




import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


__A						:		Any          =						get_logger(__name__)





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				return TestCommand(
				    args.dataset						,			args.name						,			args.cache_dir						,			args.data_dir						,			args.all_configs						,			args.save_info or args.save_infos						,			args.ignore_verifications						,			args.force_redownload						,			args.clear_cache						,			)

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= False  # to tell pytest it's not a test class




					@staticmethod
					def     _lowercase						(   _lowercase:		ArgumentParser):


									'''simple docstring'''


									__lowerCAmelCase					=  parser.add_parser("""test""",     help="""Test dataset implementation.""")
									test_parser.add_argument("""--name""",     type=_lowercase,     default=_lowercase,     help="""Dataset processing name""")
									test_parser.add_argument(
									    """--cache_dir""",     type=_lowercase,     default=_lowercase,     help="""Cache directory where the datasets are stored.""",     )
									test_parser.add_argument(
									    """--data_dir""",     type=_lowercase,     default=_lowercase,     help="""Can be used to specify a manual directory to get the files from.""",     )
									test_parser.add_argument("""--all_configs""",     action="""store_true""",     help="""Test all dataset configurations""")
									test_parser.add_argument(
									    """--save_info""",     action="""store_true""",     help="""Save the dataset infos in the dataset card (README.md)""")
									test_parser.add_argument(
									    """--ignore_verifications""",     action="""store_true""",     help="""Run the test without checksums and splits checks.""",     )
									test_parser.add_argument("""--force_redownload""",     action="""store_true""",     help="""Force dataset redownload""")
									test_parser.add_argument(
									    """--clear_cache""",     action="""store_true""",     help="""Remove downloaded files and cached datasets after each config test""",     )
									# aliases
									test_parser.add_argument("""--save_infos""",     action="""store_true""",     help="""alias to save_info""")
									test_parser.add_argument("""dataset""",     type=_lowercase,     help="""Name of the dataset to download""")
									test_parser.set_defaults(func=_test_command_factory)




					def __init__(   self:		Optional[Any],     _lowercase:		str,     _lowercase:		str,     _lowercase:		str,     _lowercase:		str,     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		bool,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  dataset
									__lowerCAmelCase					=  name
									__lowerCAmelCase					=  cache_dir
									__lowerCAmelCase					=  data_dir
									__lowerCAmelCase					=  all_configs
									__lowerCAmelCase					=  save_infos
									__lowerCAmelCase					=  ignore_verifications
									__lowerCAmelCase					=  force_redownload
									__lowerCAmelCase					=  clear_cache
									if clear_cache and not cache_dir:
													print(
													    """When --clear_cache is used, specifying a cache directory is mandatory.\n"""
													    """The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"""
													    """Please provide a --cache_dir that will be used to test the dataset script.""")
													exit(1)
									if save_infos:
													__lowerCAmelCase					=  True







					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									fl_logger().setLevel(_lowercase)
									if self._name is not None and self._all_configs:
													print("""Both parameters `config` and `all_configs` can't be used at once.""")
													exit(1)
									__lowerCAmelCase	,				__lowerCAmelCase					=  self._dataset, self._name
									__lowerCAmelCase					=  dataset_module_factory(_lowercase)
									__lowerCAmelCase					=  import_main_class(module.module_path)
									__lowerCAmelCase					=  len(builder_cls.BUILDER_CONFIGS) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

									def get_builders() -> Generator[DatasetBuilder, None, None]:
													if self._all_configs and builder_cls.BUILDER_CONFIGS:
																	for i, config in enumerate(builder_cls.BUILDER_CONFIGS):
																					if "config_name" in module.builder_kwargs:
																									yield builder_cls(
																									    cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )
																					else:
																									yield builder_cls(
																									    config_name=config.name,     cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )
													else:
																	if "config_name" in module.builder_kwargs:
																					yield builder_cls(cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs)
																	else:
																					yield builder_cls(
																					    config_name=_lowercase,     cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )

									for j, builder in enumerate(get_builders()):
													print(f'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})''')
													__lowerCAmelCase					=  os.path.exists(
													    os.path.join(builder.get_imported_module_dir(),     datasets.config.DATASETDICT_INFOS_FILENAME))  # record checksums only if we need to update a (deprecated) dataset_infos.json
													builder.download_and_prepare(
													    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
													    if not self._force_redownload
													    else DownloadMode.FORCE_REDOWNLOAD,     verification_mode=VerificationMode.NO_CHECKS
													    if self._ignore_verifications
													    else VerificationMode.ALL_CHECKS,     try_from_hf_gcs=_lowercase,     )
													builder.as_dataset()
													if self._save_infos:
																	builder._save_infos()

													# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
													# The dataset_infos are saved in the YAML part of the README.md

