


"""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, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
A     :   int			    =			logging.get_logger(__name__)


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			SwinConfig(
			    embed_dim=192    , depths=(2, 2, 18, 2)    , num_heads=(6, 12, 24, 48)    , window_size=12    , out_features=["stage2", "stage3", "stage4"]    , )

			__lowerCAmelCase				    =			DetaConfig(
			    backbone_config=_UpperCamelCase    , num_queries=900    , encoder_ffn_dim=2048    , decoder_ffn_dim=2048    , num_feature_levels=5    , assign_first_stage=_UpperCamelCase    , with_box_refine=_UpperCamelCase    , two_stage=_UpperCamelCase    , )

			# set labels
			__lowerCAmelCase				    =			"huggingface/label-files"
			if "o365" in model_name:
						__lowerCAmelCase				    =			366
						__lowerCAmelCase				    =			"object365-id2label.json"
			else:
						__lowerCAmelCase				    =			91
						__lowerCAmelCase				    =			"coco-detection-id2label.json"

			__lowerCAmelCase				    =			num_labels
			__lowerCAmelCase				    =			json.load(open(cached_download(hf_hub_url(_UpperCamelCase    , _UpperCamelCase    , repo_type="dataset"      )      )    , "r"      )      )
			__lowerCAmelCase				    =			{int(_UpperCamelCase      ): v for k, v in idalabel.items()}
			__lowerCAmelCase				    =			idalabel
			__lowerCAmelCase				    =			{v: k for k, v in idalabel.items()}

			return config


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[]

			# stem
			# fmt: off
			rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")      )
			rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")      )
			rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")      )
			rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")      )
			# stages
			for i in range(len(config.backbone_config.depths      )      ):
						for j in range(config.backbone_config.depths[i]      ):
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")      )

						if i < 3:
									rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")      )
									rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")      )

			rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")      )
			rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")      )
			rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")      )
			rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")      )
			rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")      )
			rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")      )

			# 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    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			dct.pop(_UpperCamelCase      )
			__lowerCAmelCase				    =			val


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
			for i in range(len(backbone_config.depths      )      ):
						__lowerCAmelCase				    =			num_features[i]
						for j in range(backbone_config.depths[i]      ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									__lowerCAmelCase				    =			state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"      )
									__lowerCAmelCase				    =			state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"      )
									# next, add query, keys and values (in that order) to the state dict
									__lowerCAmelCase				    =			in_proj_weight[:dim, :]
									__lowerCAmelCase				    =			in_proj_bias[: dim]
									__lowerCAmelCase				    =			in_proj_weight[
									    dim : dim * 2, :
									]
									__lowerCAmelCase				    =			in_proj_bias[
									    dim : dim * 2
									]
									__lowerCAmelCase				    =			in_proj_weight[
									    -dim :, :
									]
									__lowerCAmelCase				    =			in_proj_bias[-dim :]
									# fmt: on


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__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 _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowerCAmelCase				    =			Image.open(requests.get(_UpperCamelCase    , stream=_UpperCamelCase      ).raw      )

			return im


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			get_deta_config(_UpperCamelCase      )

			# load original state dict
			if model_name == "deta-swin-large":
						__lowerCAmelCase				    =			hf_hub_download(repo_id="nielsr/deta-checkpoints"    , filename="adet_swin_ft.pth"      )
			elif model_name == "deta-swin-large-o365":
						__lowerCAmelCase				    =			hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"    , filename="deta_swin_pt_o365.pth"      )
			else:
						raise ValueError(f"Model name {model_name} not supported"      )

			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location="cpu"      )["model"]

			# original state dict
			for name, param in state_dict.items():
						print(_UpperCamelCase    , param.shape      )

			# rename keys
			__lowerCAmelCase				    =			create_rename_keys(_UpperCamelCase      )
			for src, dest in rename_keys:
						rename_key(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )
			read_in_swin_q_k_v(_UpperCamelCase    , config.backbone_config      )
			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:
									__lowerCAmelCase				    =			state_dict.pop(_UpperCamelCase      )
									__lowerCAmelCase				    =			val
						if "input_proj" in key:
									__lowerCAmelCase				    =			state_dict.pop(_UpperCamelCase      )
									__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(_UpperCamelCase      )
									__lowerCAmelCase				    =			val

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

			__lowerCAmelCase				    =			"cuda" if torch.cuda.is_available() else "cpu"
			model.to(_UpperCamelCase      )

			# load image processor
			__lowerCAmelCase				    =			DetaImageProcessor(format="coco_detection"      )

			# verify our conversion on image
			__lowerCAmelCase				    =			prepare_img()
			__lowerCAmelCase				    =			processor(images=_UpperCamelCase    , return_tensors="pt"      )
			__lowerCAmelCase				    =			encoding["pixel_values"]
			__lowerCAmelCase				    =			model(pixel_values.to(_UpperCamelCase      )      )

			# verify logits
			print("Logits:"    , outputs.logits[0, :3, :3]      )
			print("Boxes:"    , outputs.pred_boxes[0, :3, :3]      )
			if model_name == "deta-swin-large":
						__lowerCAmelCase				    =			torch.tensor(
						    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]      )
						__lowerCAmelCase				    =			torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]      )
			elif model_name == "deta-swin-large-o365":
						__lowerCAmelCase				    =			torch.tensor(
						    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]      )
						__lowerCAmelCase				    =			torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]      )
			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     :   int			    =			argparse.ArgumentParser()

	parser.add_argument(
	    "--model_name",
	    type=str,
	    default="deta-swin-large",
	    choices=["deta-swin-large", "deta-swin-large-o365"],
	    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     :   Tuple			    =			parser.parse_args()
	convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











import math
import random



def A_	(		A__		,      A__ = False				)  ->		float:
       if deriv:
              return value * (1 - value)
       return 1 / (1 + math.exp(-value				))


# Initial Value
lowercase      :       Optional[Any]									=  0.02



def A_	(		A__		,      A__				)  ->		float:
       a__						:	int =				float(2 * (random.randint(1		,      100				)) - 1				)

       for _ in range(A__				):
              # Forward propagation
              a__						:	Union[str, Any] =				sigmoid_function(INITIAL_VALUE * weight				)
              # How much did we miss?
              a__						:	Optional[Any] =				(expected / 100) - layer_a
              # Error delta
              a__						:	Optional[int] =				layer_1_error * sigmoid_function(A__		,      A__				)
              # Update weight
              weight += INITIAL_VALUE * layer_1_delta

       return layer_a * 100


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowercase      :       Dict									=  int(input("""Expected value: """))
    lowercase      :       Dict									=  int(input("""Number of propagations: """))
    print(forward_propagation(expected, number_propagations))


