




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()
UpperCamelCase__    			=			logging.get_logger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str     ):
					__lowerCAmelCase				   =       SwinConfig(
					    embed_dim=1_92      ,      depths=(2, 2, 18, 2)      ,      num_heads=(6, 12, 24, 48)      ,      window_size=12      ,      out_features=["stage2", "stage3", "stage4"]      ,      )

					__lowerCAmelCase				   =       DetaConfig(
					    backbone_config=SCREAMING_SNAKE_CASE_      ,      num_queries=9_00      ,      encoder_ffn_dim=20_48      ,      decoder_ffn_dim=20_48      ,      num_feature_levels=5      ,      assign_first_stage=SCREAMING_SNAKE_CASE_      ,      with_box_refine=SCREAMING_SNAKE_CASE_      ,      two_stage=SCREAMING_SNAKE_CASE_      ,      )

					# set labels
					__lowerCAmelCase				   =       "huggingface/label-files"
					if "o365" in model_name:
										__lowerCAmelCase				   =       3_66
										__lowerCAmelCase				   =       "object365-id2label.json"
					else:
										__lowerCAmelCase				   =       91
										__lowerCAmelCase				   =       "coco-detection-id2label.json"

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

					return config







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict     ):
					__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      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					__lowerCAmelCase				   =       dct.pop(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       val







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):
					__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      (						SCREAMING_SNAKE_CASE_							:				Optional[Any]      ,      SCREAMING_SNAKE_CASE_							:				str     ):
					# 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(SCREAMING_SNAKE_CASE_      ,      stream=SCREAMING_SNAKE_CASE_     ).raw     )

					return im







@torch.no_grad()
def 	_a      (						SCREAMING_SNAKE_CASE_							:				Any      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					__lowerCAmelCase				   =       get_deta_config(SCREAMING_SNAKE_CASE_     )

					# 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(SCREAMING_SNAKE_CASE_      ,      map_location="cpu"     )["model"]

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

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

					# 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(SCREAMING_SNAKE_CASE_     )
															__lowerCAmelCase				   =       val
										if "input_proj" in key:
															__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )
															__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(SCREAMING_SNAKE_CASE_     )
															__lowerCAmelCase				   =       val

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

					__lowerCAmelCase				   =       "cuda" if torch.cuda.is_available() else "cpu"
					model.to(SCREAMING_SNAKE_CASE_     )

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

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

					# 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(
										    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]     )
										__lowerCAmelCase				   =       torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]     )
					elif model_name == "deta-swin-large-o365":
										__lowerCAmelCase				   =       torch.tensor(
										    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]     )
										__lowerCAmelCase				   =       torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]     )
					assert torch.allclose(outputs.logits[0, :3, :3]      ,      expected_logits.to(SCREAMING_SNAKE_CASE_     )      ,      atol=1E-4     )
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,      expected_boxes.to(SCREAMING_SNAKE_CASE_     )      ,      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(SCREAMING_SNAKE_CASE_     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_     )
										model.save_pretrained(SCREAMING_SNAKE_CASE_     )
										processor.save_pretrained(SCREAMING_SNAKE_CASE_     )

					# 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__":
					UpperCamelCase__    			=			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."""
					)
					UpperCamelCase__    			=			parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""





import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__magic_name__       =			[]
for i in range(6):
							# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
							rename_keys.append(
							    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
							)
							rename_keys.append(
							    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
							)
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
							rename_keys.append(
							    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
							)
							rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
							# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
							)
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
							)
							rename_keys.append(
							    (
							        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
							        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
							    )
							)
							rename_keys.append(
							    (
							        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
							        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
							    )
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
							)
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.encoder.norm.weight", "encoder.layernorm.weight"),
        ("transformer.encoder.norm.bias", "encoder.layernorm.bias"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
    ]
)



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = state_dict.pop(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = val



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						__SCREAMING_SNAKE_CASE            = key.replace("""backbone.0.body"""  ,				"""backbone.conv_encoder.model"""				)
						__SCREAMING_SNAKE_CASE            = value
				else:
						__SCREAMING_SNAKE_CASE            = value

		return new_state_dict



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = """"""

		# first: transformer encoder
		for i in range(6				):
				# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
				__SCREAMING_SNAKE_CASE            = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"				)
				__SCREAMING_SNAKE_CASE            = state_dict.pop(f"{prefix}transformer.encoder.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:]
		# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
		for i in range(6				):
				# read in weights + bias of input projection layer of self-attention
				__SCREAMING_SNAKE_CASE            = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"				)
				__SCREAMING_SNAKE_CASE            = state_dict.pop(f"{prefix}transformer.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:]
				# read in weights + bias of input projection layer of cross-attention
				__SCREAMING_SNAKE_CASE            = state_dict.pop(
				    f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"				)
				__SCREAMING_SNAKE_CASE            = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"				)
				# next, add query, keys and values (in that order) of cross-attention to the state dict
				__SCREAMING_SNAKE_CASE            = in_proj_weight_cross_attn[:256, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias_cross_attn[:256]
				__SCREAMING_SNAKE_CASE            = in_proj_weight_cross_attn[256:512, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias_cross_attn[256:512]
				__SCREAMING_SNAKE_CASE            = in_proj_weight_cross_attn[-256:, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias_cross_attn[-256:]



