
'''simple docstring'''

import argparse
import json
from pathlib import Path

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

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase												=   logging.get_logger(__name__)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[Any]							)       ->	Optional[Any]:
 if "backbone.0.body" in orig_key:
  SCREAMING_SNAKE_CASE  :  Dict								=		orig_key.replace('backbone.0.body'   ,     'backbone.conv_encoder.model'							)
 if "transformer" in orig_key:
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		orig_key.replace('transformer.'   ,     ''							)
 if "norm1" in orig_key:
  if "encoder" in orig_key:
   SCREAMING_SNAKE_CASE  :  List[str]								=		orig_key.replace('norm1'   ,     'self_attn_layer_norm'							)
  else:
   SCREAMING_SNAKE_CASE  :  Optional[int]								=		orig_key.replace('norm1'   ,     'encoder_attn_layer_norm'							)
 if "norm2" in orig_key:
  if "encoder" in orig_key:
   SCREAMING_SNAKE_CASE  :  int								=		orig_key.replace('norm2'   ,     'final_layer_norm'							)
  else:
   SCREAMING_SNAKE_CASE  :  Any								=		orig_key.replace('norm2'   ,     'self_attn_layer_norm'							)
 if "norm3" in orig_key:
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		orig_key.replace('norm3'   ,     'final_layer_norm'							)
 if "linear1" in orig_key:
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		orig_key.replace('linear1'   ,     'fc1'							)
 if "linear2" in orig_key:
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		orig_key.replace('linear2'   ,     'fc2'							)
 if "query_embed" in orig_key:
  SCREAMING_SNAKE_CASE  :  Dict								=		orig_key.replace('query_embed'   ,     'query_position_embeddings'							)
 if "cross_attn" in orig_key:
  SCREAMING_SNAKE_CASE  :  List[str]								=		orig_key.replace('cross_attn'   ,     'encoder_attn'							)

 return orig_key



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str							)       ->	List[str]:
 # transformer decoder self-attention layers
 for i in range(6							):
  # read in weights + bias of input projection layer of self-attention
  SCREAMING_SNAKE_CASE  :  List[Any]								=		state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""							)
  SCREAMING_SNAKE_CASE  :  Any								=		state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_bias"""							)
  # next, add query, keys and values (in that order) to the state dict
  SCREAMING_SNAKE_CASE  :  str								=		in_proj_weight[:256, :]
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		in_proj_bias[:256]
  SCREAMING_SNAKE_CASE  :  int								=		in_proj_weight[256:512, :]
  SCREAMING_SNAKE_CASE  :  str								=		in_proj_bias[256:512]
  SCREAMING_SNAKE_CASE  :  int								=		in_proj_weight[-256:, :]
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		in_proj_bias[-256:]



def        SCREAMING_SNAKE_CASE_  (	)       ->	int:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		'http://images.cocodataset.org/val2017/000000039769.jpg'
 SCREAMING_SNAKE_CASE  :  List[str]								=		Image.open(requests.get(snake_case_   ,     stream=snake_case_							).raw							)

 return im



@torch.no_grad()
def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Any   ,     snake_case_				:	Any   ,     snake_case_				:	List[Any]   ,     snake_case_				:	str   ,     snake_case_				:	Tuple   ,     snake_case_				:	Any   ,     snake_case_				:	int   ,     )       ->	Optional[int]:
 SCREAMING_SNAKE_CASE  :  Any								=		DeformableDetrConfig()
 # set config attributes
 if single_scale:
  SCREAMING_SNAKE_CASE  :  Tuple								=		1
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		dilation
 SCREAMING_SNAKE_CASE  :  List[Any]								=		with_box_refine
 SCREAMING_SNAKE_CASE  :  List[Any]								=		two_stage
 # set labels
 SCREAMING_SNAKE_CASE  :  Tuple								=		91
 SCREAMING_SNAKE_CASE  :  int								=		'huggingface/label-files'
 SCREAMING_SNAKE_CASE  :  Any								=		'coco-detection-id2label.json'
 SCREAMING_SNAKE_CASE  :  str								=		json.load(open(cached_download(hf_hub_url(snake_case_   ,     snake_case_   ,     repo_type='dataset'							)							)   ,     'r'							)							)
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		{int(snake_case_							): v for k, v in idalabel.items()}
 SCREAMING_SNAKE_CASE  :  List[str]								=		idalabel
 SCREAMING_SNAKE_CASE  :  Any								=		{v: k for k, v in idalabel.items()}

 # load image processor
 SCREAMING_SNAKE_CASE  :  Tuple								=		DeformableDetrImageProcessor(format='coco_detection'							)

 # prepare image
 SCREAMING_SNAKE_CASE  :  str								=		prepare_img()
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image_processor(images=snake_case_   ,     return_tensors='pt'							)
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		encoding['pixel_values']

 logger.info('Converting model...'							)

