


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    		=       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase    		=       []
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_(				_A   :Dict				,     _A   :List[Any]				,     _A   :Dict  )->		Optional[int]:
			UpperCamelCase__					=       state_dict.pop(_A  )
			UpperCamelCase__					=       val
def        UpperCamelCase_(				_A   :Dict  )->		int:
			UpperCamelCase__					=       OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									UpperCamelCase__					=       key.replace("backbone.0.body"				,     "backbone.conv_encoder.model"  )
									UpperCamelCase__					=       value
						else:
									UpperCamelCase__					=       value

			return new_state_dict
def        UpperCamelCase_(				_A   :List[Any]				,     _A   :Union[str, Any]=False  )->		Optional[int]:
			UpperCamelCase__					=       ""
			if is_panoptic:
						UpperCamelCase__					=       "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)
						UpperCamelCase__					=       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''  )
						UpperCamelCase__					=       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
						UpperCamelCase__					=       in_proj_weight[:2_56, :]
						UpperCamelCase__					=       in_proj_bias[:2_56]
						UpperCamelCase__					=       in_proj_weight[2_56:5_12, :]
						UpperCamelCase__					=       in_proj_bias[2_56:5_12]
						UpperCamelCase__					=       in_proj_weight[-2_56:, :]
						UpperCamelCase__					=       in_proj_bias[-2_56:]
def        UpperCamelCase_(				)->		List[Any]:
			UpperCamelCase__					=       "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCamelCase__					=       Image.open(requests.get(_A				,     stream=_A  ).raw  )

			return im
@torch.no_grad()
def        UpperCamelCase_(				_A   :List[Any]				,     _A   :Optional[int]  )->		Union[str, Any]:
			UpperCamelCase__					=       ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						UpperCamelCase__					=       "resnet101"
			if "dc5" in model_name:
						UpperCamelCase__					=       True
			UpperCamelCase__					=       "panoptic" in model_name
			if is_panoptic:
						UpperCamelCase__					=       2_50
			else:
						UpperCamelCase__					=       91
						UpperCamelCase__					=       "huggingface/label-files"
						UpperCamelCase__					=       "coco-detection-id2label.json"
						UpperCamelCase__					=       json.load(open(hf_hub_download(_A				,     _A				,     repo_type="dataset"  )				,     "r"  )  )
						UpperCamelCase__					=       {int(_A  ): v for k, v in idalabel.items()}
						UpperCamelCase__					=       idalabel
						UpperCamelCase__					=       {v: k for k, v in idalabel.items()}

			# load image processor
			UpperCamelCase__					=       "coco_panoptic" if is_panoptic else "coco_detection"
			UpperCamelCase__					=       ConditionalDetrImageProcessor(format=_A  )

			# prepare image
			UpperCamelCase__					=       prepare_img()
			UpperCamelCase__					=       image_processor(images=_A				,     return_tensors="pt"  )
			UpperCamelCase__					=       encoding["pixel_values"]

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

			# load original model from torch hub
			UpperCamelCase__					=       torch.hub.load("DeppMeng/ConditionalDETR"				,     _A				,     pretrained=_A  ).eval()
			UpperCamelCase__					=       conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									UpperCamelCase__					=       "conditional_detr." + src
						rename_key(_A				,     _A				,     _A  )
			UpperCamelCase__					=       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
			UpperCamelCase__					=       "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"  )
									):
												UpperCamelCase__					=       state_dict.pop(_A  )
												UpperCamelCase__					=       val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												UpperCamelCase__					=       state_dict.pop(_A  )
												UpperCamelCase__					=       val
									elif key.startswith("bbox_attention"  ) or key.startswith("mask_head"  ):
												continue
									else:
												UpperCamelCase__					=       state_dict.pop(_A  )
												UpperCamelCase__					=       val
						else:
									if not key.startswith("class_labels_classifier"  ) and not key.startswith("bbox_predictor"  ):
												UpperCamelCase__					=       state_dict.pop(_A  )
												UpperCamelCase__					=       val
    # finally, create HuggingFace model and load state dict
			UpperCamelCase__					=       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
			UpperCamelCase__					=       conditional_detr(_A  )
			UpperCamelCase__					=       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    		=       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    		=       parser.parse_args()
						convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






from __future__ import annotations
def        UpperCamelCase_(				_A   :list[int]				,     _A   :int  )->		list[int]:
			UpperCamelCase__					=       0
			UpperCamelCase__					=       len(_A  ) - 1

			while i < j:
						if nums[i] + nums[j] == target:
									return [i, j]
						elif nums[i] + nums[j] < target:
									UpperCamelCase__					=       i + 1
						else:
									UpperCamelCase__					=       j - 1

			return []


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(f'''{two_pointer([2, 7, 1_1, 1_5], 9) = }''')


