






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							:   Dict							=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCAmelCase							:   Optional[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 					lowercase_ (    __snake_case     :							Tuple	,	__snake_case     :							List[Any]	,	__snake_case     :							Union[str, Any]      )     -> Optional[int]:





				'''simple docstring'''


				snake_case__      :int         =   state_dict.pop(__snake_case      )
				snake_case__      :List[str]         =   val





def 					lowercase_ (    __snake_case     :							Optional[int]      )     -> List[Any]:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												snake_case__      :Tuple         =   key.replace("backbone.0.body"	,	"backbone.conv_encoder.model"      )
												snake_case__      :Any         =   value
								else:
												snake_case__      :Any         =   value

				return new_state_dict





def 					lowercase_ (    __snake_case     :							Optional[Any]	,	__snake_case     :							Optional[int]=False      )     -> str:





				'''simple docstring'''


				snake_case__      :Union[str, Any]         =   ""
				if is_panoptic:
								snake_case__      :Dict         =   "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)
								snake_case__      :List[Any]         =   state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'      )
								snake_case__      :Tuple         =   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
								snake_case__      :Dict         =   in_proj_weight[:2_56, :]
								snake_case__      :int         =   in_proj_bias[:2_56]
								snake_case__      :str         =   in_proj_weight[2_56:5_12, :]
								snake_case__      :Union[str, Any]         =   in_proj_bias[2_56:5_12]
								snake_case__      :Union[str, Any]         =   in_proj_weight[-2_56:, :]
								snake_case__      :Optional[int]         =   in_proj_bias[-2_56:]





def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				snake_case__      :str         =   "http://images.cocodataset.org/val2017/000000039769.jpg"
				snake_case__      :Optional[int]         =   Image.open(requests.get(__snake_case	,	stream=__snake_case      ).raw      )

				return im





@torch.no_grad()
def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Optional[int]      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Any         =   ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								snake_case__      :Dict         =   "resnet101"
				if "dc5" in model_name:
								snake_case__      :Any         =   True
				snake_case__      :str         =   "panoptic" in model_name
				if is_panoptic:
								snake_case__      :Union[str, Any]         =   2_50
				else:
								snake_case__      :Optional[int]         =   91
								snake_case__      :List[str]         =   "huggingface/label-files"
								snake_case__      :int         =   "coco-detection-id2label.json"
								snake_case__      :Union[str, Any]         =   json.load(open(hf_hub_download(__snake_case	,	__snake_case	,	repo_type="dataset"      )	,	"r"      )      )
								snake_case__      :Optional[int]         =   {int(__snake_case      ): v for k, v in idalabel.items()}
								snake_case__      :Optional[Any]         =   idalabel
								snake_case__      :List[Any]         =   {v: k for k, v in idalabel.items()}

				# load image processor
				snake_case__      :Tuple         =   "coco_panoptic" if is_panoptic else "coco_detection"
				snake_case__      :Dict         =   ConditionalDetrImageProcessor(format=__snake_case      )

				# prepare image
				snake_case__      :List[str]         =   prepare_img()
				snake_case__      :List[Any]         =   image_processor(images=__snake_case	,	return_tensors="pt"      )
				snake_case__      :Any         =   encoding["pixel_values"]

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

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


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							:   Tuple							=  parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







import os

try:
	from .build_directory_md import good_file_paths
except ImportError:
	from build_directory_md import good_file_paths  # type: ignore

__UpperCAmelCase							:   List[Any]							=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCAmelCase							:   int							=  [file for file in filepaths if file != file.lower()]
if upper_files:
	print(F'''{len(upper_files)} files contain uppercase characters:''')
	print("\n".join(upper_files) + "\n")

__UpperCAmelCase							:   Any							=  [file for file in filepaths if " " in file]
if space_files:
	print(F'''{len(space_files)} files contain space characters:''')
	print("\n".join(space_files) + "\n")

__UpperCAmelCase							:   str							=  [file for file in filepaths if "-" in file]
if hyphen_files:
	print(F'''{len(hyphen_files)} files contain hyphen characters:''')
	print("\n".join(hyphen_files) + "\n")

__UpperCAmelCase							:   Dict							=  [file for file in filepaths if os.sep not in file]
if nodir_files:
	print(F'''{len(nodir_files)} files are not in a directory:''')
	print("\n".join(nodir_files) + "\n")

__UpperCAmelCase							:   int							=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)