






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase      :       Union[str, Any]    =						logging.get_logger(__name__)





def    __lowerCAmelCase   (    __snake_case							):
     # initialize config
     if "resnet-50" in model_name:
          __lowerCAmelCase             =       ResNetConfig.from_pretrained("microsoft/resnet-50"							)
     elif "resnet-101" in model_name:
          __lowerCAmelCase             =       ResNetConfig.from_pretrained("microsoft/resnet-101"							)
     else:
          raise ValueError("Model name should include either resnet50 or resnet101"							)

     __lowerCAmelCase             =       DetrConfig(use_timm_backbone=__snake_case       ,			backbone_config=__snake_case							)

     # set label attributes
     __lowerCAmelCase             =       "panoptic" in model_name
     if is_panoptic:
          __lowerCAmelCase             =       250
     else:
          __lowerCAmelCase             =       91
          __lowerCAmelCase             =       "huggingface/label-files"
          __lowerCAmelCase             =       "coco-detection-id2label.json"
          __lowerCAmelCase             =       json.load(open(hf_hub_download(__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, is_panoptic





def    __lowerCAmelCase   (    __snake_case							):
     # here we list all keys to be renamed (original name on the left, our name on the right)
     __lowerCAmelCase             =       []

     # stem
     # fmt: off
     rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")							)
     rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")							)
     rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")							)
     rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")							)
     rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                        )							)
    # fmt: on

     for i in range(config.encoder_layers							):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append(
              (
                  F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
                  F"""encoder.layers.{i}.self_attn.out_proj.weight""",
              )							)
          rename_keys.append(
              (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")							)
          rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""")							)
          rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""")							)
          rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""")							)
          rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""")							)
          rename_keys.append(
              (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")							)
          rename_keys.append(
              (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""")							)
          rename_keys.append(
              (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""")							)
          rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""")							)
          # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
          rename_keys.append(
              (
                  F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
                  F"""decoder.layers.{i}.self_attn.out_proj.weight""",
              )							)
          rename_keys.append(
              (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")							)
          rename_keys.append(
              (
                  F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
                  F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
              )							)
          rename_keys.append(
              (
                  F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
                  F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
              )							)
          rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""")							)
          rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""")							)
          rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""")							)
          rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""")							)
          rename_keys.append(
              (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")							)
          rename_keys.append(
              (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""")							)
          rename_keys.append(
              (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")							)
          rename_keys.append(
              (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")							)
          rename_keys.append(
              (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""")							)
          rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""")							)

     # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
     rename_keys.extend(
         [
             ("input_proj.weight", "input_projection.weight"),
             ("input_proj.bias", "input_projection.bias"),
             ("query_embed.weight", "query_position_embeddings.weight"),
             ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
             ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
             ("class_embed.weight", "class_labels_classifier.weight"),
             ("class_embed.bias", "class_labels_classifier.bias"),
             ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
             ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
             ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
             ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
             ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
             ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
         ]							)

     return rename_keys





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       state_dict.pop(__snake_case							)
     __lowerCAmelCase             =       val





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case=False							):
     __lowerCAmelCase             =       ""
     if is_panoptic:
          __lowerCAmelCase             =       "detr."

     # first: transformer encoder
     for i in range(6							):
          # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
          __lowerCAmelCase             =       state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""							)
          __lowerCAmelCase             =       state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""							)
          # next, add query, keys and values (in that order) to the state dict
          __lowerCAmelCase             =       in_proj_weight[:256, :]
          __lowerCAmelCase             =       in_proj_bias[:256]
          __lowerCAmelCase             =       in_proj_weight[256:512, :]
          __lowerCAmelCase             =       in_proj_bias[256:512]
          __lowerCAmelCase             =       in_proj_weight[-256:, :]
          __lowerCAmelCase             =       in_proj_bias[-256:]
     # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
     for i in range(6							):
          # read in weights + bias of input projection layer of self-attention
          __lowerCAmelCase             =       state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""							)
          __lowerCAmelCase             =       state_dict.pop(F"""{prefix}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[:256, :]
          __lowerCAmelCase             =       in_proj_bias[:256]
          __lowerCAmelCase             =       in_proj_weight[256:512, :]
          __lowerCAmelCase             =       in_proj_bias[256:512]
          __lowerCAmelCase             =       in_proj_weight[-256:, :]
          __lowerCAmelCase             =       in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          __lowerCAmelCase             =       state_dict.pop(
              F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""							)
          __lowerCAmelCase             =       state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""							)
          # next, add query, keys and values (in that order) of cross-attention to the state dict
          __lowerCAmelCase             =       in_proj_weight_cross_attn[:256, :]
          __lowerCAmelCase             =       in_proj_bias_cross_attn[:256]
          __lowerCAmelCase             =       in_proj_weight_cross_attn[256:512, :]
          __lowerCAmelCase             =       in_proj_bias_cross_attn[256:512]
          __lowerCAmelCase             =       in_proj_weight_cross_attn[-256:, :]
          __lowerCAmelCase             =       in_proj_bias_cross_attn[-256:]





def    __lowerCAmelCase   (    ):
     __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    __lowerCAmelCase   (    __snake_case       ,			__snake_case=None       ,			__snake_case=False							):
     __lowerCAmelCase      ,			__lowerCAmelCase             =       get_detr_config(__snake_case							)