													# 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:
																	__lowerCAmelCase					=  os.path.join(builder_cls.get_imported_module_dir(),     """README.md""")
																	__lowerCAmelCase					=  Path(_lowercase).name + """.py"""
																	__lowerCAmelCase					=  os.path.join(_lowercase,     _lowercase)
																	if os.path.isfile(_lowercase):
																					__lowerCAmelCase					=  os.path.dirname(_lowercase)
																	elif os.path.isfile(_lowercase):
																					__lowerCAmelCase					=  path
																	elif os.path.isdir(_lowercase):  # for local directories containing only data files
																					__lowerCAmelCase					=  path
																	else:  # in case of a remote dataset
																					__lowerCAmelCase					=  None
																					print(f'''Dataset card saved at {dataset_readme_path}''')

																	# Move dataset_info back to the user
																	if dataset_dir is not None:
																					__lowerCAmelCase					=  os.path.join(_lowercase,     """README.md""")
																					copyfile(_lowercase,     _lowercase)
																					print(f'''Dataset card saved at {user_dataset_readme_path}''')

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
													if self._clear_cache:
																	if os.path.isdir(builder._cache_dir):
																					logger.warning(f'''Clearing cache at {builder._cache_dir}''')
																					rmtree(builder._cache_dir)
																	__lowerCAmelCase					=  os.path.join(self._cache_dir,     datasets.config.DOWNLOADED_DATASETS_DIR)
																	if os.path.isdir(_lowercase):
																					logger.warning(f'''Clearing cache at {download_dir}''')
																					rmtree(_lowercase)

									print("""Test successful.""")









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


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

      return orig_key


def   _snake_case    (    _snake_case					:      List[str]      ):
      # transformer decoder self-attention layers
      for i in range(6      ):
            # read in weights + bias of input projection layer of self-attention
            lowerCAmelCase       :						str							= state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''      )
            lowerCAmelCase       :						str							= 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
            lowerCAmelCase       :						Tuple							= in_proj_weight[:256, :]
            lowerCAmelCase       :						Optional[int]							= in_proj_bias[:256]
            lowerCAmelCase       :						int							= in_proj_weight[256:512, :]
            lowerCAmelCase       :						Optional[int]							= in_proj_bias[256:512]
            lowerCAmelCase       :						Any							= in_proj_weight[-256:, :]
            lowerCAmelCase       :						str							= in_proj_bias[-256:]


def   _snake_case    (    ):
      lowerCAmelCase       :						List[str]							= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase       :						Dict							= Image.open(requests.get(_snake_case			, stream=_snake_case      ).raw      )

      return im


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

      # load image processor
      lowerCAmelCase       :						str							= DeformableDetrImageProcessor(format='''coco_detection'''      )

      # prepare image
      lowerCAmelCase       :						Tuple							= prepare_img()
      lowerCAmelCase       :						int							= image_processor(images=_snake_case			, return_tensors='''pt'''      )
      lowerCAmelCase       :						int							= encoding['''pixel_values''']

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

      # load original state dict
      lowerCAmelCase       :						Tuple							= torch.load(_snake_case			, map_location='''cpu'''      )['''model''']
      # rename keys
      for key in state_dict.copy().keys():
            lowerCAmelCase       :						Dict							= state_dict.pop(_snake_case      )
            lowerCAmelCase       :						Optional[Any]							= 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
      lowerCAmelCase       :						List[str]							= '''model.'''
      for key in state_dict.copy().keys():
            if not key.startswith('''class_embed'''      ) and not key.startswith('''bbox_embed'''      ):
                  lowerCAmelCase       :						Union[str, Any]							= state_dict.pop(_snake_case      )
                  lowerCAmelCase       :						str							= val
    # finally, create HuggingFace model and load state dict
      lowerCAmelCase       :						Any							= DeformableDetrForObjectDetection(_snake_case      )
      model.load_state_dict(_snake_case      )
      model.eval()

      lowerCAmelCase       :						List[str]							= '''cuda''' if torch.cuda.is_available() else '''cpu'''
      model.to(_snake_case      )
      # verify our conversion
      lowerCAmelCase       :						Dict							= model(pixel_values.to(_snake_case      )      )

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

      if single_scale:
            lowerCAmelCase       :						List[Any]							= torch.tensor(
                [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]      )
            lowerCAmelCase       :						int							= 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:
            lowerCAmelCase       :						str							= torch.tensor(
                [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]      )
            lowerCAmelCase       :						Tuple							= torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]      )

      if with_box_refine:
            lowerCAmelCase       :						Optional[Any]							= torch.tensor(
                [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]      )
            lowerCAmelCase       :						List[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:
            lowerCAmelCase       :						Dict							= torch.tensor(
                [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]      )
            lowerCAmelCase       :						Union[str, 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:
            lowerCAmelCase       :						Optional[Any]							= '''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__":
 snake_case__		:  Union[str, Any]     					=					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.'''
 )
 snake_case__		:  Union[str, Any]     					=					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,
 )


