


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()
a_ :str  							=			logging.get_logger(__name__)




def    a   (							A__							)	-> Any:




      '''simple docstring'''

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

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		DetrConfig(use_timm_backbone=A__      ,    backbone_config=A__							)

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

      return config, is_panoptic




def    a   (							A__							)	-> List[str]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  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    a   (							A__      ,    A__      ,    A__							)	-> Dict:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		state_dict.pop(A__							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		val




def    a   (							A__      ,    A__=False							)	-> List[Any]:




      '''simple docstring'''

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




def    a   (							)	-> Dict:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  str						     =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		Image.open(requests.get(A__      ,    stream=A__							).raw							)

      return im




@torch.no_grad()
def    a   (							A__      ,    A__=None      ,    A__=False							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  List[Any]						     =		get_detr_config(A__							)

      # load original model from torch hub
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		{
          '''detr-resnet-50''': '''detr_resnet50''',
          '''detr-resnet-101''': '''detr_resnet101''',
      }
      logger.info(f"""Converting model {model_name}..."""							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		torch.hub.load('''facebookresearch/detr'''      ,    model_name_to_original_name[model_name]      ,    pretrained=A__							).eval()
      SCREAMING_SNAKE_CASE__       :  Any						     =		detr.state_dict()
      # rename keys
      for src, dest in create_rename_keys(A__							):
            if is_panoptic:
                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		'''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
      SCREAMING_SNAKE_CASE__       :  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'''							)
                  ):
                        SCREAMING_SNAKE_CASE__       :  Any						     =		state_dict.pop(A__							)
                        SCREAMING_SNAKE_CASE__       :  Tuple						     =		val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        SCREAMING_SNAKE_CASE__       :  List[Any]						     =		state_dict.pop(A__							)
                        SCREAMING_SNAKE_CASE__       :  Any						     =		val
                  elif key.startswith('''bbox_attention'''							) or key.startswith('''mask_head'''							):
                        continue
                  else:
                        SCREAMING_SNAKE_CASE__       :  Tuple						     =		state_dict.pop(A__							)
                        SCREAMING_SNAKE_CASE__       :  str						     =		val
            else:
                  if not key.startswith('''class_labels_classifier'''							) and not key.startswith('''bbox_predictor'''							):
                        SCREAMING_SNAKE_CASE__       :  Tuple						     =		state_dict.pop(A__							)
                        SCREAMING_SNAKE_CASE__       :  List[str]						     =		val

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

      # verify our conversion on an image
      SCREAMING_SNAKE_CASE__       :  Any						     =		'''coco_panoptic''' if is_panoptic else '''coco_detection'''
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		DetrImageProcessor(format=A__							)

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		processor(images=prepare_img()      ,    return_tensors='''pt'''							)
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		encoding['''pixel_values''']

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		detr(A__							)
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		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__":
    a_ :List[str]  							=			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.')
    a_ :Union[str, Any]  							=			parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








__lowerCamelCase				=			"""Tobias Carryer"""

from time import time






class     UpperCAmelCase						:



   def __init__(self						:   Dict       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   str       ,		snake_case__						:   Optional[int]=int(time()					)					)						->      List[Any]:  # noqa: B008


          '''simple docstring'''


          snake_case			:  Dict     							= multiplier
          snake_case			:  Dict     							= increment
          snake_case			:  Union[str, Any]     							= modulo
          snake_case			:  Tuple     							= seed



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= (self.multiplier * self.seed + self.increment) % self.modulo
          return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   __lowerCamelCase				=			LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
   while True:
      print(lcg.next_number())





