

'''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					:			Dict       =	logging.get_logger(__name__)







def   a__			(    a__		):




     """simple docstring"""


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

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

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

     return config, is_panoptic







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []

     # 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__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = state_dict.pop(a__		)
     __SCREAMING_SNAKE_CASE      = val







def   a__			(    a__ ,       a__=False		):




     """simple docstring"""


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







def   a__			(    ):




     """simple docstring"""


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

     return im







@torch.no_grad()
def   a__			(    a__ ,       a__=None ,       a__=False		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = get_detr_config(a__		)

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

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

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

     __SCREAMING_SNAKE_CASE      = processor(images=prepare_img() ,       return_tensors="""pt"""		)
     __SCREAMING_SNAKE_CASE      = encoding["""pixel_values"""]

     __SCREAMING_SNAKE_CASE      = detr(a__		)
     __SCREAMING_SNAKE_CASE      = 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__":
  UpperCAmelCase					:			Optional[int]       =	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					:			List[str]       =	parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "CLIPImageProcessor"
 lowerCAmelCase__       = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = None
      if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError("""You need to specify an `image_processor`."""     )
      if tokenizer is None:
           raise ValueError("""You need to specify a `tokenizer`."""     )

      super().__init__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """simple docstring"""




      if text is None and images is None:
           raise ValueError("""You have to specify either text or images. Both cannot be none."""     )

      if text is not None:
           __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if images is not None:
           __SCREAMING_SNAKE_CASE      = self.image_processor(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE      = image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE     )   ,				tensor_type=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				*__SCREAMING_SNAKE_CASE   :			Tuple   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				*__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )


 @property
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.tokenizer.model_input_names
      __SCREAMING_SNAKE_CASE      = self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )






