


'''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()
__lowercase	:   int   		=					logging.get_logger(__name__)





def 				a_							(    )      ->Dict:






				'''simple docstring'''

				__lowerCamelCase		:   str           =       DetaConfig(
				    num_queries=900				,    encoder_ffn_dim=2048				,    decoder_ffn_dim=2048				,    num_feature_levels=5				,    assign_first_stage=lowercase				,    with_box_refine=lowercase				,    two_stage=lowercase				,    )

				# set labels
				__lowerCamelCase		:   List[str]           =       91
				__lowerCamelCase		:   str           =       "huggingface/label-files"
				__lowerCamelCase		:   Optional[int]           =       "coco-detection-id2label.json"
				__lowerCamelCase		:   Any           =       json.load(open(cached_download(hf_hub_url(lowercase				,    lowercase				,    repo_type="dataset"     )     )				,    "r"     )     )
				__lowerCamelCase		:   List[str]           =       {int(lowercase     ): v for k, v in idalabel.items()}
				__lowerCamelCase		:   str           =       idalabel
				__lowerCamelCase		:   Any           =       {v: k for k, v in idalabel.items()}

				return config





def 				a_							(    lowercase     )      ->str:






				'''simple docstring'''

				__lowerCamelCase		:   Dict           =       []

				# 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 				a_							(    lowercase				,    lowercase				,    lowercase     )      ->str:






				'''simple docstring'''

				__lowerCamelCase		:   Optional[Any]           =       dct.pop(lowercase     )
				__lowerCamelCase		:   Optional[int]           =       val





def 				a_							(    lowercase				,    lowercase     )      ->Dict:






				'''simple docstring'''

				__lowerCamelCase		:   Any           =       config.d_model
				for i in range(config.decoder_layers     ):
								# read in weights + bias of input projection layer of self-attention
								__lowerCamelCase		:   int           =       state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'     )
								__lowerCamelCase		:   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
								__lowerCamelCase		:   Any           =       in_proj_weight[:hidden_size, :]
								__lowerCamelCase		:   Dict           =       in_proj_bias[:hidden_size]
								__lowerCamelCase		:   Tuple           =       in_proj_weight[
								    hidden_size : hidden_size * 2, :
								]
								__lowerCamelCase		:   Dict           =       in_proj_bias[hidden_size : hidden_size * 2]
								__lowerCamelCase		:   str           =       in_proj_weight[-hidden_size:, :]
								__lowerCamelCase		:   Optional[Any]           =       in_proj_bias[-hidden_size:]





def 				a_							(    )      ->List[str]:






				'''simple docstring'''

				__lowerCamelCase		:   Optional[Any]           =       "http://images.cocodataset.org/val2017/000000039769.jpg"
				__lowerCamelCase		:   Any           =       Image.open(requests.get(lowercase				,    stream=lowercase     ).raw     )

				return im





@torch.no_grad()
def 				a_							(    lowercase				,    lowercase				,    lowercase     )      ->Any:






				'''simple docstring'''

				__lowerCamelCase		:   Dict           =       get_deta_config()

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

				# rename keys
				__lowerCamelCase		:   Any           =       create_rename_keys(lowercase     )
				for src, dest in rename_keys:
								rename_key(lowercase				,    lowercase				,    lowercase     )
				read_in_decoder_q_k_v(lowercase				,    lowercase     )

				# 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		:   List[Any]           =       state_dict.pop(lowercase     )
												__lowerCamelCase		:   Union[str, Any]           =       val
								if "input_proj" in key:
												__lowerCamelCase		:   int           =       state_dict.pop(lowercase     )
												__lowerCamelCase		:   Tuple           =       val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												__lowerCamelCase		:   List[str]           =       state_dict.pop(lowercase     )
												__lowerCamelCase		:   Tuple           =       val

    # finally, create HuggingFace model and load state dict
				__lowerCamelCase		:   Tuple           =       DetaForObjectDetection(lowercase     )
				model.load_state_dict(lowercase     )
				model.eval()

				__lowerCamelCase		:   Any           =       "cuda" if torch.cuda.is_available() else "cpu"
				model.to(lowercase     )

				# load image processor
				__lowerCamelCase		:   Optional[int]           =       DetaImageProcessor(format="coco_detection"     )

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

				# verify logits
				if model_name == "deta-resnet-50":
								__lowerCamelCase		:   Optional[Any]           =       torch.tensor(
								    [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]     )
								__lowerCamelCase		:   Union[str, Any]           =       torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]     )
				elif model_name == "deta-resnet-50-24-epochs":
								__lowerCamelCase		:   str           =       torch.tensor(
								    [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]     )
								__lowerCamelCase		:   Optional[Any]           =       torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]     )

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

				# 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__":
				__lowercase	:   str   		=					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."""
				)
				__lowercase	:   Dict   		=					parser.parse_args()
				convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''




def 				a_							(    lowercase				,    lowercase     )      ->list:






				'''simple docstring'''

				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       (
				    first_input_list_length
				    if first_input_list_length > second_input_list_length
				    else second_input_list_length
				)
				__lowerCamelCase		:   list           =       []
				for char_count in range(lowercase     ):
								if char_count < first_input_list_length:
												output_result_list.append(first_input_list[char_count]     )
								if char_count < second_input_list_length:
												output_result_list.append(second_input_list[char_count]     )

				return output_result_list


if __name__ == "__main__":
				print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)