 # load original state dict
 SCREAMING_SNAKE_CASE  :  List[str]								=		torch.load(snake_case_   ,     map_location='cpu'							)['model']
 # rename keys
 for key in state_dict.copy().keys():
  SCREAMING_SNAKE_CASE  :  List[Any]								=		state_dict.pop(snake_case_							)
  SCREAMING_SNAKE_CASE  :  Dict								=		val
 # query, key and value matrices need special treatment
 read_in_q_k_v(snake_case_							)
 # 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  :  int								=		'model.'
 for key in state_dict.copy().keys():
  if not key.startswith('class_embed'							) and not key.startswith('bbox_embed'							):
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		state_dict.pop(snake_case_							)
   SCREAMING_SNAKE_CASE  :  Any								=		val
    # finally, create HuggingFace model and load state dict
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		DeformableDetrForObjectDetection(snake_case_							)
 model.load_state_dict(snake_case_							)
 model.eval()

 SCREAMING_SNAKE_CASE  :  Tuple								=		'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(snake_case_							)
 # verify our conversion
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model(pixel_values.to(snake_case_							)							)

 SCREAMING_SNAKE_CASE  :  Dict								=		torch.tensor(
     [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]							)
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]							)

 if single_scale:
  SCREAMING_SNAKE_CASE  :  List[str]								=		torch.tensor(
      [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]							)
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]							)

 if single_scale and dilation:
  SCREAMING_SNAKE_CASE  :  str								=		torch.tensor(
      [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]							)
  SCREAMING_SNAKE_CASE  :  List[str]								=		torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]							)

 if with_box_refine:
  SCREAMING_SNAKE_CASE  :  int								=		torch.tensor(
      [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]							)
  SCREAMING_SNAKE_CASE  :  str								=		torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]							)

 if with_box_refine and two_stage:
  SCREAMING_SNAKE_CASE  :  int								=		torch.tensor(
      [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]							)
  SCREAMING_SNAKE_CASE  :  Any								=		torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]							)

 print('Logits:'   ,     outputs.logits[0, :3, :3]							)

 assert torch.allclose(outputs.logits[0, :3, :3]   ,     expected_logits.to(snake_case_							)   ,     atol=1e-4							)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,     expected_boxes.to(snake_case_							)   ,     atol=1e-4							)

 print('Everything ok!'							)

 # Save model and image processor
 logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""							)
 Path(snake_case_							).mkdir(exist_ok=snake_case_							)
 model.save_pretrained(snake_case_							)
 image_processor.save_pretrained(snake_case_							)

 # Push to hub
 if push_to_hub:
  SCREAMING_SNAKE_CASE  :  List[str]								=		'deformable-detr'
  model_name += "-single-scale" if single_scale else ""
  model_name += "-dc5" if dilation else ""
  model_name += "-with-box-refine" if with_box_refine else ""
  model_name += "-two-stage" if two_stage else ""
  print('Pushing model to hub...'							)
  model.push_to_hub(repo_path_or_name=snake_case_   ,     organization='nielsr'   ,     commit_message='Add model'							)


if __name__ == "__main__":
   __UpperCAmelCase												=   argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_path',
       type=str,
       default='/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth',
       help='Path to Pytorch checkpoint (.pth file) you\'d like to convert.',
   )
   parser.add_argument('--single_scale', action='store_true', help='Whether to set config.num_features_levels = 1.')
   parser.add_argument('--dilation', action='store_true', help='Whether to set config.dilation=True.')
   parser.add_argument('--with_box_refine', action='store_true', help='Whether to set config.with_box_refine=True.')
   parser.add_argument('--two_stage', action='store_true', help='Whether to set config.two_stage=True.')
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=str,
       required=True,
       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.'
   )
   __UpperCAmelCase												=   parser.parse_args()
   convert_deformable_detr_checkpoint(
       args.checkpoint_path,
       args.single_scale,
       args.dilation,
       args.with_box_refine,
       args.two_stage,
       args.pytorch_dump_folder_path,
       args.push_to_hub,
   )




'''simple docstring'''

import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[str]   ,     **snake_case_				:	str							)       ->	Optional[int]:
 return RunBeamCommand(
     args.dataset   ,     args.name   ,     args.cache_dir   ,     args.beam_pipeline_options   ,     args.data_dir   ,     args.all_configs   ,     args.save_info or args.save_infos   ,     args.ignore_verifications   ,     args.force_redownload   ,     **snake_case_   ,     )





