






"""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()
_lowerCAmelCase            =	logging.get_logger(__name__)


def      UpperCamelCase       ( _A )     ->       List[str]:
				lowercase    :      Tuple       = SwinConfig(
				    embed_dim=192  ,				depths=(2, 2, 18, 2)  ,				num_heads=(6, 12, 24, 48)  ,				window_size=12  ,				out_features=["""stage2""", """stage3""", """stage4"""]  ,				)

				lowercase    :      int       = DetaConfig(
				    backbone_config=_A  ,				num_queries=900  ,				encoder_ffn_dim=2_048  ,				decoder_ffn_dim=2_048  ,				num_feature_levels=5  ,				assign_first_stage=_A  ,				with_box_refine=_A  ,				two_stage=_A  ,				)

				# set labels
				lowercase    :      Optional[int]       = """huggingface/label-files"""
				if "o365" in model_name:
								lowercase    :      str       = 366
								lowercase    :      str       = """object365-id2label.json"""
				else:
								lowercase    :      List[str]       = 91
								lowercase    :      int       = """coco-detection-id2label.json"""

				lowercase    :      Dict       = num_labels
				lowercase    :      str       = json.load(open(cached_download(hf_hub_url(_A  ,				_A  ,				repo_type="""dataset""" ) )  ,				"""r""" ) )
				lowercase    :      List[str]       = {int(_A ): v for k, v in idalabel.items()}
				lowercase    :      Dict       = idalabel
				lowercase    :      int       = {v: k for k, v in idalabel.items()}

				return config


def      UpperCamelCase       ( _A )     ->       int:
				lowercase    :      Tuple       = []

				# 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      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Any:
				lowercase    :      Any       = dct.pop(_A )
				lowercase    :      Optional[int]       = val


def      UpperCamelCase       ( _A  ,				_A )     ->       List[Any]:
				lowercase    :      List[str]       = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
				for i in range(len(backbone_config.depths ) ):
								lowercase    :      Tuple       = 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)
												lowercase    :      Dict       = state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight""" )
												lowercase    :      List[Any]       = 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
												lowercase    :      str       = in_proj_weight[:dim, :]
												lowercase    :      List[Any]       = in_proj_bias[: dim]
												lowercase    :      Tuple       = in_proj_weight[
												    dim : dim * 2, :
												]
												lowercase    :      Tuple       = in_proj_bias[
												    dim : dim * 2
												]
												lowercase    :      Union[str, Any]       = in_proj_weight[
												    -dim :, :
												]
												lowercase    :      Tuple       = in_proj_bias[-dim :]
												# fmt: on


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


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

				return im


@torch.no_grad()
def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Optional[Any]:
				lowercase    :      Any       = get_deta_config(_A )

				# load original state dict
				if model_name == "deta-swin-large":
								lowercase    :      List[Any]       = hf_hub_download(repo_id="""nielsr/deta-checkpoints"""  ,				filename="""adet_swin_ft.pth""" )
				elif model_name == "deta-swin-large-o365":
								lowercase    :      str       = 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""" )

				lowercase    :      Optional[int]       = torch.load(_A  ,				map_location="""cpu""" )["""model"""]

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

				# rename keys
				lowercase    :      Any       = create_rename_keys(_A )
				for src, dest in rename_keys:
								rename_key(_A  ,				_A  ,				_A )
				read_in_swin_q_k_v(_A  ,				config.backbone_config )
				read_in_decoder_q_k_v(_A  ,				_A )

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

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

				lowercase    :      int       = """cuda""" if torch.cuda.is_available() else """cpu"""
				model.to(_A )

				# load image processor
				lowercase    :      Dict       = DetaImageProcessor(format="""coco_detection""" )

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

				# verify logits
				print("""Logits:"""  ,				outputs.logits[0, :3, :3] )
				print("""Boxes:"""  ,				outputs.pred_boxes[0, :3, :3] )
				if model_name == "deta-swin-large":
								lowercase    :      List[Any]       = torch.tensor(
								    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]] )
								lowercase    :      Dict       = torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]] )
				elif model_name == "deta-swin-large-o365":
								lowercase    :      Dict       = torch.tensor(
								    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]] )
								lowercase    :      Tuple       = torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]] )
				assert torch.allclose(outputs.logits[0, :3, :3]  ,				expected_logits.to(_A )  ,				atol=1e-4 )
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,				expected_boxes.to(_A )  ,				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(_A ).mkdir(exist_ok=_A )
								model.save_pretrained(_A )
								processor.save_pretrained(_A )

				# 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__":
				_lowerCAmelCase            =	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.'
				)
				_lowerCAmelCase            =	parser.parse_args()
				convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""







import math
import random


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


# Initial Value
_lowerCAmelCase            =	0.02


def      UpperCamelCase       ( _A  ,				_A )     ->       float:
				lowercase    :      Any       = float(2 * (random.randint(1  ,				100 )) - 1 )

				for _ in range(_A ):
								# Forward propagation
								lowercase    :      Dict       = sigmoid_function(INITIAL_VALUE * weight )
								# How much did we miss?
								lowercase    :      Any       = (expected / 100) - layer_a
								# Error delta
								lowercase    :      Dict       = 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()

				_lowerCAmelCase            =	int(input('Expected value: '))
				_lowerCAmelCase            =	int(input('Number of propagations: '))
				print(forward_propagation(expected, number_propagations))
