





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__   :				List[str]						=	logging.get_logger(__name__)



def _a							(      )      ->		Any:







      '''simple docstring'''






      __A			  =							DetaConfig(
          num_queries=9_00   ,							encoder_ffn_dim=20_48   ,							decoder_ffn_dim=20_48   ,							num_feature_levels=5   ,							assign_first_stage=lowerCamelCase   ,							with_box_refine=lowerCamelCase   ,							two_stage=lowerCamelCase   ,							)

      # set labels
      __A			  =							91
      __A			  =							'''huggingface/label-files'''
      __A			  =							'''coco-detection-id2label.json'''
      __A			  =							json.load(open(cached_download(hf_hub_url(lowerCamelCase   ,							lowerCamelCase   ,							repo_type='''dataset'''					)					)   ,							'''r'''					)					)
      __A			  =							{int(lowerCamelCase					): v for k, v in idalabel.items()}
      __A			  =							idalabel
      __A			  =							{v: k for k, v in idalabel.items()}

      return config



def _a							(      lowerCamelCase:  List[Any]					)      ->		Union[str, Any]:







      '''simple docstring'''






      __A			  =							[]

      # stem
      # fmt: off
      rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''')					)
      rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''')					)
      rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''')					)
      rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''')					)
      rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                            )					)
    # transformer encoder
      for i in range(config.encoder_layers					):
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", F"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", F"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", F"""model.encoder.layers.{i}.self_attn.value_proj.weight""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", F"""model.encoder.layers.{i}.self_attn.value_proj.bias""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", F"""model.encoder.layers.{i}.self_attn.output_proj.weight""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", F"""model.encoder.layers.{i}.self_attn.output_proj.bias""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.weight""", F"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""model.encoder.layers.{i}.fc1.weight""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""model.encoder.layers.{i}.fc1.bias""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""model.encoder.layers.{i}.fc2.weight""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""model.encoder.layers.{i}.fc2.bias""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""model.encoder.layers.{i}.final_layer_norm.weight""")					)
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""model.encoder.layers.{i}.final_layer_norm.bias""")					)

      # transformer decoder
      for i in range(config.decoder_layers					):
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.weight""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""model.decoder.layers.{i}.self_attn.out_proj.weight""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""model.decoder.layers.{i}.self_attn.out_proj.bias""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.weight""", F"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.bias""", F"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""model.decoder.layers.{i}.fc1.weight""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""model.decoder.layers.{i}.fc1.bias""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""model.decoder.layers.{i}.fc2.weight""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""model.decoder.layers.{i}.fc2.bias""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""model.decoder.layers.{i}.final_layer_norm.weight""")					)
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""model.decoder.layers.{i}.final_layer_norm.bias""")					)

      # fmt: on

      return rename_keys



def _a							(      lowerCamelCase:  List[str]   ,							lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  Any					)      ->		List[Any]:







      '''simple docstring'''






      __A			  =							dct.pop(lowerCamelCase					)
      __A			  =							val



def _a							(      lowerCamelCase:  Tuple   ,							lowerCamelCase:  int					)      ->		str:







      '''simple docstring'''






      __A			  =							config.d_model
      for i in range(config.decoder_layers					):
            # read in weights + bias of input projection layer of self-attention
            __A			  =							state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
            __A			  =							state_dict.pop(F"""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[:hidden_size, :]
            __A			  =							in_proj_bias[:hidden_size]
            __A			  =							in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            __A			  =							in_proj_bias[hidden_size : hidden_size * 2]
            __A			  =							in_proj_weight[-hidden_size:, :]
            __A			  =							in_proj_bias[-hidden_size:]



def _a							(      )      ->		str:







      '''simple docstring'''






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

      return im



@torch.no_grad()
def _a							(      lowerCamelCase:  str   ,							lowerCamelCase:  Optional[int]   ,							lowerCamelCase:  Tuple					)      ->		List[Any]:







      '''simple docstring'''






