


"""simple docstring"""




import argparse
from collections import defaultdict





def       a_ (	lowercase__ :Optional[Any],			lowercase__ :int,			lowercase__ :Union[str, Any],			lowercase__ :str,			lowercase__ :Dict     ):
			__lowerCamelCase          =      f'{file}_{class_name}_{test_name}'
			done_test[_id] += 1

			with open(lowercase__,			"""r"""     ) as f:
						__lowerCamelCase          =      f.readlines()

			__lowerCamelCase          =      f'class {class_name}('
			__lowerCamelCase          =      f'{4 * " "}def {test_name}('
			__lowerCamelCase          =      f'{8 * " "}{correct_line.split()[0]}'
			__lowerCamelCase          =      f'{16 * " "}{correct_line.split()[0]}'
			__lowerCamelCase          =      False
			__lowerCamelCase          =      False
			__lowerCamelCase          =      False
			__lowerCamelCase          =      False
			__lowerCamelCase          =      0
			__lowerCamelCase          =      0

			__lowerCamelCase          =      []
			for line in lines:
						if line.startswith(lowercase__     ):
									__lowerCamelCase          =      True
						elif in_class and line.startswith(lowercase__     ):
									__lowerCamelCase          =      True
						elif in_class and in_func and (line.startswith(lowercase__     ) or line.startswith(lowercase__     )):
									__lowerCamelCase          =      len(line.split(correct_line.split()[0]     )[0]     )
									count += 1

									if count == done_test[_id]:
												__lowerCamelCase          =      True

						if in_class and in_func and in_line:
									if ")" not in line:
												continue
									else:
												__lowerCamelCase          =      True

						if in_class and in_func and in_line and insert_line:
									new_lines.append(f'{spaces * " "}{correct_line}'     )
									__lowerCamelCase          =      __lowerCamelCase          =      __lowerCamelCase          =      __lowerCamelCase          =      False
						else:
									new_lines.append(lowercase__     )

			with open(lowercase__,			"""w"""     ) as f:
						for line in new_lines:
									f.write(lowercase__     )





def       a_ (	lowercase__ :int,			lowercase__ :List[str]=None     ):
			if fail is not None:
						with open(lowercase__,			"""r"""     ) as f:
									__lowerCamelCase          =      {l.strip() for l in f.readlines()}
			else:
						__lowerCamelCase          =      None

			with open(lowercase__,			"""r"""     ) as f:
						__lowerCamelCase          =      f.readlines()

			__lowerCamelCase          =      defaultdict(lowercase__     )
			for line in correct_lines:
						__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase          =      line.split(""";"""     )
						if test_failures is None or "::".join([file, class_name, test_name]     ) in test_failures:
									overwrite_file(lowercase__,			lowercase__,			lowercase__,			lowercase__,			lowercase__     )


if __name__ == "__main__":
					__magic_name__						: str    =				argparse.ArgumentParser()
					parser.add_argument('--correct_filename', help='filename of tests with expected result')
					parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
					__magic_name__						: Dict    =				parser.parse_args()

					main(args.correct_filename, args.fail_filename)








"""simple docstring"""




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, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__						: Optional[Any]    =				logging.get_logger(__name__)





def       a_ (	lowercase__ :List[str]     ):
			__lowerCamelCase          =      SwinConfig(
			    embed_dim=192,			depths=(2, 2, 18, 2),			num_heads=(6, 12, 24, 48),			window_size=12,			out_features=["""stage2""", """stage3""", """stage4"""],			)

			__lowerCamelCase          =      DetaConfig(
			    backbone_config=lowercase__,			num_queries=900,			encoder_ffn_dim=2048,			decoder_ffn_dim=2048,			num_feature_levels=5,			assign_first_stage=lowercase__,			with_box_refine=lowercase__,			two_stage=lowercase__,			)

			# set labels
			__lowerCamelCase          =      """huggingface/label-files"""
			if "o365" in model_name:
						__lowerCamelCase          =      366
						__lowerCamelCase          =      """object365-id2label.json"""
			else:
						__lowerCamelCase          =      91
						__lowerCamelCase          =      """coco-detection-id2label.json"""

			__lowerCamelCase          =      num_labels
			__lowerCamelCase          =      json.load(open(cached_download(hf_hub_url(lowercase__,			lowercase__,			repo_type="""dataset"""     )     ),			"""r"""     )     )
			__lowerCamelCase          =      {int(lowercase__     ): v for k, v in idalabel.items()}
			__lowerCamelCase          =      idalabel
			__lowerCamelCase          =      {v: k for k, v in idalabel.items()}

			return config





def       a_ (	lowercase__ :List[str]     ):
			__lowerCamelCase          =      []

			# stem
			# fmt: off
			rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")     )
			# stages
			for i in range(len(config.backbone_config.depths     )     ):
						for j in range(config.backbone_config.depths[i]     ):
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')     )

						if i < 3:
									rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')     )
									rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')     )

			rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")     )
			rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")     )
			rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")     )
			rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")     )
			rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")     )
			rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")     )

