




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()
__magic_name__  :  Any					=       logging.get_logger(__name__)
def 		a_							( __lowerCAmelCase				):
  # 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=__lowerCAmelCase   , backbone_config=__lowerCAmelCase				)

  # set label attributes
  lowerCAmelCase__										=      '''panoptic''' in model_name
  if is_panoptic:
    lowerCAmelCase__										=      2_50
  else:
    lowerCAmelCase__										=      91
    lowerCAmelCase__										=      '''huggingface/label-files'''
    lowerCAmelCase__										=      '''coco-detection-id2label.json'''
    lowerCAmelCase__										=      json.load(open(hf_hub_download(__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, is_panoptic
def 		a_							( __lowerCAmelCase				):
  # 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 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      state_dict.pop(__lowerCAmelCase				)
  lowerCAmelCase__										=      val
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase=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[:2_56, :]
    lowerCAmelCase__										=      in_proj_bias[:2_56]
    lowerCAmelCase__										=      in_proj_weight[2_56:5_12, :]
    lowerCAmelCase__										=      in_proj_bias[2_56:5_12]
    lowerCAmelCase__										=      in_proj_weight[-2_56:, :]
    lowerCAmelCase__										=      in_proj_bias[-2_56:]
  # 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[:2_56, :]
    lowerCAmelCase__										=      in_proj_bias[:2_56]
    lowerCAmelCase__										=      in_proj_weight[2_56:5_12, :]
    lowerCAmelCase__										=      in_proj_bias[2_56:5_12]
    lowerCAmelCase__										=      in_proj_weight[-2_56:, :]
    lowerCAmelCase__										=      in_proj_bias[-2_56:]
    # 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[:2_56, :]
    lowerCAmelCase__										=      in_proj_bias_cross_attn[:2_56]
    lowerCAmelCase__										=      in_proj_weight_cross_attn[2_56:5_12, :]
    lowerCAmelCase__										=      in_proj_bias_cross_attn[2_56:5_12]
    lowerCAmelCase__										=      in_proj_weight_cross_attn[-2_56:, :]
    lowerCAmelCase__										=      in_proj_bias_cross_attn[-2_56:]
def 		a_							( ):
  lowerCAmelCase__										=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  lowerCAmelCase__										=      Image.open(requests.get(__lowerCAmelCase   , stream=__lowerCAmelCase				).raw				)

  return im
@torch.no_grad()
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase=None   , __lowerCAmelCase=False				):
  lowerCAmelCase__				,      lowerCAmelCase__										=      get_detr_config(__lowerCAmelCase				)

  # 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=__lowerCAmelCase				).eval()
  lowerCAmelCase__										=      detr.state_dict()
  # rename keys
  for src, dest in create_rename_keys(__lowerCAmelCase				):
    if is_panoptic:
      lowerCAmelCase__										=      '''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
  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(__lowerCAmelCase				)
        lowerCAmelCase__										=      val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowerCAmelCase__										=      state_dict.pop(__lowerCAmelCase				)
        lowerCAmelCase__										=      val
      elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
        continue
      else:
        lowerCAmelCase__										=      state_dict.pop(__lowerCAmelCase				)
        lowerCAmelCase__										=      val
    else:
      if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
        lowerCAmelCase__										=      state_dict.pop(__lowerCAmelCase				)
        lowerCAmelCase__										=      val

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

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

  lowerCAmelCase__										=      processor(images=prepare_img()   , return_tensors='''pt'''				)
  lowerCAmelCase__										=      encoding['''pixel_values''']

  lowerCAmelCase__										=      detr(__lowerCAmelCase				)
  lowerCAmelCase__										=      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__":
      __magic_name__  :  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.""")
      __magic_name__  :  Dict					=       parser.parse_args()
      convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







from __future__ import annotations
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase = None				):
  lowerCAmelCase__										=      word_bank or []
  # create a table
  lowerCAmelCase__										=      len(__lowerCAmelCase				) + 1

  lowerCAmelCase__										=      []
  for _ in range(__lowerCAmelCase				):
    table.append([]				)
  # seed value
  lowerCAmelCase__										=      [[]]  # because empty string has empty combination

  # iterate through the indices
  for i in range(__lowerCAmelCase				):
    # condition
    if table[i] != []:
      for word in word_bank:
        # slice condition
        if target[i : i + len(__lowerCAmelCase				)] == word:
          lowerCAmelCase__										=      [
              [word, *way] for way in table[i]
          ]
          # adds the word to every combination the current position holds
          # now,push that combination to the table[i+len(word)]
          table[i + len(__lowerCAmelCase				)] += new_combinations

    # combinations are in reverse order so reverse for better output
  for combination in table[len(__lowerCAmelCase				)]:
    combination.reverse()

  return table[len(__lowerCAmelCase				)]


if __name__ == "__main__":
      print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
      print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
      print(
          all_construct(
              """hexagonosaurus""",
              ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
          )
      )

