_lowerCamelCase   )
                _lowerCAmelCase       :   List[str]  =    val
        else:
            if not key.startswith('class_labels_classifier'   ) and not key.startswith('bbox_predictor'   ):
                _lowerCAmelCase       :   Dict  =    state_dict.pop(_lowerCamelCase   )
                _lowerCAmelCase       :   List[Any]  =    val
    # finally, create HuggingFace model and load state dict
    _lowerCAmelCase       :   str  =    ConditionalDetrForSegmentation(_lowerCamelCase   ) if is_panoptic else ConditionalDetrForObjectDetection(_lowerCamelCase   )
    model.load_state_dict(_lowerCamelCase   )
    model.eval()
    model.push_to_hub(repo_id=_lowerCamelCase ,   organization='DepuMeng' ,   commit_message='Add model'   )
    # verify our conversion
    _lowerCAmelCase       :   List[Any]  =    conditional_detr(_lowerCamelCase   )
    _lowerCAmelCase       :   Dict  =    model(_lowerCamelCase   )
    assert torch.allclose(outputs.logits ,   original_outputs['pred_logits'] ,   atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes ,   original_outputs['pred_boxes'] ,   atol=1e-4   )
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks ,   original_outputs['pred_masks'] ,   atol=1e-4   )

    # Save model and image processor
    logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
    Path(_lowerCamelCase   ).mkdir(exist_ok=_lowerCamelCase   )
    model.save_pretrained(_lowerCamelCase   )
    image_processor.save_pretrained(_lowerCamelCase   )


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

 parser.add_argument(
     """--model_name""",
     default="""conditional_detr_resnet50""",
     type=str,
     help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 _lowerCAmelCase						=		parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""simple docstring"""






import logging
import os

from .state import PartialState







class 	__UpperCamelCase    (    logging.LoggerAdapter						):

  @staticmethod
  def __lowerCamelCase		(		_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    PartialState()
      return not main_process_only or (main_process_only and state.is_main_process)







  def __lowerCamelCase		(		self    ,_A    ,_A    ,*_A    ,**_A      ):
      '''simple docstring'''

      if PartialState._shared_state == {}:
          raise RuntimeError(
              'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'      )
      _lowerCAmelCase       :   Tuple  =    kwargs.pop('main_process_only'    ,_A      )
      _lowerCAmelCase       :   Any  =    kwargs.pop('in_order'    ,_A      )

      if self.isEnabledFor(_A      ):
          if self._should_log(_A      ):
              _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.process(_A    ,_A      )
              self.logger.log(_A    ,_A    ,*_A    ,**_A      )

          elif in_order:
              _lowerCAmelCase       :   str  =    PartialState()
              for i in range(state.num_processes      ):
                  if i == state.process_index:
                      _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.process(_A    ,_A      )
                      self.logger.log(_A    ,_A    ,*_A    ,**_A      )
                  state.wait_for_everyone()





def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase = None   ):

    '''simple docstring'''



    if log_level is None:
        _lowerCAmelCase       :   Union[str, Any]  =    os.environ.get('ACCELERATE_LOG_LEVEL' ,   _lowerCamelCase   )
    _lowerCAmelCase       :   int  =    logging.getLogger(_lowerCamelCase   )
    if log_level is not None:
        logger.setLevel(log_level.upper()   )
        logger.root.setLevel(log_level.upper()   )
    return MultiProcessAdapter(_lowerCamelCase ,   {}   )





