
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    lowercase	(  ):
			lowerCamelCase_ 		=     DetaConfig(
			    num_queries=900  ,encoder_ffn_dim=2_048  ,decoder_ffn_dim=2_048  ,num_feature_levels=5  ,assign_first_stage=snake_case__  ,with_box_refine=snake_case__  ,two_stage=snake_case__  ,)

			# set labels
			lowerCamelCase_ 		=     91
			lowerCamelCase_ 		=     'huggingface/label-files'
			lowerCamelCase_ 		=     'coco-detection-id2label.json'
			lowerCamelCase_ 		=     json.load(open(cached_download(hf_hub_url(snake_case__  ,snake_case__  ,repo_type='''dataset'''	)	)  ,'''r'''	)	)
			lowerCamelCase_ 		=     {int(snake_case__	): v for k, v in idalabel.items()}
			lowerCamelCase_ 		=     idalabel
			lowerCamelCase_ 		=     {v: k for k, v in idalabel.items()}

			return config






def    lowercase	(  lowerCAmelCase__	):
			lowerCamelCase_ 		=     []

			# 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    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     dct.pop(snake_case__	)
			lowerCamelCase_ 		=     val






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			# transformer decoder self-attention layers
			lowerCamelCase_ 		=     config.d_model
			for i in range(config.decoder_layers	):
						# read in weights + bias of input projection layer of self-attention
						lowerCamelCase_ 		=     state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"	)
						lowerCamelCase_ 		=     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
						lowerCamelCase_ 		=     in_proj_weight[:hidden_size, :]
						lowerCamelCase_ 		=     in_proj_bias[:hidden_size]
						lowerCamelCase_ 		=     in_proj_weight[
						    hidden_size : hidden_size * 2, :
						]
						lowerCamelCase_ 		=     in_proj_bias[hidden_size : hidden_size * 2]
						lowerCamelCase_ 		=     in_proj_weight[-hidden_size:, :]
						lowerCamelCase_ 		=     in_proj_bias[-hidden_size:]






def    lowercase	(  ):
			lowerCamelCase_ 		=     'http://images.cocodataset.org/val2017/000000039769.jpg'
			lowerCamelCase_ 		=     Image.open(requests.get(snake_case__  ,stream=snake_case__	).raw	)

			return im






@torch.no_grad()
def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     get_deta_config()

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

			# rename keys
			lowerCamelCase_ 		=     create_rename_keys(snake_case__	)
			for src, dest in rename_keys:
						rename_key(snake_case__  ,snake_case__  ,snake_case__	)
			read_in_decoder_q_k_v(snake_case__  ,snake_case__	)

			# fix some prefixes
			for key in state_dict.copy().keys():
						if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
									lowerCamelCase_ 		=     state_dict.pop(snake_case__	)
									lowerCamelCase_ 		=     val
						if "input_proj" in key:
									lowerCamelCase_ 		=     state_dict.pop(snake_case__	)
									lowerCamelCase_ 		=     val
						if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
									lowerCamelCase_ 		=     state_dict.pop(snake_case__	)
									lowerCamelCase_ 		=     val

    # finally, create HuggingFace model and load state dict
			lowerCamelCase_ 		=     DetaForObjectDetection(snake_case__	)
			model.load_state_dict(snake_case__	)
			model.eval()

			lowerCamelCase_ 		=     'cuda' if torch.cuda.is_available() else 'cpu'
			model.to(snake_case__	)

			# load image processor
			lowerCamelCase_ 		=     DetaImageProcessor(format='''coco_detection'''	)

			# verify our conversion on image
			lowerCamelCase_ 		=     prepare_img()
			lowerCamelCase_ 		=     processor(images=snake_case__  ,return_tensors='''pt'''	)
			lowerCamelCase_ 		=     encoding['pixel_values']
			lowerCamelCase_ 		=     model(pixel_values.to(snake_case__	)	)

			# verify logits
			if model_name == "deta-resnet-50":
						lowerCamelCase_ 		=     torch.tensor(
						    [[-7.3_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]	)
						lowerCamelCase_ 		=     torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]	)
			elif model_name == "deta-resnet-50-24-epochs":
						lowerCamelCase_ 		=     torch.tensor(
						    [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]	)
						lowerCamelCase_ 		=     torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]	)

			assert torch.allclose(outputs.logits[0, :3, :3]  ,expected_logits.to(snake_case__	)  ,atol=1E-4	)
			assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,expected_boxes.to(snake_case__	)  ,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(snake_case__	).mkdir(exist_ok=snake_case__	)
						model.save_pretrained(snake_case__	)
						processor.save_pretrained(snake_case__	)

			# 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"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_						       =				logging.get_logger(__name__)

A_						       =				{
    """uw-madison/yoso-4096""": """https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json""",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			Dict									=						'yoso'




				def __init__(							self       ,	UpperCAmelCase=5_0265       ,	UpperCAmelCase=768       ,	UpperCAmelCase=12       ,	UpperCAmelCase=12       ,	UpperCAmelCase=3072       ,	UpperCAmelCase="gelu"       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=4096       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0.0_2       ,	UpperCAmelCase=1e-1_2       ,	UpperCAmelCase="absolute"       ,	UpperCAmelCase=True       ,	UpperCAmelCase=9       ,	UpperCAmelCase=64       ,	UpperCAmelCase=None       ,	UpperCAmelCase=True       ,	UpperCAmelCase=True       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0       ,	UpperCAmelCase=2       ,	**UpperCAmelCase       ,	):
							super().__init__(pad_token_id=UpperCAmelCase       ,	bos_token_id=UpperCAmelCase       ,	eos_token_id=UpperCAmelCase       ,	**UpperCAmelCase      )

							lowerCamelCase_ 		=     vocab_size
							lowerCamelCase_ 		=     max_position_embeddings
							lowerCamelCase_ 		=     hidden_size
							lowerCamelCase_ 		=     num_hidden_layers
							lowerCamelCase_ 		=     num_attention_heads
							lowerCamelCase_ 		=     intermediate_size
							lowerCamelCase_ 		=     hidden_act
							lowerCamelCase_ 		=     hidden_dropout_prob
							lowerCamelCase_ 		=     attention_probs_dropout_prob
							lowerCamelCase_ 		=     initializer_range
							lowerCamelCase_ 		=     type_vocab_size
							lowerCamelCase_ 		=     layer_norm_eps
							lowerCamelCase_ 		=     position_embedding_type
							lowerCamelCase_ 		=     use_expectation
							lowerCamelCase_ 		=     hash_code_len
							lowerCamelCase_ 		=     num_hash
							lowerCamelCase_ 		=     conv_window
							lowerCamelCase_ 		=     use_fast_hash
							lowerCamelCase_ 		=     lsh_backward