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = image.size
		__SCREAMING_SNAKE_CASE            = max(UpperCamelCase_  ,				UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = 800 if """detection""" in checkpoint_url else 1000
		__SCREAMING_SNAKE_CASE            = target_max_size / current_max_size
		__SCREAMING_SNAKE_CASE            = image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

		return resized_image



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = F.to_tensor(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = F.normalize(UpperCamelCase_  ,				mean=[0.485, 0.456, 0.406]  ,				std=[0.229, 0.224, 0.225]				)
		return image



@torch.no_grad()
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		logger.info("""Converting model..."""				)

		# load original state dict
		__SCREAMING_SNAKE_CASE            = torch.hub.load_state_dict_from_url(UpperCamelCase_  ,				map_location="""cpu"""				)
		# rename keys
		for src, dest in rename_keys:
				rename_key(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = rename_backbone_keys(UpperCamelCase_				)
		# 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_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
						__SCREAMING_SNAKE_CASE            = state_dict.pop(UpperCamelCase_				)
						__SCREAMING_SNAKE_CASE            = val
    # create HuggingFace model and load state dict
		__SCREAMING_SNAKE_CASE            = TableTransformerConfig(
		    backbone="""resnet18"""  ,				mask_loss_coefficient=1  ,				dice_loss_coefficient=1  ,				ce_loss_coefficient=1  ,				bbox_loss_coefficient=5  ,				giou_loss_coefficient=2  ,				eos_coefficient=0.4  ,				class_cost=1  ,				bbox_cost=5  ,				giou_cost=2  ,				)

		if "detection" in checkpoint_url:
				__SCREAMING_SNAKE_CASE            = 15
				__SCREAMING_SNAKE_CASE            = 2
				__SCREAMING_SNAKE_CASE            = {0: """table""", 1: """table rotated"""}
				__SCREAMING_SNAKE_CASE            = idalabel
				__SCREAMING_SNAKE_CASE            = {v: k for k, v in idalabel.items()}
		else:
				__SCREAMING_SNAKE_CASE            = 125
				__SCREAMING_SNAKE_CASE            = 6
				__SCREAMING_SNAKE_CASE            = {
				    0: """table""",
				    1: """table column""",
				    2: """table row""",
				    3: """table column header""",
				    4: """table projected row header""",
				    5: """table spanning cell""",
				}
				__SCREAMING_SNAKE_CASE            = idalabel
				__SCREAMING_SNAKE_CASE            = {v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE            = DetrImageProcessor(
		    format="""coco_detection"""  ,				max_size=800 if """detection""" in checkpoint_url else 1000				)
		__SCREAMING_SNAKE_CASE            = TableTransformerForObjectDetection(UpperCamelCase_				)
		model.load_state_dict(UpperCamelCase_				)
		model.eval()

		# verify our conversion
		__SCREAMING_SNAKE_CASE            = """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
		__SCREAMING_SNAKE_CASE            = hf_hub_download(repo_id="""nielsr/example-pdf"""  ,				repo_type="""dataset"""  ,				filename=UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = Image.open(UpperCamelCase_				).convert("""RGB"""				)
		__SCREAMING_SNAKE_CASE            = normalize(resize(UpperCamelCase_  ,				UpperCamelCase_				)				).unsqueeze(0				)

		__SCREAMING_SNAKE_CASE            = model(UpperCamelCase_				)

		if "detection" in checkpoint_url:
				__SCREAMING_SNAKE_CASE            = (1, 15, 3)
				__SCREAMING_SNAKE_CASE            = torch.tensor(
				    [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]				)
				__SCREAMING_SNAKE_CASE            = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]				)

		else:
				__SCREAMING_SNAKE_CASE            = (1, 125, 7)
				__SCREAMING_SNAKE_CASE            = torch.tensor(
				    [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]				)
				__SCREAMING_SNAKE_CASE            = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]				)

		assert outputs.logits.shape == expected_shape
		assert torch.allclose(outputs.logits[0, :3, :3]  ,				UpperCamelCase_  ,				atol=1e-4				)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,				UpperCamelCase_  ,				atol=1e-4				)
		print("""Looks ok!"""				)

		if pytorch_dump_folder_path is not None:
				# 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_				)

		if push_to_hub:
				# Push model to HF hub
				logger.info("""Pushing model to the hub..."""				)
				__SCREAMING_SNAKE_CASE            = (
				    """microsoft/table-transformer-detection"""
				    if """detection""" in checkpoint_url
				    else """microsoft/table-transformer-structure-recognition"""
				)
				model.push_to_hub(UpperCamelCase_				)
				image_processor.push_to_hub(UpperCamelCase_				)


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

							parser.add_argument(
							    "--checkpoint_url",
							    default="https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
							    type=str,
							    choices=[
							        "https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
							        "https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth",
							    ],
							    help="URL of the Table Transformer checkpoint 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__       =			parser.parse_args()
							convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




