

import importlib.metadata
from typing import Union

from packaging.version import Version, parse

from .constants import STR_OPERATION_TO_FUNC


lowerCamelCase           =					parse(importlib.metadata.version('''torch'''))




def 			UpperCAmelCase__				(			_A      : Union[str, Version]	,      _A      : str	,      _A      : str							):





  '''simple docstring'''







  if operation not in STR_OPERATION_TO_FUNC.keys():
    raise ValueError(F"""`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys()							)}, received {operation}"""							)
  a__  =STR_OPERATION_TO_FUNC[operation]
  if isinstance(_A	,      _A							):
    a__  =parse(importlib.metadata.version(_A							)							)
  return operation(_A	,      parse(_A							)							)




def 			UpperCAmelCase__				(			_A      : str	,      _A      : str							):





  '''simple docstring'''







  return compare_versions(_A	,      _A	,      _A							)



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           =					logging.get_logger(__name__)




def 			UpperCAmelCase__				(			_A      : List[Any]							):





  '''simple docstring'''







  if "resnet-50" in model_name:
    a__  =ResNetConfig.from_pretrained('''microsoft/resnet-50'''							)
  elif "resnet-101" in model_name:
    a__  =ResNetConfig.from_pretrained('''microsoft/resnet-101'''							)
  else:
    raise ValueError('''Model name should include either resnet50 or resnet101'''							)

  a__  =DetrConfig(use_timm_backbone=_A	,      backbone_config=_A							)

  # set label attributes
  a__  ='''panoptic''' in model_name
  if is_panoptic:
    a__  =2_50
  else:
    a__  =91
    a__  ='''huggingface/label-files'''
    a__  ='''coco-detection-id2label.json'''
    a__  =json.load(open(hf_hub_download(_A	,      _A	,      repo_type='''dataset'''							)	,      '''r'''							)							)
    a__  ={int(_A							): v for k, v in idalabel.items()}
    a__  =idalabel
    a__  ={v: k for k, v in idalabel.items()}

  return config, is_panoptic




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  a__  =[]

  # 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__				(			_A      : str	,      _A      : Union[str, Any]	,      _A      : int							):





  '''simple docstring'''







  a__  =state_dict.pop(_A							)
  a__  =val




def 			UpperCAmelCase__				(			_A      : Dict	,      _A      : str=False							):





  '''simple docstring'''







  a__  =''''''
  if is_panoptic:
    a__  ='''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)
    a__  =state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""							)
    a__  =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
    a__  =in_proj_weight[:2_56, :]
    a__  =in_proj_bias[:2_56]
    a__  =in_proj_weight[2_56:5_12, :]
    a__  =in_proj_bias[2_56:5_12]
    a__  =in_proj_weight[-2_56:, :]
    a__  =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
    a__  =state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""							)
    a__  =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
    a__  =in_proj_weight[:2_56, :]
    a__  =in_proj_bias[:2_56]
    a__  =in_proj_weight[2_56:5_12, :]
    a__  =in_proj_bias[2_56:5_12]
    a__  =in_proj_weight[-2_56:, :]
    a__  =in_proj_bias[-2_56:]
    # read in weights + bias of input projection layer of cross-attention
    a__  =state_dict.pop(
        F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""							)
    a__  =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
    a__  =in_proj_weight_cross_attn[:2_56, :]
    a__  =in_proj_bias_cross_attn[:2_56]
    a__  =in_proj_weight_cross_attn[2_56:5_12, :]
    a__  =in_proj_bias_cross_attn[2_56:5_12]
    a__  =in_proj_weight_cross_attn[-2_56:, :]
    a__  =in_proj_bias_cross_attn[-2_56:]




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







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

  return im




@torch.no_grad()
def 			UpperCAmelCase__				(			_A      : Dict	,      _A      : Optional[Any]=None	,      _A      : List[str]=False							):





  '''simple docstring'''







  a__,      a__  =get_detr_config(_A							)

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

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

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

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

  a__  =detr(_A							)
  a__  =model(_A							)

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

  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           =					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           =					parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
