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()
lowerCAmelCase__							= logging.get_logger(__name__)






def       __lowerCamelCase		(						):
       lowerCAmelCase__     	=					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=lowerCAmelCase__	,  with_box_refine=lowerCAmelCase__	,  two_stage=lowerCAmelCase__	,  )

       # set labels
       lowerCAmelCase__     	=					9_1
       lowerCAmelCase__     	=					'huggingface/label-files'
       lowerCAmelCase__     	=					'coco-detection-id2label.json'
       lowerCAmelCase__     	=					json.load(open(cached_download(hf_hub_url(lowerCAmelCase__	,  lowerCAmelCase__	,  repo_type='dataset'	)	)	,  'r'	)	)
       lowerCAmelCase__     	=					{int(lowerCAmelCase__	): v for k, v in idalabel.items()}
       lowerCAmelCase__     	=					idalabel
       lowerCAmelCase__     	=					{v: k for k, v in idalabel.items()}

       return config






def       __lowerCamelCase		(						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       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					dct.pop(lowerCAmelCase__	)
       lowerCAmelCase__     	=					val






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

       return im






@torch.no_grad()
def       __lowerCamelCase		(						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=lowerCAmelCase__	)
       lowerCAmelCase__     	=					torch.load(lowerCAmelCase__	,  map_location='cpu'	)['model']

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

       # 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(lowerCAmelCase__	)
                     lowerCAmelCase__     	=					val
              if "input_proj" in key:
                     lowerCAmelCase__     	=					state_dict.pop(lowerCAmelCase__	)
                     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(lowerCAmelCase__	)
                     lowerCAmelCase__     	=					val

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

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

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

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

       # verify logits
       if model_name == "deta-resnet-50":
              lowerCAmelCase__     	=					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__     	=					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__     	=					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__     	=					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(lowerCAmelCase__	)	,  atol=1e-4	)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,  expected_boxes.to(lowerCAmelCase__	)	,  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(lowerCAmelCase__	).mkdir(exist_ok=lowerCAmelCase__	)
              model.save_pretrained(lowerCAmelCase__	)
              processor.save_pretrained(lowerCAmelCase__	)

       # 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-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.'
    )
    lowerCAmelCase__							= parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__ = 100						):
 __lowerCamelCase		:							List[Any]							=						[[0 for _ in range(SCREAMING_SNAKE_CASE__						)] for _ in range(m + 1						)]
 for i in range(m + 1						):
  __lowerCamelCase		:							Any							=						1

 for n in range(m + 1						):
  for k in range(1			,       SCREAMING_SNAKE_CASE__						):
   memo[n][k] += memo[n][k - 1]
   if n > k:
    memo[n][k] += memo[n - k - 1][k]

 return memo[m][m - 1] - 1


if __name__ == "__main__":
       print(solution(int(input('Enter a number: ').strip())))

