

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[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 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   int			,a__    :   Dict       )					->				Dict:
	__A :			Optional[Any]		    =			state_dict.pop(a__       )
	__A :			int		    =			val
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[Any]       )					->				Union[str, Any]:
	__A :			int		    =			OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			__A :			Dict		    =			key.replace("""backbone.0.body"""			,"""backbone.conv_encoder.model"""       )
			__A :			Optional[int]		    =			value
		else:
			__A :			Optional[int]		    =			value

	return new_state_dict
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   int=False       )					->				Optional[Any]:
	__A :			Union[str, Any]		    =			""""""
	if is_panoptic:
		__A :			List[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 :			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 :			List[Any]		    =			in_proj_weight[:256, :]
		__A :			List[str]		    =			in_proj_bias[:256]
		__A :			Dict		    =			in_proj_weight[256:512, :]
		__A :			List[str]		    =			in_proj_bias[256:512]
		__A :			List[str]		    =			in_proj_weight[-256:, :]
		__A :			List[str]		    =			in_proj_bias[-256:]
def 							__SCREAMING_SNAKE_CASE     (   )					->				Dict:
	__A :			str		    =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	__A :			Optional[Any]		    =			Image.open(requests.get(a__			,stream=a__       ).raw       )

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

	# load image processor
	__A :			Union[str, Any]		    =			"""coco_panoptic""" if is_panoptic else """coco_detection"""
	__A :			int		    =			ConditionalDetrImageProcessor(format=a__       )

	# prepare image
	__A :			int		    =			prepare_img()
	__A :			Optional[int]		    =			image_processor(images=a__			,return_tensors="""pt"""       )
	__A :			Union[str, Any]		    =			encoding["""pixel_values"""]

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

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


if __name__ == "__main__":
			UpperCAmelCase_							:					Any		 =   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)








class      lowerCamelCase_      :




	def __init__(     self						:     Dict					,      __A						:     Tuple					,      __A						:     Optional[int]					,      __A						:     int							):
		__A :			List[str]		    =			name
		__A :			Optional[int]		    =			value
		__A :			Optional[Any]		    =			weight




	def __repr__(     self						:     Any							):
		return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		return self.value




	def        lowerCAmelCase_       (     self						:     str							):
		return self.name




	def        lowerCAmelCase_       (     self						:     str							):
		return self.weight




	def        lowerCAmelCase_       (     self						:     Dict							):
		return self.value / self.weight






def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   Optional[int]			,a__    :   Union[str, Any]       )					->				int:
	__A :			Tuple		    =			[]
	for i in range(len(a__       )       ):
		menu.append(Things(name[i]			,value[i]			,weight[i]       )       )
	return menu
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Any			,a__    :   Optional[int]       )					->				Tuple:
	__A :			Optional[int]		    =			sorted(a__			,key=a__			,reverse=a__       )
	__A :			Optional[Any]		    =			[]
	__A    ,				__A :			Tuple		    =			0.0, 0.0
	for i in range(len(a__       )       ):
		if (total_cost + items_copy[i].get_weight()) <= max_cost:
			result.append(items_copy[i]       )
			total_cost += items_copy[i].get_weight()
			total_value += items_copy[i].get_value()
	return (result, total_value)
def 							__SCREAMING_SNAKE_CASE     (   )					->				List[Any]:
	pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()





