




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	:						str			=     logging.get_logger(__name__)



def       UpperCAmelCase__  (     lowerCamelCase   ):
  # initialize config
  if "resnet-50" in model_name:
    lowercase    :List[Any]      	=						ResNetConfig.from_pretrained("microsoft/resnet-50"   )
  elif "resnet-101" in model_name:
    lowercase    :List[str]      	=						ResNetConfig.from_pretrained("microsoft/resnet-101"   )
  else:
    raise ValueError("Model name should include either resnet50 or resnet101"   )

  lowercase    :Any      	=						DetrConfig(use_timm_backbone=lowerCamelCase,			backbone_config=lowerCamelCase   )

  # set label attributes
  lowercase    :Optional[Any]      	=						"panoptic" in model_name
  if is_panoptic:
    lowercase    :Optional[Any]      	=						250
  else:
    lowercase    :Optional[Any]      	=						91
    lowercase    :str      	=						"huggingface/label-files"
    lowercase    :Optional[Any]      	=						"coco-detection-id2label.json"
    lowercase    :Dict      	=						json.load(open(hf_hub_download(lowerCamelCase,			lowerCamelCase,			repo_type="dataset"   ),			"r"   )   )
    lowercase    :Any      	=						{int(lowerCamelCase   ): v for k, v in idalabel.items()}
    lowercase    :Tuple      	=						idalabel
    lowercase    :List[Any]      	=						{v: k for k, v in idalabel.items()}

  return config, is_panoptic



def       UpperCAmelCase__  (     lowerCamelCase   ):
  # here we list all keys to be renamed (original name on the left, our name on the right)
  lowercase    :Union[str, 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       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  lowercase    :List[Any]      	=						state_dict.pop(lowerCamelCase   )
  lowercase    :str      	=						val



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=False   ):
  lowercase    :Union[str, Any]      	=						""
  if is_panoptic:
    lowercase    :int      	=						"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)
    lowercase    :Optional[int]      	=						state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"   )
    lowercase    :Optional[Any]      	=						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
    lowercase    :List[Any]      	=						in_proj_weight[:256, :]
    lowercase    :Dict      	=						in_proj_bias[:256]
    lowercase    :List[Any]      	=						in_proj_weight[256:512, :]
    lowercase    :List[str]      	=						in_proj_bias[256:512]
    lowercase    :Optional[int]      	=						in_proj_weight[-256:, :]
    lowercase    :Optional[Any]      	=						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
    lowercase    :str      	=						state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"   )
    lowercase    :Tuple      	=						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
    lowercase    :Optional[int]      	=						in_proj_weight[:256, :]
    lowercase    :Dict      	=						in_proj_bias[:256]
    lowercase    :Union[str, Any]      	=						in_proj_weight[256:512, :]
    lowercase    :List[Any]      	=						in_proj_bias[256:512]
    lowercase    :Union[str, Any]      	=						in_proj_weight[-256:, :]
    lowercase    :Dict      	=						in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    lowercase    :Optional[int]      	=						state_dict.pop(
        F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"   )
    lowercase    :Dict      	=						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
    lowercase    :str      	=						in_proj_weight_cross_attn[:256, :]
    lowercase    :Optional[int]      	=						in_proj_bias_cross_attn[:256]
    lowercase    :Union[str, Any]      	=						in_proj_weight_cross_attn[256:512, :]
    lowercase    :List[str]      	=						in_proj_bias_cross_attn[256:512]
    lowercase    :Dict      	=						in_proj_weight_cross_attn[-256:, :]
    lowercase    :Optional[int]      	=						in_proj_bias_cross_attn[-256:]



def       UpperCAmelCase__  (     ):
  lowercase    :str      	=						"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowercase    :Tuple      	=						Image.open(requests.get(lowerCamelCase,			stream=lowerCamelCase   ).raw   )

  return im



@torch.no_grad()
def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=False   ):
  lowercase	,    lowercase    :List[Any]      	=						get_detr_config(lowerCamelCase   )

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

    # finally, create HuggingFace model and load state dict
  lowercase    :Union[str, Any]      	=						DetrForSegmentation(lowerCamelCase   ) if is_panoptic else DetrForObjectDetection(lowerCamelCase   )
  model.load_state_dict(lowerCamelCase   )
  model.eval()

  # verify our conversion on an image
  lowercase    :Any      	=						"coco_panoptic" if is_panoptic else "coco_detection"
  lowercase    :Optional[Any]      	=						DetrImageProcessor(format=lowerCamelCase   )

  lowercase    :List[str]      	=						processor(images=prepare_img(),			return_tensors="pt"   )
  lowercase    :Any      	=						encoding["pixel_values"]

  lowercase    :str      	=						detr(lowerCamelCase   )
  lowercase    :List[Any]      	=						model(lowerCamelCase   )

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

  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	:						List[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.")
     _UpperCAmelCase	:						Tuple			=     parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


_UpperCAmelCase	:						List[Any]			=     logging.get_logger(__name__)



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :int      	=						r"\w+[.]\d+"
  lowercase    :Tuple      	=						re.findall(lowerCamelCase,			lowerCamelCase   )
  for pat in pats:
    lowercase    :List[str]      	=						key.replace(lowerCamelCase,			"_".join(pat.split("."   )   )   )
  return key



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  lowercase    :List[str]      	=						pt_tuple_key[:-1] + ("scale",)
  if (
      any("norm" in str_ for str_ in pt_tuple_key   )
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    lowercase    :Optional[int]      	=						pt_tuple_key[:-1] + ("scale",)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    lowercase    :int      	=						pt_tuple_key[:-1] + ("scale",)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    lowercase    :List[str]      	=						pt_tuple_key[:-1] + ("embedding",)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  lowercase    :Union[str, Any]      	=						pt_tuple_key[:-1] + ("kernel",)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    lowercase    :List[Any]      	=						pt_tensor.transpose(2,			3,			1,			0   )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  lowercase    :Optional[Any]      	=						pt_tuple_key[:-1] + ("kernel",)
  if pt_tuple_key[-1] == "weight":
    lowercase    :Tuple      	=						pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  lowercase    :Tuple      	=						pt_tuple_key[:-1] + ("weight",)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  lowercase    :int      	=						pt_tuple_key[:-1] + ("bias",)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor







def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase=42   ):
  # Step 1: Convert pytorch tensor to numpy
  lowercase    :Optional[int]      	=						{k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  lowercase    :str      	=						flax_model.init_weights(PRNGKey(lowerCamelCase   )   )

  lowercase    :Tuple      	=						flatten_dict(lowerCamelCase   )
  lowercase    :Optional[Any]      	=						{}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    lowercase    :List[Any]      	=						rename_key(lowerCamelCase   )
    lowercase    :List[Any]      	=						tuple(renamed_pt_key.split("."   )   )

    # Correctly rename weight parameters
    lowercase	,    lowercase    :List[str]      	=						rename_key_and_reshape_tensor(lowerCamelCase,			lowerCamelCase,			lowerCamelCase   )

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
            F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."   )

        # also add unexpected weight so that warning is thrown
    lowercase    :List[str]      	=						jnp.asarray(lowerCamelCase   )

  return unflatten_dict(lowerCamelCase   )



