





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__   : Any					  = logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase__   : Tuple					  = []
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    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     state_dict.pop(A   )
	UpperCAmelCase_								=     val




def    A_(			A   ):
	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    A_(			A				,      A=False   ):
	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[:256, :]
		UpperCAmelCase_								=     in_proj_bias[:256]
		UpperCAmelCase_								=     in_proj_weight[256:512, :]
		UpperCAmelCase_								=     in_proj_bias[256:512]
		UpperCAmelCase_								=     in_proj_weight[-256:, :]
		UpperCAmelCase_								=     in_proj_bias[-256:]




def    A_(			):
	UpperCAmelCase_								=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
	UpperCAmelCase_								=     Image.open(requests.get(A				,      stream=A   ).raw   )

	return im




@torch.no_grad()
def    A_(			A				,      A   ):
	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_								=     250
	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__   : List[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__   : int					  = parser.parse_args()
				convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def    A_(			A				,      A				,      **A   ):
	UpperCAmelCase_								=     AutoConfig.from_pretrained(A				,      **A   )
	UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_config(A   )
	model.save_pretrained(A   )
	AutoTokenizer.from_pretrained(A   ).save_pretrained(A   )
	return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)


