
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()
lowerCamelCase_    :			Tuple    =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase_    :			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 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Dict:
				UpperCamelCase_:				Tuple						=    state_dict.pop(__lowerCAmelCase    )
				UpperCamelCase_:				Dict						=    val







def 							A__       (    lowerCamelCase    )	->						int:
				UpperCamelCase_:				str						=    OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												UpperCamelCase_:				Any						=    key.replace("""backbone.0.body"""  ,     """backbone.conv_encoder.model"""    )
												UpperCamelCase_:				int						=    value
								else:
												UpperCamelCase_:				List[Any]						=    value

				return new_state_dict







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







def 							A__       (    )	->						Optional[Any]:
				UpperCamelCase_:				Union[str, Any]						=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
				UpperCamelCase_:				Tuple						=    Image.open(requests.get(__lowerCAmelCase  ,     stream=__lowerCAmelCase    ).raw    )

				return im







@torch.no_grad()
def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						str:
				UpperCamelCase_:				Any						=    ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								UpperCamelCase_:				str						=    """resnet101"""
				if "dc5" in model_name:
								UpperCamelCase_:				Any						=    True
				UpperCamelCase_:				Any						=    """panoptic""" in model_name
				if is_panoptic:
								UpperCamelCase_:				Union[str, Any]						=    2_50
				else:
								UpperCamelCase_:				Tuple						=    91
								UpperCamelCase_:				List[str]						=    """huggingface/label-files"""
								UpperCamelCase_:				Tuple						=    """coco-detection-id2label.json"""
								UpperCamelCase_:				List[Any]						=    json.load(open(hf_hub_download(__lowerCAmelCase  ,     __lowerCAmelCase  ,     repo_type="""dataset"""    )  ,     """r"""    )    )
								UpperCamelCase_:				Union[str, Any]						=    {int(__lowerCAmelCase    ): v for k, v in idalabel.items()}
								UpperCamelCase_:				str						=    idalabel
								UpperCamelCase_:				Tuple						=    {v: k for k, v in idalabel.items()}

				# load image processor
				UpperCamelCase_:				int						=    """coco_panoptic""" if is_panoptic else """coco_detection"""
				UpperCamelCase_:				Tuple						=    ConditionalDetrImageProcessor(format=__lowerCAmelCase    )

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

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

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













'''simple docstring'''




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_pt_objects import *  # noqa F403
else:
 from .scheduling_consistency_models import CMStochasticIterativeScheduler
 from .scheduling_ddim import DDIMScheduler
 from .scheduling_ddim_inverse import DDIMInverseScheduler
 from .scheduling_ddim_parallel import DDIMParallelScheduler
 from .scheduling_ddpm import DDPMScheduler
 from .scheduling_ddpm_parallel import DDPMParallelScheduler
 from .scheduling_deis_multistep import DEISMultistepScheduler
 from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
 from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
 from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
 from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
 from .scheduling_euler_discrete import EulerDiscreteScheduler
 from .scheduling_heun_discrete import HeunDiscreteScheduler
 from .scheduling_ipndm import IPNDMScheduler
 from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
 from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
 from .scheduling_karras_ve import KarrasVeScheduler
 from .scheduling_pndm import PNDMScheduler
 from .scheduling_repaint import RePaintScheduler
 from .scheduling_sde_ve import ScoreSdeVeScheduler
 from .scheduling_sde_vp import ScoreSdeVpScheduler
 from .scheduling_unclip import UnCLIPScheduler
 from .scheduling_unipc_multistep import UniPCMultistepScheduler
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
 from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_flax_objects import *  # noqa F403
else:
 from .scheduling_ddim_flax import FlaxDDIMScheduler
 from .scheduling_ddpm_flax import FlaxDDPMScheduler
 from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
 from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
 from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
 from .scheduling_pndm_flax import FlaxPNDMScheduler
 from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
 from .scheduling_utils_flax import (
     FlaxKarrasDiffusionSchedulers,
     FlaxSchedulerMixin,
     FlaxSchedulerOutput,
     broadcast_to_shape_from_left,
 )


try:
 if not (is_torch_available() and is_scipy_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
 from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
 if not (is_torch_available() and is_torchsde_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
 from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler

