
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()
SCREAMING_SNAKE_CASE__   :			int												=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE__   :			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  __magic_name__ (   __lowerCAmelCase	:						List[Any]		,  __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						str							)			->   List[Any]:
				__lowerCamelCase    					=	state_dict.pop(__lowerCAmelCase							)
				__lowerCamelCase    					=	val




def  __magic_name__ (   __lowerCAmelCase	:						Any							)			->   Dict:
				__lowerCamelCase    					=	OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												__lowerCamelCase    					=	key.replace('''backbone.0.body'''		,  '''backbone.conv_encoder.model'''							)
												__lowerCamelCase    					=	value
								else:
												__lowerCamelCase    					=	value

				return new_state_dict




def  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						Union[str, Any]=False							)			->   Optional[Any]:
				__lowerCamelCase    					=	''''''
				if is_panoptic:
								__lowerCamelCase    					=	'''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)
								__lowerCamelCase    					=	state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''							)
								__lowerCamelCase    					=	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
								__lowerCamelCase    					=	in_proj_weight[:256, :]
								__lowerCamelCase    					=	in_proj_bias[:256]
								__lowerCamelCase    					=	in_proj_weight[256:512, :]
								__lowerCamelCase    					=	in_proj_bias[256:512]
								__lowerCamelCase    					=	in_proj_weight[-256:, :]
								__lowerCamelCase    					=	in_proj_bias[-256:]




def  __magic_name__ (   )			->   Union[str, Any]:
				__lowerCamelCase    					=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowerCamelCase    					=	Image.open(requests.get(__lowerCAmelCase		,  stream=__lowerCAmelCase							).raw							)

				return im




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

				# load image processor
				__lowerCamelCase    					=	'''coco_panoptic''' if is_panoptic else '''coco_detection'''
				__lowerCamelCase    					=	ConditionalDetrImageProcessor(format=__lowerCAmelCase							)

				# prepare image
				__lowerCamelCase    					=	prepare_img()
				__lowerCamelCase    					=	image_processor(images=__lowerCAmelCase		,  return_tensors='''pt'''							)
				__lowerCamelCase    					=	encoding['''pixel_values''']

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

				# load original model from torch hub
				__lowerCamelCase    					=	torch.hub.load('''DeppMeng/ConditionalDETR'''		,  __lowerCAmelCase		,  pretrained=__lowerCAmelCase							).eval()
				__lowerCamelCase    					=	conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												__lowerCamelCase    					=	'''conditional_detr.''' + src
								rename_key(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase							)
				__lowerCamelCase    					=	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
				__lowerCamelCase    					=	'''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'''							)
												):
																__lowerCamelCase    					=	state_dict.pop(__lowerCAmelCase							)
																__lowerCamelCase    					=	val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																__lowerCamelCase    					=	state_dict.pop(__lowerCAmelCase							)
																__lowerCamelCase    					=	val
												elif key.startswith('''bbox_attention'''							) or key.startswith('''mask_head'''							):
																continue
												else:
																__lowerCamelCase    					=	state_dict.pop(__lowerCAmelCase							)
																__lowerCamelCase    					=	val
								else:
												if not key.startswith('''class_labels_classifier'''							) and not key.startswith('''bbox_predictor'''							):
																__lowerCamelCase    					=	state_dict.pop(__lowerCAmelCase							)
																__lowerCamelCase    					=	val
    # finally, create HuggingFace model and load state dict
				__lowerCamelCase    					=	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
				__lowerCamelCase    					=	conditional_detr(__lowerCAmelCase							)
				__lowerCamelCase    					=	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__":
					SCREAMING_SNAKE_CASE__   :			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."
					)
					SCREAMING_SNAKE_CASE__   :			str												=    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_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ResNetForImageClassification",
					    "ResNetModel",
					    "ResNetPreTrainedModel",
					    "ResNetBackbone",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFResNetForImageClassification",
					    "TFResNetModel",
					    "TFResNetPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    [
					    "FlaxResNetForImageClassification",
					    "FlaxResNetModel",
					    "FlaxResNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_resnet import (
										    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ResNetBackbone,
										    ResNetForImageClassification,
										    ResNetModel,
										    ResNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_resnet import (
										    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFResNetForImageClassification,
										    TFResNetModel,
										    TFResNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Any												=    _LazyModule(__name__, globals()["__file__"], _import_structure)



