






"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_     =				logging.get_logger(__name__)




def   _lowerCAmelCase		(				)		->Dict:
						A__    :	List[str]			=	DetaConfig(
						    num_queries=9_0_0,    encoder_ffn_dim=2_0_4_8,    decoder_ffn_dim=2_0_4_8,    num_feature_levels=5,    assign_first_stage=UpperCAmelCase__,    with_box_refine=UpperCAmelCase__,    two_stage=UpperCAmelCase__,    )

						# set labels
						A__    :	str			=	9_1
						A__    :	List[str]			=	"""huggingface/label-files"""
						A__    :	Any			=	"""coco-detection-id2label.json"""
						A__    :	int			=	json.load(open(cached_download(hf_hub_url(UpperCAmelCase__,    UpperCAmelCase__,    repo_type="""dataset"""   )   ),    """r"""   )   )
						A__    :	Optional[int]			=	{int(UpperCAmelCase__   ): v for k, v in idalabel.items()}
						A__    :	Optional[int]			=	idalabel
						A__    :	List[str]			=	{v: k for k, v in idalabel.items()}

						return config




def   _lowerCAmelCase		(				UpperCAmelCase__							:       List[str]   )		->Any:
						A__    :	str			=	[]

						# stem
						# fmt: off
						rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")   )
						rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")   )
						rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")   )
						rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")   )
						rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.model.embedder.embedder.normalization.running_var""")   )
						# stages
						for stage_idx in range(len(config.backbone_config.depths   )   ):
												for layer_idx in range(config.backbone_config.depths[stage_idx]   ):
																		# shortcut
																		if layer_idx == 0:
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
																								        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
																								    )   )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
																								        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
																								    )   )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
																								        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
																								    )   )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
																								        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
																								    )   )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
																								        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
																								    )   )
																		# 3 convs
																		for i in range(3   ):
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
																								        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
																								    )   )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
																								        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
																								    )   )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
																								        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
																								    )   )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
																								        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
																								    )   )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
																								        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
																								    )   )
    # transformer encoder
						for i in range(config.encoder_layers   ):
												rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight')   )
												rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias')   )

						# transformer decoder
						for i in range(config.decoder_layers   ):
												rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight')   )
												rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias')   )

						# fmt: on

						return rename_keys




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       List[str],    UpperCAmelCase__							:       Any   )		->List[str]:
						A__    :	List[Any]			=	dct.pop(UpperCAmelCase__   )
						A__    :	List[str]			=	val




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int   )		->Optional[Any]:
						# transformer decoder self-attention layers
						A__    :	Any			=	config.d_model
						for i in range(config.decoder_layers   ):
												# read in weights + bias of input projection layer of self-attention
												A__    :	int			=	state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'   )
												A__    :	Any			=	state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias'   )
												# next, add query, keys and values (in that order) to the state dict
												A__    :	Union[str, Any]			=	in_proj_weight[:hidden_size, :]
												A__    :	Optional[int]			=	in_proj_bias[:hidden_size]
												A__    :	List[Any]			=	in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												A__    :	Union[str, Any]			=	in_proj_bias[hidden_size : hidden_size * 2]
												A__    :	str			=	in_proj_weight[-hidden_size:, :]
												A__    :	str			=	in_proj_bias[-hidden_size:]




def   _lowerCAmelCase		(				)		->Dict:
						A__    :	Optional[Any]			=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						A__    :	Dict			=	Image.open(requests.get(UpperCAmelCase__,    stream=UpperCAmelCase__   ).raw   )

						return im




@torch.no_grad()
def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       List[Any],    UpperCAmelCase__							:       str   )		->int:
						A__    :	List[str]			=	get_deta_config()

						# load original state dict
						if model_name == "deta-resnet-50":
												A__    :	Union[str, Any]			=	"""adet_checkpoint0011.pth"""
						elif model_name == "deta-resnet-50-24-epochs":
												A__    :	List[str]			=	"""adet_2x_checkpoint0023.pth"""
						else:
												raise ValueError(f'Model name {model_name} not supported'   )
						A__    :	Optional[int]			=	hf_hub_download(repo_id="""nielsr/deta-checkpoints""",    filename=UpperCAmelCase__   )
						A__    :	int			=	torch.load(UpperCAmelCase__,    map_location="""cpu"""   )["""model"""]

