






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			:	Optional[Any]								=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCamelCase			:	List[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  a__ (__lowercase							:Dict					,  __lowercase							:Union[str, Any]					,  __lowercase							:Any   )		-> Optional[int]:
				_A	:				Union[str, Any]   =      state_dict.pop(__lowercase   )
				_A	:				Tuple   =      val



def  a__ (__lowercase							:Optional[int]   )		-> Dict:
				_A	:				str   =      OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_A	:				int   =      key.replace('''backbone.0.body'''					,  '''backbone.conv_encoder.model'''   )
												_A	:				int   =      value
								else:
												_A	:				Tuple   =      value

				return new_state_dict



def  a__ (__lowercase							:Dict					,  __lowercase							:int=False   )		-> List[str]:
				_A	:				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	:				int   =      state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
								_A	:				List[str]   =      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	:				Optional[Any]   =      in_proj_weight[:256, :]
								_A	:				int   =      in_proj_bias[:256]
								_A	:				List[str]   =      in_proj_weight[256:512, :]
								_A	:				Optional[Any]   =      in_proj_bias[256:512]
								_A	:				Dict   =      in_proj_weight[-256:, :]
								_A	:				Dict   =      in_proj_bias[-256:]



def  a__ ()		-> List[str]:
				_A	:				str   =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_A	:				Tuple   =      Image.open(requests.get(__lowercase					,  stream=__lowercase   ).raw   )

				return im



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

				# load image processor
				_A	:				str   =      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				_A	:				Union[str, Any]   =      ConditionalDetrImageProcessor(format=__lowercase   )

				# prepare image
				_A	:				Optional[Any]   =      prepare_img()
				_A	:				Dict   =      image_processor(images=__lowercase					,  return_tensors='''pt'''   )
				_A	:				int   =      encoding['''pixel_values''']

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

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


if __name__ == "__main__":
	_UpperCamelCase			:	Dict								=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			:	str								=parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)











from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   ["image_processor", "tokenizer"]
		__snake_case      :       Tuple     =   "BridgeTowerImageProcessor"
		__snake_case      :       List[str]     =   ("RobertaTokenizer", "RobertaTokenizerFast")







		def __init__(					self       ,A__       ,A__		):
						super().__init__(A__       ,A__		)







		def __call__(					self       ,A__       ,A__ = None       ,A__ = True       ,A__ = False       ,A__ = None       ,A__ = None       ,A__ = 0       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      self.tokenizer(
						    text=A__       ,add_special_tokens=A__       ,padding=A__       ,truncation=A__       ,max_length=A__       ,stride=A__       ,pad_to_multiple_of=A__       ,return_token_type_ids=A__       ,return_attention_mask=A__       ,return_overflowing_tokens=A__       ,return_special_tokens_mask=A__       ,return_offsets_mapping=A__       ,return_length=A__       ,verbose=A__       ,return_tensors=A__       ,**A__       ,)
						# add pixel_values + pixel_mask
						_A	:				Optional[Any]   =      self.image_processor(
						    A__       ,return_tensors=A__       ,do_normalize=A__       ,do_center_crop=A__       ,**A__		)
						encoding.update(A__		)

						return encoding







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.batch_decode(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.decode(*A__       ,**A__		)







		@property
		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.tokenizer.model_input_names
						_A	:				str   =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



