





'''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()
lowerCAmelCase_   :       str       =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_   :       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 					_lowerCamelCase							(					lowercase		: Any       ,				lowercase		: int       ,				lowercase		: int		)      ->      Dict:
				_a				      =					state_dict.pop(lowercase		)
				_a				      =					val






def 					_lowerCamelCase							(					lowercase		: List[Any]		)      ->      int:
				_a				      =					OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_a				      =					key.replace("backbone.0.body"       ,				"backbone.conv_encoder.model"		)
												_a				      =					value
								else:
												_a				      =					value

				return new_state_dict






def 					_lowerCamelCase							(					lowercase		: List[Any]       ,				lowercase		: List[str]=False		)      ->      Union[str, Any]:
				_a				      =					""""""
				if is_panoptic:
								_a				      =					"""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				      =					state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
								_a				      =					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				      =					in_proj_weight[:256, :]
								_a				      =					in_proj_bias[:256]
								_a				      =					in_proj_weight[256:512, :]
								_a				      =					in_proj_bias[256:512]
								_a				      =					in_proj_weight[-256:, :]
								_a				      =					in_proj_bias[-256:]






def 					_lowerCamelCase							(					)      ->      Optional[Any]:
				_a				      =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				_a				      =					Image.open(requests.get(lowercase       ,				stream=lowercase		).raw		)

				return im






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

				# load image processor
				_a				      =					"""coco_panoptic""" if is_panoptic else """coco_detection"""
				_a				      =					ConditionalDetrImageProcessor(format=lowercase		)

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

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

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







"""simple docstring"""


import math

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return math.pow(SCREAMING_SNAKE_CASE							,     2     ) - a

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return 2 * x

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Union[str, Any]				   =			2.0

						while start <= a:
												UpperCamelCase				:		Optional[Any]				   =			math.pow(SCREAMING_SNAKE_CASE							,     2     )

						return start

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE = 9999							,     SCREAMING_SNAKE_CASE = 0.00_00_00_00_00_00_01     ):

						if a < 0:
												raise ValueError("""math domain error"""     )

						UpperCamelCase				:		Optional[Any]				   =			get_initial_point(SCREAMING_SNAKE_CASE     )

						for _ in range(SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[str]				   =			value
												UpperCamelCase				:		Tuple				   =			value - fx(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) / fx_derivative(SCREAMING_SNAKE_CASE     )
												if abs(prev_value - value     ) < tolerance:
																		return value

						return value


if __name__ == "__main__":
		from doctest import testmod

		testmod()



