



"""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()
__snake_case				:  List[str] =       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case				:  Union[str, 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							(    __snake_case   ,__snake_case   ,__snake_case      )		->  str:
			__lowerCAmelCase :    Union[str, Any]				=			state_dict.pop(__snake_case      )
			__lowerCAmelCase :    Optional[int]				=			val


def      _lowercase							(    __snake_case      )		->  Any:
			__lowerCAmelCase :    Dict				=			OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									__lowerCAmelCase :    str				=			key.replace("backbone.0.body"   ,"backbone.conv_encoder.model"      )
									__lowerCAmelCase :    Dict				=			value
						else:
									__lowerCAmelCase :    List[str]				=			value

			return new_state_dict


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


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Union[str, Any]				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowerCAmelCase :    Optional[Any]				=			Image.open(requests.get(__snake_case   ,stream=__snake_case      ).raw      )

			return im


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

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

			# prepare image
			__lowerCAmelCase :    Dict				=			prepare_img()
			__lowerCAmelCase :    str				=			image_processor(images=__snake_case   ,return_tensors="pt"      )
			__lowerCAmelCase :    Dict				=			encoding["pixel_values"]

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

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


if __name__ == "__main__":
	__snake_case				:  List[str] =       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.'
	)
	__snake_case				:  Tuple =       parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
'''simple docstring'''

from __future__ import annotations

from typing import Any



def snake_case_      (					__snake_case  :							list[Any])			->					None:
   create_state_space_tree(__snake_case	,   []	,   0)



def snake_case_      (					__snake_case  :							list[Any]	,   __snake_case  :							list[Any]	,   __snake_case  :							int)			->					None:

   if index == len(__snake_case):
      print(__snake_case)
      return

   create_state_space_tree(__snake_case	,   __snake_case	,   index + 1)
   current_subsequence.append(sequence[index])
   create_state_space_tree(__snake_case	,   __snake_case	,   index + 1)
   current_subsequence.pop()


if __name__ == "__main__":
 A_     :						list[Any]    							=[3, 1, 2, 4]
 generate_all_subsequences(seq)

 seq.clear()
 seq.extend(['''A''', '''B''', '''C'''])
 generate_all_subsequences(seq)





