



'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a     :    Union[str, Any]      						=					logging.get_logger(__name__)

a     :    Dict      						=					{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}

class     UpperCamelCase_				(							lowercase__    ):
	lowercase    =				'ibert'





	def __init__(						self	,	A=30522	,	A=768	,	A=12	,	A=12	,	A=3072	,	A="gelu"	,	A=0.1	,	A=0.1	,	A=512	,	A=2	,	A=0.0_2	,	A=1e-12	,	A=1	,	A=0	,	A=2	,	A="absolute"	,	A=False	,	A="none"	,	**A	,	)    ->    Any:
								super().__init__(pad_token_id=__lowerCamelCase	,	bos_token_id=__lowerCamelCase	,	eos_token_id=__lowerCamelCase	,	**__lowerCamelCase					)

								UpperCAmelCase  :	Any							  =	vocab_size
								UpperCAmelCase  :	Optional[Any]							  =	hidden_size
								UpperCAmelCase  :	Optional[int]							  =	num_hidden_layers
								UpperCAmelCase  :	int							  =	num_attention_heads
								UpperCAmelCase  :	List[str]							  =	hidden_act
								UpperCAmelCase  :	List[str]							  =	intermediate_size
								UpperCAmelCase  :	Optional[int]							  =	hidden_dropout_prob
								UpperCAmelCase  :	Any							  =	attention_probs_dropout_prob
								UpperCAmelCase  :	Tuple							  =	max_position_embeddings
								UpperCAmelCase  :	Any							  =	type_vocab_size
								UpperCAmelCase  :	Optional[int]							  =	initializer_range
								UpperCAmelCase  :	Tuple							  =	layer_norm_eps
								UpperCAmelCase  :	int							  =	position_embedding_type
								UpperCAmelCase  :	List[str]							  =	quant_mode
								UpperCAmelCase  :	int							  =	force_dequant




class     UpperCamelCase_				(							lowercase__    ):





	@property
	def 				_lowercase(						self					)    ->    List[Any]:



								if self.task == "multiple-choice":
															UpperCAmelCase  :	Any							  =	{0: "batch", 1: "choice", 2: "sequence"}
								else:
															UpperCAmelCase  :	Any							  =	{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								    ]					)






'''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()
a     :    List[Any]      						=					logging.get_logger(__name__)

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



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

							return new_state_dict



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False	)   ->    Optional[int]:
							UpperCAmelCase  :	Dict							  =	""""""
							if is_panoptic:
														UpperCAmelCase  :	Tuple							  =	"""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  :	List[Any]							  =	state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
														UpperCAmelCase  :	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
														UpperCAmelCase  :	Dict							  =	in_proj_weight[:2_5_6, :]
														UpperCAmelCase  :	Optional[Any]							  =	in_proj_bias[:2_5_6]
														UpperCAmelCase  :	List[Any]							  =	in_proj_weight[2_5_6:5_1_2, :]
														UpperCAmelCase  :	Tuple							  =	in_proj_bias[2_5_6:5_1_2]
														UpperCAmelCase  :	List[str]							  =	in_proj_weight[-2_5_6:, :]
														UpperCAmelCase  :	List[str]							  =	in_proj_bias[-2_5_6:]



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	List[Any]							  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase  :	Tuple							  =	Image.open(requests.get(_lowercase			,       stream=_lowercase	).raw	)

							return im



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

							# load image processor
							UpperCAmelCase  :	List[str]							  =	"""coco_panoptic""" if is_panoptic else """coco_detection"""
							UpperCAmelCase  :	List[Any]							  =	ConditionalDetrImageProcessor(format=_lowercase	)

							# prepare image
							UpperCAmelCase  :	Union[str, Any]							  =	prepare_img()
							UpperCAmelCase  :	Dict							  =	image_processor(images=_lowercase			,       return_tensors="""pt"""	)
							UpperCAmelCase  :	List[Any]							  =	encoding["""pixel_values"""]

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

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

