
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()
lowercase__          =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__          =[]
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        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Any  ):
	__a			:  Optional[Any]		    =  state_dict.pop(lowerCAmelCase__  )
	__a			:  int		    =  val




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ):
	__a			:  List[Any]		    =  OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			__a			:  Optional[int]		    =  key.replace('''backbone.0.body'''  ,       '''backbone.conv_encoder.model'''  )
			__a			:  str		    =  value
		else:
			__a			:  int		    =  value

	return new_state_dict




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Any=False  ):
	__a			:  Any		    =  ''''''
	if is_panoptic:
		__a			:  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)
		__a			:  List[Any]		    =  state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"  )
		__a			:  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
		__a			:  List[str]		    =  in_proj_weight[:2_5_6, :]
		__a			:  str		    =  in_proj_bias[:2_5_6]
		__a			:  Optional[Any]		    =  in_proj_weight[2_5_6:5_1_2, :]
		__a			:  List[Any]		    =  in_proj_bias[2_5_6:5_1_2]
		__a			:  Optional[Any]		    =  in_proj_weight[-2_5_6:, :]
		__a			:  int		    =  in_proj_bias[-2_5_6:]




def        __UpperCamelCase					(    ):
	__a			:  str		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
	__a			:  List[str]		    =  Image.open(requests.get(lowerCAmelCase__  ,       stream=lowerCAmelCase__  ).raw  )

	return im




@torch.no_grad()
def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Dict  ):
	__a			:  Optional[Any]		    =  ConditionalDetrConfig()
	# set backbone and dilation attributes
	if "resnet101" in model_name:
		__a			:  Tuple		    =  '''resnet101'''
	if "dc5" in model_name:
		__a			:  int		    =  True
	__a			:  List[Any]		    =  '''panoptic''' in model_name
	if is_panoptic:
		__a			:  Optional[Any]		    =  2_5_0
	else:
		__a			:  Optional[int]		    =  9_1
		__a			:  str		    =  '''huggingface/label-files'''
		__a			:  Optional[int]		    =  '''coco-detection-id2label.json'''
		__a			:  Tuple		    =  json.load(open(hf_hub_download(lowerCAmelCase__  ,       lowerCAmelCase__  ,       repo_type='''dataset'''  )  ,       '''r'''  )  )
		__a			:  List[Any]		    =  {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
		__a			:  int		    =  idalabel
		__a			:  Union[str, Any]		    =  {v: k for k, v in idalabel.items()}

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

	# prepare image
	__a			:  Union[str, Any]		    =  prepare_img()
	__a			:  Tuple		    =  image_processor(images=lowerCAmelCase__  ,       return_tensors='''pt'''  )
	__a			:  int		    =  encoding['''pixel_values''']

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

	# load original model from torch hub
	__a			:  List[str]		    =  torch.hub.load('''DeppMeng/ConditionalDETR'''  ,       lowerCAmelCase__  ,       pretrained=lowerCAmelCase__  ).eval()
	__a			:  Tuple		    =  conditional_detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			__a			:  Any		    =  '''conditional_detr.''' + src
		rename_key(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
	__a			:  int		    =  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
	__a			:  Optional[Any]		    =  '''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			:  int		    =  state_dict.pop(lowerCAmelCase__  )
				__a			:  Optional[Any]		    =  val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				__a			:  Union[str, Any]		    =  state_dict.pop(lowerCAmelCase__  )
				__a			:  Any		    =  val
			elif key.startswith('''bbox_attention'''  ) or key.startswith('''mask_head'''  ):
				continue
			else:
				__a			:  str		    =  state_dict.pop(lowerCAmelCase__  )
				__a			:  str		    =  val
		else:
			if not key.startswith('''class_labels_classifier'''  ) and not key.startswith('''bbox_predictor'''  ):
				__a			:  Optional[int]		    =  state_dict.pop(lowerCAmelCase__  )
				__a			:  str		    =  val
				# finally, create HuggingFace model and load state dict
	__a			:  List[Any]		    =  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
	__a			:  Any		    =  conditional_detr(lowerCAmelCase__  )
	__a			:  str		    =  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__":
						lowercase__          =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.'
						)
						lowercase__          =parser.parse_args()
						convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				CustomTokenizer
   pass