						# rename keys
						A__    :	Union[str, Any]			=	create_rename_keys(UpperCAmelCase__   )
						for src, dest in rename_keys:
												rename_key(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
						read_in_decoder_q_k_v(UpperCAmelCase__,    UpperCAmelCase__   )

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		A__    :	Dict			=	state_dict.pop(UpperCAmelCase__   )
																		A__    :	Optional[int]			=	val
												if "input_proj" in key:
																		A__    :	Union[str, Any]			=	state_dict.pop(UpperCAmelCase__   )
																		A__    :	str			=	val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		A__    :	List[Any]			=	state_dict.pop(UpperCAmelCase__   )
																		A__    :	Any			=	val

    # finally, create HuggingFace model and load state dict
						A__    :	str			=	DetaForObjectDetection(UpperCAmelCase__   )
						model.load_state_dict(UpperCAmelCase__   )
						model.eval()

						A__    :	Tuple			=	"""cuda""" if torch.cuda.is_available() else """cpu"""
						model.to(UpperCAmelCase__   )

						# load image processor
						A__    :	int			=	DetaImageProcessor(format="""coco_detection"""   )

						# verify our conversion on image
						A__    :	List[Any]			=	prepare_img()
						A__    :	List[str]			=	processor(images=UpperCAmelCase__,    return_tensors="""pt"""   )
						A__    :	Optional[Any]			=	encoding["""pixel_values"""]
						A__    :	Any			=	model(pixel_values.to(UpperCAmelCase__   )   )

						# verify logits
						if model_name == "deta-resnet-50":
												A__    :	str			=	torch.tensor(
												    [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]   )
												A__    :	Dict			=	torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]   )
						elif model_name == "deta-resnet-50-24-epochs":
												A__    :	List[Any]			=	torch.tensor(
												    [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]   )
												A__    :	Tuple			=	torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]   )

						assert torch.allclose(outputs.logits[0, :3, :3],    expected_logits.to(UpperCAmelCase__   ),    atol=1e-4   )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3],    expected_boxes.to(UpperCAmelCase__   ),    atol=1e-4   )
						print("""Everything ok!"""   )

						if pytorch_dump_folder_path:
												# Save model and processor
												logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'   )
												Path(UpperCAmelCase__   ).mkdir(exist_ok=UpperCAmelCase__   )
												model.save_pretrained(UpperCAmelCase__   )
												processor.save_pretrained(UpperCAmelCase__   )

						# Push to hub
						if push_to_hub:
												print("""Pushing model and processor to hub..."""   )
												model.push_to_hub(f'jozhang97/{model_name}'   )
												processor.push_to_hub(f'jozhang97/{model_name}'   )


if __name__ == "__main__":
						A_     =				argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_name''',
						    type=str,
						    default='''deta-resnet-50''',
						    choices=['''deta-resnet-50''', '''deta-resnet-50-24-epochs'''],
						    help='''Name of the 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.''',
						)
						parser.add_argument(
						    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
						)
						A_     =				parser.parse_args()
						convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)














"""simple docstring"""



import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
						from .testing_utils import (
						    floats_tensor,
						    load_hf_numpy,
						    load_image,
						    load_numpy,
						    load_pt,
						    nightly,
						    parse_flag_from_env,
						    print_tensor_test,
						    require_torch_a,
						    require_torch_gpu,
						    skip_mps,
						    slow,
						    torch_all_close,
						    torch_device,
						)
						from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_     =				get_logger(__name__)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Dict   )		->Union[str, Any]:
						if version.parse(UpperCAmelCase__   ) < version.parse(UpperCAmelCase__   ):
												if "dev" in min_version:
																		A__    :	Optional[int]			=	(
																		    """This example requires a source install from HuggingFace diffusers (see """
																		    """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
																		)
												else:
																		A__    :	Union[str, Any]			=	f'This example requires a minimum version of {min_version},'
												error_message += f' but the version found is {__version__}.\n'
												raise ImportError(UpperCAmelCase__   )






