


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


snake_case			:      int 							=   get_logger(__name__)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""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  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					List[Any]        =						False  # to tell pytest it's not a test class





							@staticmethod
							def 		__magic_name__			(    A_	)->	int:
											_SCREAMING_SNAKE_CASE      = parser.add_parser('test'				, help='Test dataset implementation.'	)
											test_parser.add_argument('--name'				, type=A_				, default=A_				, help='Dataset processing name'	)
											test_parser.add_argument(
											    '--cache_dir'				, type=A_				, default=A_				, help='Cache directory where the datasets are stored.'				, )
											test_parser.add_argument(
											    '--data_dir'				, type=A_				, default=A_				, 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=A_				, help='Name of the dataset to download'	)
											test_parser.set_defaults(func=_test_command_factory	)





							def __init__(    self				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, )->	Any:
											_SCREAMING_SNAKE_CASE      = dataset
											_SCREAMING_SNAKE_CASE      = name
											_SCREAMING_SNAKE_CASE      = cache_dir
											_SCREAMING_SNAKE_CASE      = data_dir
											_SCREAMING_SNAKE_CASE      = all_configs
											_SCREAMING_SNAKE_CASE      = save_infos
											_SCREAMING_SNAKE_CASE      = ignore_verifications
											_SCREAMING_SNAKE_CASE      = force_redownload
											_SCREAMING_SNAKE_CASE      = 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:
															_SCREAMING_SNAKE_CASE      = True





							def 		__magic_name__			(    self	)->	Any:
											fl_logger().setLevel(A_	)
											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	)
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._dataset, self._name
											_SCREAMING_SNAKE_CASE      = dataset_module_factory(A_	)
											_SCREAMING_SNAKE_CASE      = import_main_class(module.module_path	)
											_SCREAMING_SNAKE_CASE      = 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=A_				, 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})'''	)
															_SCREAMING_SNAKE_CASE      = 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=A_				, )
															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:
																			_SCREAMING_SNAKE_CASE      = os.path.join(builder_cls.get_imported_module_dir()				, 'README.md'	)
																			_SCREAMING_SNAKE_CASE      = Path(A_	).name + '.py'
																			_SCREAMING_SNAKE_CASE      = os.path.join(A_				, A_	)
																			if os.path.isfile(A_	):
																							_SCREAMING_SNAKE_CASE      = os.path.dirname(A_	)
																			elif os.path.isfile(A_	):
																							_SCREAMING_SNAKE_CASE      = path
																			elif os.path.isdir(A_	):  # for local directories containing only data files
																							_SCREAMING_SNAKE_CASE      = path
																			else:  # in case of a remote dataset
																							_SCREAMING_SNAKE_CASE      = None
																							print(F'''Dataset card saved at {dataset_readme_path}'''	)

																			# Move dataset_info back to the user
																			if dataset_dir is not None:
																							_SCREAMING_SNAKE_CASE      = os.path.join(A_				, 'README.md'	)
																							copyfile(A_				, A_	)
																							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	)
																			_SCREAMING_SNAKE_CASE      = os.path.join(self._cache_dir				, datasets.config.DOWNLOADED_DATASETS_DIR	)
																			if os.path.isdir(A_	):
																							logger.warning(F'''Clearing cache at {download_dir}'''	)
																							rmtree(A_	)

											print('Test successful.'	)




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			:      Dict 							=   logging.get_logger(__name__)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				if "backbone.0.body" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('backbone.0.body'   ,'backbone.conv_encoder.model'							)
				if "transformer" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('transformer.'   ,''							)
				if "norm1" in orig_key:
								if "encoder" in orig_key:
												_SCREAMING_SNAKE_CASE      = orig_key.replace('norm1'   ,'self_attn_layer_norm'							)
								else:
												_SCREAMING_SNAKE_CASE      = orig_key.replace('norm1'   ,'encoder_attn_layer_norm'							)
				if "norm2" in orig_key:
								if "encoder" in orig_key:
												_SCREAMING_SNAKE_CASE      = orig_key.replace('norm2'   ,'final_layer_norm'							)
								else:
												_SCREAMING_SNAKE_CASE      = orig_key.replace('norm2'   ,'self_attn_layer_norm'							)
				if "norm3" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('norm3'   ,'final_layer_norm'							)
				if "linear1" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('linear1'   ,'fc1'							)
				if "linear2" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('linear2'   ,'fc2'							)
				if "query_embed" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('query_embed'   ,'query_position_embeddings'							)
				if "cross_attn" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('cross_attn'   ,'encoder_attn'							)

