

"""simple docstring"""

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()
_UpperCamelCase		     =    logging.get_logger(__name__)



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Dict:
 # initialize config
 if "resnet-50" in model_name:
  lowerCAmelCase__   :				Union[str, Any] 			=				ResNetConfig.from_pretrained("microsoft/resnet-50"   )
 elif "resnet-101" in model_name:
  lowerCAmelCase__   :				Dict 			=				ResNetConfig.from_pretrained("microsoft/resnet-101"   )
 else:
  raise ValueError("Model name should include either resnet50 or resnet101"   )

 lowerCAmelCase__   :				Optional[int] 			=				DetrConfig(use_timm_backbone=lowercase__					,					backbone_config=lowercase__   )

 # set label attributes
 lowerCAmelCase__   :				Optional[int] 			=				"panoptic" in model_name
 if is_panoptic:
  lowerCAmelCase__   :				Union[str, Any] 			=				2_5_0
 else:
  lowerCAmelCase__   :				str 			=				9_1
  lowerCAmelCase__   :				int 			=				"huggingface/label-files"
  lowerCAmelCase__   :				Any 			=				"coco-detection-id2label.json"
  lowerCAmelCase__   :				Union[str, Any] 			=				json.load(open(hf_hub_download(lowercase__					,					lowercase__					,					repo_type="dataset"   )					,					"r"   )   )
  lowerCAmelCase__   :				Any 			=				{int(lowercase__   ): v for k, v in idalabel.items()}
  lowerCAmelCase__   :				int 			=				idalabel
  lowerCAmelCase__   :				Optional[int] 			=				{v: k for k, v in idalabel.items()}

 return config, is_panoptic



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						str:
 # here we list all keys to be renamed (original name on the left, our name on the right)
 lowerCAmelCase__   :				Any 			=				[]

 # 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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						int:
 lowerCAmelCase__   :				Optional[Any] 			=				state_dict.pop(lowercase__   )
 lowerCAmelCase__   :				List[str] 			=				val



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__=False   )			->						int:
 lowerCAmelCase__   :				Dict 			=				""
 if is_panoptic:
  lowerCAmelCase__   :				List[str] 			=				"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__   :				Tuple 			=				state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
  lowerCAmelCase__   :				Tuple 			=				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__   :				List[str] 			=				in_proj_weight[:2_5_6, :]
  lowerCAmelCase__   :				Union[str, Any] 			=				in_proj_bias[:2_5_6]
  lowerCAmelCase__   :				Tuple 			=				in_proj_weight[2_5_6:5_1_2, :]
  lowerCAmelCase__   :				List[Any] 			=				in_proj_bias[2_5_6:5_1_2]
  lowerCAmelCase__   :				Any 			=				in_proj_weight[-2_5_6:, :]
  lowerCAmelCase__   :				int 			=				in_proj_bias[-2_5_6:]
 # 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__   :				str 			=				state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
  lowerCAmelCase__   :				List[Any] 			=				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__   :				int 			=				in_proj_weight[:2_5_6, :]
  lowerCAmelCase__   :				Any 			=				in_proj_bias[:2_5_6]
  lowerCAmelCase__   :				Tuple 			=				in_proj_weight[2_5_6:5_1_2, :]
  lowerCAmelCase__   :				Optional[int] 			=				in_proj_bias[2_5_6:5_1_2]
  lowerCAmelCase__   :				Optional[Any] 			=				in_proj_weight[-2_5_6:, :]
  lowerCAmelCase__   :				Optional[Any] 			=				in_proj_bias[-2_5_6:]
  # read in weights + bias of input projection layer of cross-attention
  lowerCAmelCase__   :				Tuple 			=				state_dict.pop(
      F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
  lowerCAmelCase__   :				Optional[Any] 			=				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__   :				List[str] 			=				in_proj_weight_cross_attn[:2_5_6, :]
  lowerCAmelCase__   :				Tuple 			=				in_proj_bias_cross_attn[:2_5_6]
  lowerCAmelCase__   :				Union[str, Any] 			=				in_proj_weight_cross_attn[2_5_6:5_1_2, :]
  lowerCAmelCase__   :				Union[str, Any] 			=				in_proj_bias_cross_attn[2_5_6:5_1_2]
  lowerCAmelCase__   :				List[Any] 			=				in_proj_weight_cross_attn[-2_5_6:, :]
  lowerCAmelCase__   :				List[str] 			=				in_proj_bias_cross_attn[-2_5_6:]



def       SCREAMING_SNAKE_CASE							( )			->						Dict:
 lowerCAmelCase__   :				str 			=				"http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCAmelCase__   :				Tuple 			=				Image.open(requests.get(lowercase__					,					stream=lowercase__   ).raw   )

 return im



@torch.no_grad()
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__=None					,					lowercase__=False   )			->						str:
 lowerCAmelCase__			,						lowerCAmelCase__   :				Dict 			=				get_detr_config(lowercase__   )

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

    # finally, create HuggingFace model and load state dict
 lowerCAmelCase__   :				Optional[int] 			=				DetrForSegmentation(lowercase__   ) if is_panoptic else DetrForObjectDetection(lowercase__   )
 model.load_state_dict(lowercase__   )
 model.eval()

 # verify our conversion on an image
 lowerCAmelCase__   :				Tuple 			=				"coco_panoptic" if is_panoptic else "coco_detection"
 lowerCAmelCase__   :				Dict 			=				DetrImageProcessor(format=lowercase__   )

 lowerCAmelCase__   :				Dict 			=				processor(images=prepare_img()					,					return_tensors="pt"   )
 lowerCAmelCase__   :				Optional[Any] 			=				encoding["pixel_values"]

 lowerCAmelCase__   :				Union[str, Any] 			=				detr(lowercase__   )
 lowerCAmelCase__   :				Dict 			=				model(lowercase__   )

 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(lowercase__   ).mkdir(exist_ok=lowercase__   )
  model.save_pretrained(lowercase__   )
  processor.save_pretrained(lowercase__   )

 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__":
    _UpperCamelCase		     =    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.""")
    _UpperCamelCase		     =    parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 if digit_amount > 0:
  return round(number - int(lowercase__   )					,					lowercase__   )
 return number - int(lowercase__   )


if __name__ == "__main__":
    print(decimal_isolate(1.53, 0))
    print(decimal_isolate(35.345, 1))
    print(decimal_isolate(35.345, 2))
    print(decimal_isolate(35.345, 3))
    print(decimal_isolate(-14.789, 3))
    print(decimal_isolate(0, 2))
    print(decimal_isolate(-14.123, 1))
    print(decimal_isolate(-14.123, 2))
    print(decimal_isolate(-14.123, 3))






