


'''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()
UpperCAmelCase_			:  List[str]										=   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_			:  Any										=   []
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       _lowercase							(					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Any,					UpperCamelCase__							:       Optional[Any]      ):
 __A	:		Union[str, Any]	  =      state_dict.pop(UpperCamelCase__      )
 __A	:		List[str]	  =      val


def       _lowercase							(					UpperCamelCase__							:       str      ):
 __A	:		Tuple	  =      OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   __A	:		str	  =      key.replace('backbone.0.body',					'backbone.conv_encoder.model'      )
   __A	:		Optional[Any]	  =      value
  else:
   __A	:		Optional[Any]	  =      value

 return new_state_dict


def       _lowercase							(					UpperCamelCase__							:       str,					UpperCamelCase__							:       List[Any]=False      ):
 __A	:		Union[str, Any]	  =      ''
 if is_panoptic:
  __A	:		int	  =      '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)
  __A	:		List[str]	  =      state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""      )
  __A	:		Dict	  =      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
  __A	:		List[str]	  =      in_proj_weight[:256, :]
  __A	:		Tuple	  =      in_proj_bias[:256]
  __A	:		Union[str, Any]	  =      in_proj_weight[256:512, :]
  __A	:		Optional[int]	  =      in_proj_bias[256:512]
  __A	:		int	  =      in_proj_weight[-256:, :]
  __A	:		str	  =      in_proj_bias[-256:]


def       _lowercase							(					):
 __A	:		Dict	  =      'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A	:		Dict	  =      Image.open(requests.get(UpperCamelCase__,					stream=UpperCamelCase__      ).raw      )

 return im


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

 # load image processor
 __A	:		int	  =      'coco_panoptic' if is_panoptic else 'coco_detection'
 __A	:		Dict	  =      ConditionalDetrImageProcessor(format=UpperCamelCase__      )

 # prepare image
 __A	:		Optional[int]	  =      prepare_img()
 __A	:		str	  =      image_processor(images=UpperCamelCase__,					return_tensors='pt'      )
 __A	:		List[str]	  =      encoding['pixel_values']

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

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


if __name__ == "__main__":
 UpperCAmelCase_			:  Optional[int]										=   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.'
 )
 UpperCAmelCase_			:  List[Any]										=   parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCAmelCase_			:  Any										=   {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Tuple										=   ['MaskFormerFeatureExtractor']
 UpperCAmelCase_			:  List[str]										=   ['MaskFormerImageProcessor']


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MaskFormerForInstanceSegmentation',
     'MaskFormerModel',
     'MaskFormerPreTrainedModel',
 ]
 UpperCAmelCase_			:  Optional[int]										=   [
     'MaskFormerSwinBackbone',
     'MaskFormerSwinModel',
     'MaskFormerSwinPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
 from .configuration_maskformer_swin import MaskFormerSwinConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_maskformer import MaskFormerFeatureExtractor
  from .image_processing_maskformer import MaskFormerImageProcessor
 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_maskformer import (
      MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaskFormerForInstanceSegmentation,
      MaskFormerModel,
      MaskFormerPreTrainedModel,
  )
  from .modeling_maskformer_swin import (
      MaskFormerSwinBackbone,
      MaskFormerSwinModel,
      MaskFormerSwinPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_			:  Union[str, Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure)