				return orig_key


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				for i in range(6							):
								# read in weights + bias of input projection layer of self-attention
								_SCREAMING_SNAKE_CASE      = state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''							)
								_SCREAMING_SNAKE_CASE      = 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      = in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE      = in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE      = in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE      = in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE      = in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE      = in_proj_bias[-256:]


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






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

				return im


@torch.no_grad()
def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = DeformableDetrConfig()
				# set config attributes
				if single_scale:
								_SCREAMING_SNAKE_CASE      = 1
				_SCREAMING_SNAKE_CASE      = dilation
				_SCREAMING_SNAKE_CASE      = with_box_refine
				_SCREAMING_SNAKE_CASE      = two_stage
				# set labels
				_SCREAMING_SNAKE_CASE      = 91
				_SCREAMING_SNAKE_CASE      = 'huggingface/label-files'
				_SCREAMING_SNAKE_CASE      = 'coco-detection-id2label.json'
				_SCREAMING_SNAKE_CASE      = json.load(open(cached_download(hf_hub_url(UpperCAmelCase__   ,UpperCAmelCase__   ,repo_type='dataset'							)							)   ,'r'							)							)
				_SCREAMING_SNAKE_CASE      = {int(UpperCAmelCase__							): v for k, v in idalabel.items()}
				_SCREAMING_SNAKE_CASE      = idalabel
				_SCREAMING_SNAKE_CASE      = {v: k for k, v in idalabel.items()}

				# load image processor
				_SCREAMING_SNAKE_CASE      = DeformableDetrImageProcessor(format='coco_detection'							)

				# prepare image
				_SCREAMING_SNAKE_CASE      = prepare_img()
				_SCREAMING_SNAKE_CASE      = image_processor(images=UpperCAmelCase__   ,return_tensors='pt'							)
				_SCREAMING_SNAKE_CASE      = encoding['pixel_values']

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

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

				_SCREAMING_SNAKE_CASE      = 'cuda' if torch.cuda.is_available() else 'cpu'
				model.to(UpperCAmelCase__							)
				# verify our conversion
				_SCREAMING_SNAKE_CASE      = model(pixel_values.to(UpperCAmelCase__							)							)

				_SCREAMING_SNAKE_CASE      = torch.tensor(
				    [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]							)
				_SCREAMING_SNAKE_CASE      = torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]							)

				if single_scale:
								_SCREAMING_SNAKE_CASE      = torch.tensor(
								    [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]							)
								_SCREAMING_SNAKE_CASE      = torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]							)

				if single_scale and dilation:
								_SCREAMING_SNAKE_CASE      = torch.tensor(
								    [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]							)
								_SCREAMING_SNAKE_CASE      = torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]							)

				if with_box_refine:
								_SCREAMING_SNAKE_CASE      = torch.tensor(
								    [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]							)
								_SCREAMING_SNAKE_CASE      = torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]							)

				if with_box_refine and two_stage:
								_SCREAMING_SNAKE_CASE      = torch.tensor(
								    [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]							)
								_SCREAMING_SNAKE_CASE      = torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]							)

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

				assert torch.allclose(outputs.logits[0, :3, :3]   ,expected_logits.to(UpperCAmelCase__							)   ,atol=1e-4							)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,expected_boxes.to(UpperCAmelCase__							)   ,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(UpperCAmelCase__							).mkdir(exist_ok=UpperCAmelCase__							)
				model.save_pretrained(UpperCAmelCase__							)
				image_processor.save_pretrained(UpperCAmelCase__							)

				# Push to hub
				if push_to_hub:
								_SCREAMING_SNAKE_CASE      = '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=UpperCAmelCase__   ,organization='nielsr'   ,commit_message='Add model'							)


if __name__ == "__main__":
							snake_case			:      Dict 							=   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			:      List[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,
							)
