





"""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()
UpperCamelCase_									=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase_									=[]
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_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Dict         =   state_dict.pop(_lowercase       )
						_UpperCamelCase      : Optional[int]         =   val





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[Any]         =   OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		_UpperCamelCase      : List[Any]         =   key.replace('''backbone.0.body'''	,						'''backbone.conv_encoder.model'''       )
																		_UpperCamelCase      : Union[str, Any]         =   value
												else:
																		_UpperCamelCase      : Union[str, Any]         =   value

						return new_state_dict





def 	a_    (							_lowercase	,						_lowercase=False       ):
						_UpperCamelCase      : Union[str, Any]         =   ''''''
						if is_panoptic:
												_UpperCamelCase      : str         =   '''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      : Any         =   state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
												_UpperCamelCase      : 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
												_UpperCamelCase      : int         =   in_proj_weight[:256, :]
												_UpperCamelCase      : Tuple         =   in_proj_bias[:256]
												_UpperCamelCase      : Optional[Any]         =   in_proj_weight[256:512, :]
												_UpperCamelCase      : Optional[Any]         =   in_proj_bias[256:512]
												_UpperCamelCase      : Dict         =   in_proj_weight[-256:, :]
												_UpperCamelCase      : Optional[int]         =   in_proj_bias[-256:]





def 	a_    (							):
						_UpperCamelCase      : List[str]         =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						_UpperCamelCase      : Dict         =   Image.open(requests.get(_lowercase	,						stream=_lowercase       ).raw       )

						return im





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

						# load image processor
						_UpperCamelCase      : Any         =   '''coco_panoptic''' if is_panoptic else '''coco_detection'''
						_UpperCamelCase      : int         =   ConditionalDetrImageProcessor(format=_lowercase       )

						# prepare image
						_UpperCamelCase      : Dict         =   prepare_img()
						_UpperCamelCase      : Optional[Any]         =   image_processor(images=_lowercase	,						return_tensors='''pt'''       )
						_UpperCamelCase      : Any         =   encoding['''pixel_values''']

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

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










"""simple docstring"""




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
						from .autoencoder_kl import AutoencoderKL
						from .controlnet import ControlNetModel
						from .dual_transformer_ad import DualTransformeraDModel
						from .modeling_utils import ModelMixin
						from .prior_transformer import PriorTransformer
						from .ta_film_transformer import TaFilmDecoder
						from .transformer_ad import TransformeraDModel
						from .unet_ad import UNetaDModel
						from .unet_ad import UNetaDModel
						from .unet_ad_condition import UNetaDConditionModel
						from .unet_ad_condition import UNetaDConditionModel
						from .vq_model import VQModel

if is_flax_available():
						from .controlnet_flax import FlaxControlNetModel
						from .unet_ad_condition_flax import FlaxUNetaDConditionModel
						from .vae_flax import FlaxAutoencoderKL