     # load original model from torch hub
     __lowerCAmelCase             =       {
         "detr-resnet-50": "detr_resnet50",
         "detr-resnet-101": "detr_resnet101",
     }
     logger.info(F"""Converting model {model_name}..."""							)
     __lowerCAmelCase             =       torch.hub.load("facebookresearch/detr"       ,			model_name_to_original_name[model_name]       ,			pretrained=__snake_case							).eval()
     __lowerCAmelCase             =       detr.state_dict()
     # rename keys
     for src, dest in create_rename_keys(__snake_case							):
          if is_panoptic:
               __lowerCAmelCase             =       "detr." + src
          rename_key(__snake_case       ,			__snake_case       ,			__snake_case							)
     # query, key and value matrices need special treatment
     read_in_q_k_v(__snake_case       ,			is_panoptic=__snake_case							)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     __lowerCAmelCase             =       "detr.model." if is_panoptic else "model."
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith("detr"							)
                   and not key.startswith("class_labels_classifier"							)
                   and not key.startswith("bbox_predictor"							)
               ):
                    __lowerCAmelCase             =       state_dict.pop(__snake_case							)
                    __lowerCAmelCase             =       val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    __lowerCAmelCase             =       state_dict.pop(__snake_case							)
                    __lowerCAmelCase             =       val
               elif key.startswith("bbox_attention"							) or key.startswith("mask_head"							):
                    continue
               else:
                    __lowerCAmelCase             =       state_dict.pop(__snake_case							)
                    __lowerCAmelCase             =       val
          else:
               if not key.startswith("class_labels_classifier"							) and not key.startswith("bbox_predictor"							):
                    __lowerCAmelCase             =       state_dict.pop(__snake_case							)
                    __lowerCAmelCase             =       val

    # finally, create HuggingFace model and load state dict
     __lowerCAmelCase             =       DetrForSegmentation(__snake_case							) if is_panoptic else DetrForObjectDetection(__snake_case							)
     model.load_state_dict(__snake_case							)
     model.eval()

     # verify our conversion on an image
     __lowerCAmelCase             =       "coco_panoptic" if is_panoptic else "coco_detection"
     __lowerCAmelCase             =       DetrImageProcessor(format=__snake_case							)

     __lowerCAmelCase             =       processor(images=prepare_img()       ,			return_tensors="pt"							)
     __lowerCAmelCase             =       encoding["pixel_values"]

     __lowerCAmelCase             =       detr(__snake_case							)
     __lowerCAmelCase             =       model(__snake_case							)

     assert torch.allclose(outputs.logits       ,			original_outputs["pred_logits"]       ,			atol=1E-3							)
     assert torch.allclose(outputs.pred_boxes       ,			original_outputs["pred_boxes"]       ,			atol=1E-3							)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks       ,			original_outputs["pred_masks"]       ,			atol=1E-4							)
     print("Looks ok!"							)

     if pytorch_dump_folder_path is not None:
          # Save model and image processor
          logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""							)
          Path(__snake_case							).mkdir(exist_ok=__snake_case							)
          model.save_pretrained(__snake_case							)
          processor.save_pretrained(__snake_case							)

     if push_to_hub:
          # Upload model and image processor to the hub
          logger.info("Uploading PyTorch model and image processor to the hub..."							)
          model.push_to_hub(F"""nielsr/{model_name}"""							)
          processor.push_to_hub(F"""nielsr/{model_name}"""							)


if __name__ == "__main__":
 lowerCamelCase      :       Optional[Any]    =						argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''detr-resnet-50''',
     type=str,
     choices=['''detr-resnet-50''', '''detr-resnet-101'''],
     help='''Name of the DETR 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 to push the model to the hub or not.''')
 lowerCamelCase      :       Optional[Any]    =						parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)














import os

# Precomputes a list of the 100 first triangular numbers
lowerCamelCase      :       str    =						[int(0.5 * n * (n + 1)) for n in range(1, 101)]





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       os.path.dirname(os.path.realpath(__snake_case							)							)
     __lowerCAmelCase             =       os.path.join(__snake_case       ,			"words.txt"							)

     __lowerCAmelCase             =       ""
     with open(__snake_case							) as f:
          __lowerCAmelCase             =       f.readline()

     __lowerCAmelCase             =       [word.strip("\""							) for word in words.strip("\r\n"							).split(","							)]
     __lowerCAmelCase             =       [
         word
         for word in [sum(ord(__snake_case							) - 64 for x in word							) for word in words]
         if word in TRIANGULAR_NUMBERS
     ]
     return len(__snake_case							)


if __name__ == "__main__":
 print(solution())