      __A			  =							get_deta_config()

      # load original state dict
      if model_name == "deta-resnet-50":
            __A			  =							'''adet_checkpoint0011.pth'''
      elif model_name == "deta-resnet-50-24-epochs":
            __A			  =							'''adet_2x_checkpoint0023.pth'''
      else:
            raise ValueError(F"""Model name {model_name} not supported"""					)
      __A			  =							hf_hub_download(repo_id='''nielsr/deta-checkpoints'''   ,							filename=lowerCamelCase					)
      __A			  =							torch.load(lowerCamelCase   ,							map_location='''cpu'''					)['''model''']

      # rename keys
      __A			  =							create_rename_keys(lowerCamelCase					)
      for src, dest in rename_keys:
            rename_key(lowerCamelCase   ,							lowerCamelCase   ,							lowerCamelCase					)
      read_in_decoder_q_k_v(lowerCamelCase   ,							lowerCamelCase					)

      # fix some prefixes
      for key in state_dict.copy().keys():
            if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                  __A			  =							state_dict.pop(lowerCamelCase					)
                  __A			  =							val
            if "input_proj" in key:
                  __A			  =							state_dict.pop(lowerCamelCase					)
                  __A			  =							val
            if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                  __A			  =							state_dict.pop(lowerCamelCase					)
                  __A			  =							val

    # finally, create HuggingFace model and load state dict
      __A			  =							DetaForObjectDetection(lowerCamelCase					)
      model.load_state_dict(lowerCamelCase					)
      model.eval()

      __A			  =							'''cuda''' if torch.cuda.is_available() else '''cpu'''
      model.to(lowerCamelCase					)

      # load image processor
      __A			  =							DetaImageProcessor(format='''coco_detection'''					)

      # verify our conversion on image
      __A			  =							prepare_img()
      __A			  =							processor(images=lowerCamelCase   ,							return_tensors='''pt'''					)
      __A			  =							encoding['''pixel_values''']
      __A			  =							model(pixel_values.to(lowerCamelCase					)					)

      # verify logits
      if model_name == "deta-resnet-50":
            __A			  =							torch.tensor(
                [[-7.3_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]					)
            __A			  =							torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]					)
      elif model_name == "deta-resnet-50-24-epochs":
            __A			  =							torch.tensor(
                [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]					)
            __A			  =							torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]					)

      assert torch.allclose(outputs.logits[0, :3, :3]   ,							expected_logits.to(lowerCamelCase					)   ,							atol=1e-4					)
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,							expected_boxes.to(lowerCamelCase					)   ,							atol=1e-4					)
      print('''Everything ok!'''					)

      if pytorch_dump_folder_path:
            # Save model and processor
            logger.info(F"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""					)
            Path(lowerCamelCase					).mkdir(exist_ok=lowerCamelCase					)
            model.save_pretrained(lowerCamelCase					)
            processor.save_pretrained(lowerCamelCase					)

      # Push to hub
      if push_to_hub:
            print('''Pushing model and processor to hub...'''					)
            model.push_to_hub(F"""jozhang97/{model_name}"""					)
            processor.push_to_hub(F"""jozhang97/{model_name}"""					)


if __name__ == "__main__":
       snake_case__   :				Optional[int]						=	argparse.ArgumentParser()

       parser.add_argument(
           '--model_name',
           type=str,
           default='deta-resnet-50',
           choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
           help='Name of the 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 or not to push the converted model to the 🤗 hub.'
       )
       snake_case__   :				Dict						=	parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case__   :				Tuple						=	{
    'configuration_realm': ['REALM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RealmConfig'],
    'tokenization_realm': ['RealmTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Tuple						=	['RealmTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				str						=	[
           'REALM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RealmEmbedder',
           'RealmForOpenQA',
           'RealmKnowledgeAugEncoder',
           'RealmPreTrainedModel',
           'RealmReader',
           'RealmScorer',
           'load_tf_weights_in_realm',
       ]
       snake_case__   :				Any						=	['RealmRetriever']


if TYPE_CHECKING:
       from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
       from .tokenization_realm import RealmTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_realm import RealmTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_realm import (
                  REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RealmEmbedder,
                  RealmForOpenQA,
                  RealmKnowledgeAugEncoder,
                  RealmPreTrainedModel,
                  RealmReader,
                  RealmScorer,
                  load_tf_weights_in_realm,
              )
              from .retrieval_realm import RealmRetriever


else:
       import sys

       snake_case__   :				Any						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