class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   @staticmethod
   def 					__a			(       __SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		parser.add_parser('run_beam'    ,help='Run a Beam dataset processing pipeline'      )
    run_beam_parser.add_argument('dataset'    ,type=__SCREAMING_SNAKE_CASE    ,help='Name of the dataset to download'      )
    run_beam_parser.add_argument('--name'    ,type=__SCREAMING_SNAKE_CASE    ,default=__SCREAMING_SNAKE_CASE    ,help='Dataset config name'      )
    run_beam_parser.add_argument(
        '--cache_dir'    ,type=__SCREAMING_SNAKE_CASE    ,default=__SCREAMING_SNAKE_CASE    ,help='Cache directory where the datasets are stored'    ,)
    run_beam_parser.add_argument(
        '--beam_pipeline_options'    ,type=__SCREAMING_SNAKE_CASE    ,default=''    ,help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'    ,)
    run_beam_parser.add_argument(
        '--data_dir'    ,type=__SCREAMING_SNAKE_CASE    ,default=__SCREAMING_SNAKE_CASE    ,help='Can be used to specify a manual directory to get the files from'    ,)
    run_beam_parser.add_argument('--all_configs'    ,action='store_true'    ,help='Test all dataset configurations'      )
    run_beam_parser.add_argument('--save_info'    ,action='store_true'    ,help='Save the dataset infos file'      )
    run_beam_parser.add_argument(
        '--ignore_verifications'    ,action='store_true'    ,help='Run the test without checksums and splits checks'      )
    run_beam_parser.add_argument('--force_redownload'    ,action='store_true'    ,help='Force dataset redownload'      )
    # aliases
    run_beam_parser.add_argument('--save_infos'    ,action='store_true'    ,help='alias for save_info'      )
    run_beam_parser.set_defaults(func=__SCREAMING_SNAKE_CASE      )






   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		dataset
    SCREAMING_SNAKE_CASE  :  List[Any]								=		name
    SCREAMING_SNAKE_CASE  :  int								=		cache_dir
    SCREAMING_SNAKE_CASE  :  str								=		beam_pipeline_options
    SCREAMING_SNAKE_CASE  :  List[str]								=		data_dir
    SCREAMING_SNAKE_CASE  :  int								=		all_configs
    SCREAMING_SNAKE_CASE  :  Any								=		save_infos
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		ignore_verifications
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		force_redownload
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		config_kwargs






   def 					__a			(       self      ):
    import apache_beam as beam

    if self._name is not None and self._all_configs:
     print('Both parameters `name` and `all_configs` can\'t be used at once.'      )
     exit(1      )
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		self._dataset, self._name
    SCREAMING_SNAKE_CASE  :  str								=		dataset_module_factory(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  str								=		import_main_class(dataset_module.module_path      )
    SCREAMING_SNAKE_CASE  :  List[DatasetBuilder]								=		[]
    if self._beam_pipeline_options:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		beam.options.pipeline_options.PipelineOptions(
         flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','      ) if opt]      )
    else:
     SCREAMING_SNAKE_CASE  :  List[Any]								=		None
    if self._all_configs and len(builder_cls.BUILDER_CONFIGS      ) > 0:
     for builder_config in builder_cls.BUILDER_CONFIGS:
      builders.append(
          builder_cls(
              config_name=builder_config.name    ,data_dir=self._data_dir    ,hash=dataset_module.hash    ,beam_options=__SCREAMING_SNAKE_CASE    ,cache_dir=self._cache_dir    ,base_path=dataset_module.builder_kwargs.get('base_path'      )    ,)      )
    else:
     builders.append(
         builder_cls(
             config_name=__SCREAMING_SNAKE_CASE    ,data_dir=self._data_dir    ,beam_options=__SCREAMING_SNAKE_CASE    ,cache_dir=self._cache_dir    ,base_path=dataset_module.builder_kwargs.get('base_path'      )    ,**self._config_kwargs    ,)      )

    for builder in builders:
     builder.download_and_prepare(
         download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
         if not self._force_redownload
         else DownloadMode.FORCE_REDOWNLOAD    ,download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH      )    ,verification_mode=VerificationMode.NO_CHECKS
         if self._ignore_verifications
         else VerificationMode.ALL_CHECKS    ,try_from_hf_gcs=__SCREAMING_SNAKE_CASE    ,)
     if self._save_infos:
      builder._save_infos()

    print('Apache beam run successful.'      )

    # If save_infos=True, the dataset infos file is created next to the loaded module file.
    # Let's move it to the original directory of the dataset script, to allow the user to
    # upload them on S3 at the same time afterwards.
    if self._save_infos:
     SCREAMING_SNAKE_CASE  :  Dict								=		os.path.join(builder_cls.get_imported_module_dir()    ,config.DATASETDICT_INFOS_FILENAME      )

     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		Path(__SCREAMING_SNAKE_CASE      ).name + '.py'

     SCREAMING_SNAKE_CASE  :  Dict								=		os.path.join(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
     if os.path.isfile(__SCREAMING_SNAKE_CASE      ):
      SCREAMING_SNAKE_CASE  :  Tuple								=		os.path.dirname(__SCREAMING_SNAKE_CASE      )
     elif os.path.isfile(__SCREAMING_SNAKE_CASE      ):
      SCREAMING_SNAKE_CASE  :  Optional[Any]								=		path
     else:  # in case of a remote dataset
      print(f"""Dataset Infos file saved at {dataset_infos_path}"""      )
      exit(1      )

     # Move datasetinfo back to the user
     SCREAMING_SNAKE_CASE  :  List[Any]								=		os.path.join(__SCREAMING_SNAKE_CASE    ,config.DATASETDICT_INFOS_FILENAME      )
     copyfile(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
     print(f"""Dataset Infos file saved at {user_dataset_infos_path}"""      )


