




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	:						str			=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCAmelCase	:						Dict			=     []
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__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
		lowercase    :Any      	=						state_dict.pop(lowerCamelCase   )
		lowercase    :int      	=						val



def       UpperCAmelCase__  (     lowerCamelCase   ):
		lowercase    :Union[str, Any]      	=						OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						lowercase    :Optional[int]      	=						key.replace("backbone.0.body",			"backbone.conv_encoder.model"   )
						lowercase    :Dict      	=						value
				else:
						lowercase    :Optional[Any]      	=						value

		return new_state_dict



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



def       UpperCAmelCase__  (     ):
		lowercase    :Tuple      	=						"http://images.cocodataset.org/val2017/000000039769.jpg"
		lowercase    :Union[str, Any]      	=						Image.open(requests.get(lowerCamelCase,			stream=lowerCamelCase   ).raw   )

		return im



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

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

		# prepare image
		lowercase    :List[str]      	=						prepare_img()
		lowercase    :int      	=						image_processor(images=lowerCamelCase,			return_tensors="pt"   )
		lowercase    :Tuple      	=						encoding["pixel_values"]

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

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


if __name__ == "__main__":
					_UpperCAmelCase	:						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."
					)
					_UpperCAmelCase	:						Optional[int]			=     parser.parse_args()
					convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase	:						List[Any]			=     {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						Union[str, Any]			=     [
         "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Swinv2ForImageClassification",
         "Swinv2ForMaskedImageModeling",
         "Swinv2Model",
         "Swinv2PreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swinva import (
              SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinvaForImageClassification,
              SwinvaForMaskedImageModeling,
              SwinvaModel,
              SwinvaPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Tuple			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