			# 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_ (	lowercase__ :Optional[Any],			lowercase__ :str,			lowercase__ :Optional[int]     ):
			__lowerCamelCase          =      dct.pop(lowercase__     )
			__lowerCamelCase          =      val





def       a_ (	lowercase__ :List[Any],			lowercase__ :Dict     ):
			__lowerCamelCase          =      [int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
			for i in range(len(backbone_config.depths     )     ):
						__lowerCamelCase          =      num_features[i]
						for j in range(backbone_config.depths[i]     ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									__lowerCamelCase          =      state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'     )
									__lowerCamelCase          =      state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'     )
									# next, add query, keys and values (in that order) to the state dict
									__lowerCamelCase          =      in_proj_weight[:dim, :]
									__lowerCamelCase          =      in_proj_bias[: dim]
									__lowerCamelCase          =      in_proj_weight[
									    dim : dim * 2, :
									]
									__lowerCamelCase          =      in_proj_bias[
									    dim : dim * 2
									]
									__lowerCamelCase          =      in_proj_weight[
									    -dim :, :
									]
									__lowerCamelCase          =      in_proj_bias[-dim :]
									# fmt: on





def       a_ (	lowercase__ :int,			lowercase__ :Optional[int]     ):
			# transformer decoder self-attention layers
			__lowerCamelCase          =      config.d_model
			for i in range(config.decoder_layers     ):
						# read in weights + bias of input projection layer of self-attention
						__lowerCamelCase          =      state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'     )
						__lowerCamelCase          =      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
						__lowerCamelCase          =      in_proj_weight[:hidden_size, :]
						__lowerCamelCase          =      in_proj_bias[:hidden_size]
						__lowerCamelCase          =      in_proj_weight[
						    hidden_size : hidden_size * 2, :
						]
						__lowerCamelCase          =      in_proj_bias[hidden_size : hidden_size * 2]
						__lowerCamelCase          =      in_proj_weight[-hidden_size:, :]
						__lowerCamelCase          =      in_proj_bias[-hidden_size:]





def       a_ (	):
			__lowerCamelCase          =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__lowerCamelCase          =      Image.open(requests.get(lowercase__,			stream=lowercase__     ).raw     )

			return im





@torch.no_grad()
def       a_ (	lowercase__ :Optional[int],			lowercase__ :Any,			lowercase__ :Dict     ):
			__lowerCamelCase          =      get_deta_config(lowercase__     )

			# load original state dict
			if model_name == "deta-swin-large":
						__lowerCamelCase          =      hf_hub_download(repo_id="""nielsr/deta-checkpoints""",			filename="""adet_swin_ft.pth"""     )
			elif model_name == "deta-swin-large-o365":
						__lowerCamelCase          =      hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365""",			filename="""deta_swin_pt_o365.pth"""     )
			else:
						raise ValueError(f'Model name {model_name} not supported'     )

			__lowerCamelCase          =      torch.load(lowercase__,			map_location="""cpu"""     )["""model"""]

			# original state dict
			for name, param in state_dict.items():
						print(lowercase__,			param.shape     )

			# rename keys
			__lowerCamelCase          =      create_rename_keys(lowercase__     )
			for src, dest in rename_keys:
						rename_key(lowercase__,			lowercase__,			lowercase__     )
			read_in_swin_q_k_v(lowercase__,			config.backbone_config     )
			read_in_decoder_q_k_v(lowercase__,			lowercase__     )

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

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

			__lowerCamelCase          =      """cuda""" if torch.cuda.is_available() else """cpu"""
			model.to(lowercase__     )

			# load image processor
			__lowerCamelCase          =      DetaImageProcessor(format="""coco_detection"""     )

			# verify our conversion on image
			__lowerCamelCase          =      prepare_img()
			__lowerCamelCase          =      processor(images=lowercase__,			return_tensors="""pt"""     )
			__lowerCamelCase          =      encoding["""pixel_values"""]
			__lowerCamelCase          =      model(pixel_values.to(lowercase__     )     )

			# verify logits
			print("""Logits:""",			outputs.logits[0, :3, :3]     )
			print("""Boxes:""",			outputs.pred_boxes[0, :3, :3]     )
			if model_name == "deta-swin-large":
						__lowerCamelCase          =      torch.tensor(
						    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]     )
						__lowerCamelCase          =      torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]     )
			elif model_name == "deta-swin-large-o365":
						__lowerCamelCase          =      torch.tensor(
						    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]     )
						__lowerCamelCase          =      torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]     )
			assert torch.allclose(outputs.logits[0, :3, :3],			expected_logits.to(lowercase__     ),			atol=1e-4     )
			assert torch.allclose(outputs.pred_boxes[0, :3, :3],			expected_boxes.to(lowercase__     ),			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(lowercase__     ).mkdir(exist_ok=lowercase__     )
						model.save_pretrained(lowercase__     )
						processor.save_pretrained(lowercase__     )

			# 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__":
					__magic_name__						: Union[str, Any]    =				argparse.ArgumentParser()

					parser.add_argument(
					    '--model_name',
					    type=str,
					    default='deta-swin-large',
					    choices=['deta-swin-large', 'deta-swin-large-o365'],
					    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.'
					)
					__magic_name__						: str    =				parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




