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()
A__     :   Any           =							logging.get_logger(__name__)





def  _lowerCamelCase					(   a_				: Union[str, Any]):
			lowerCamelCase						:Union[str, Any]						  =     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						:Dict						  =     DetaConfig(
			    backbone_config=a_						,			num_queries=9_00						,			encoder_ffn_dim=20_48						,			decoder_ffn_dim=20_48						,			num_feature_levels=5						,			assign_first_stage=a_						,			with_box_refine=a_						,			two_stage=a_						,			)

			# set labels
			lowerCamelCase						:Dict						  =     '''huggingface/label-files'''
			if "o365" in model_name:
						lowerCamelCase						:int						  =     3_66
						lowerCamelCase						:str						  =     '''object365-id2label.json'''
			else:
						lowerCamelCase						:List[Any]						  =     91
						lowerCamelCase						:int						  =     '''coco-detection-id2label.json'''

			lowerCamelCase						:List[Any]						  =     num_labels
			lowerCamelCase						:Union[str, Any]						  =     json.load(open(cached_download(hf_hub_url(a_						,			a_						,			repo_type='''dataset'''))						,			'''r'''))
			lowerCamelCase						:List[Any]						  =     {int(a_): v for k, v in idalabel.items()}
			lowerCamelCase						:Dict						  =     idalabel
			lowerCamelCase						:List[str]						  =     {v: k for k, v in idalabel.items()}

			return config





def  _lowerCamelCase					(   a_				: Optional[Any]):
			lowerCamelCase						:Tuple						  =     []

			# 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  _lowerCamelCase					(   a_				: Any						,			a_				: Tuple						,			a_				: List[Any]):
			lowerCamelCase						:List[Any]						  =     dct.pop(a_)
			lowerCamelCase						:Tuple						  =     val





def  _lowerCamelCase					(   a_				: Dict						,			a_				: Optional[Any]):
			lowerCamelCase						:Tuple						  =     [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
			for i in range(len(backbone_config.depths)):
						lowerCamelCase						:Optional[int]						  =     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						:List[str]						  =     state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight")
									lowerCamelCase						:Dict						  =     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						:Tuple						  =     in_proj_weight[:dim, :]
									lowerCamelCase						:List[str]						  =     in_proj_bias[: dim]
									lowerCamelCase						:Optional[Any]						  =     in_proj_weight[
									    dim : dim * 2, :
									]
									lowerCamelCase						:Optional[int]						  =     in_proj_bias[
									    dim : dim * 2
									]
									lowerCamelCase						:Dict						  =     in_proj_weight[
									    -dim :, :
									]
									lowerCamelCase						:Union[str, Any]						  =     in_proj_bias[-dim :]
									# fmt: on





def  _lowerCamelCase					(   a_				: int						,			a_				: Optional[Any]):
			# transformer decoder self-attention layers
			lowerCamelCase						:Union[str, Any]						  =     config.d_model
			for i in range(config.decoder_layers):
						# read in weights + bias of input projection layer of self-attention
						lowerCamelCase						:Union[str, Any]						  =     state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight")
						lowerCamelCase						:Any						  =     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						:Dict						  =     in_proj_weight[:hidden_size, :]
						lowerCamelCase						:Any						  =     in_proj_bias[:hidden_size]
						lowerCamelCase						:Any						  =     in_proj_weight[
						    hidden_size : hidden_size * 2, :
						]
						lowerCamelCase						:Any						  =     in_proj_bias[hidden_size : hidden_size * 2]
						lowerCamelCase						:Optional[int]						  =     in_proj_weight[-hidden_size:, :]
						lowerCamelCase						:Union[str, Any]						  =     in_proj_bias[-hidden_size:]





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Any						  =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase						:List[Any]						  =     Image.open(requests.get(a_						,			stream=a_).raw)

			return im





@torch.no_grad()
def  _lowerCamelCase					(   a_				: Tuple						,			a_				: Tuple						,			a_				: Dict):
			lowerCamelCase						:Tuple						  =     get_deta_config(a_)

			# load original state dict
			if model_name == "deta-swin-large":
						lowerCamelCase						:Optional[Any]						  =     hf_hub_download(repo_id='''nielsr/deta-checkpoints'''						,			filename='''adet_swin_ft.pth''')
			elif model_name == "deta-swin-large-o365":
						lowerCamelCase						:Optional[Any]						  =     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						:int						  =     torch.load(a_						,			map_location='''cpu''')['''model''']

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

			# rename keys
			lowerCamelCase						:Union[str, Any]						  =     create_rename_keys(a_)
			for src, dest in rename_keys:
						rename_key(a_						,			a_						,			a_)
			read_in_swin_q_k_v(a_						,			config.backbone_config)
			read_in_decoder_q_k_v(a_						,			a_)

			# 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						:List[str]						  =     state_dict.pop(a_)
									lowerCamelCase						:List[str]						  =     val
						if "input_proj" in key:
									lowerCamelCase						:Optional[Any]						  =     state_dict.pop(a_)
									lowerCamelCase						:str						  =     val
						if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
									lowerCamelCase						:Dict						  =     state_dict.pop(a_)
									lowerCamelCase						:str						  =     val

    # finally, create HuggingFace model and load state dict
			lowerCamelCase						:Tuple						  =     DetaForObjectDetection(a_)
			model.load_state_dict(a_)
			model.eval()

			lowerCamelCase						:Any						  =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
			model.to(a_)

			# load image processor
			lowerCamelCase						:Tuple						  =     DetaImageProcessor(format='''coco_detection''')

			# verify our conversion on image
			lowerCamelCase						:int						  =     prepare_img()
			lowerCamelCase						:List[str]						  =     processor(images=a_						,			return_tensors='''pt''')
			lowerCamelCase						:int						  =     encoding['''pixel_values''']
			lowerCamelCase						:List[str]						  =     model(pixel_values.to(a_))

			# verify logits
			print('''Logits:'''						,			outputs.logits[0, :3, :3])
			print('''Boxes:'''						,			outputs.pred_boxes[0, :3, :3])
			if model_name == "deta-swin-large":
						lowerCamelCase						:str						  =     torch.tensor(
						    [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]])
						lowerCamelCase						:Optional[int]						  =     torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]])
			elif model_name == "deta-swin-large-o365":
						lowerCamelCase						:Optional[int]						  =     torch.tensor(
						    [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]])
						lowerCamelCase						:List[Any]						  =     torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]])
			assert torch.allclose(outputs.logits[0, :3, :3]						,			expected_logits.to(a_)						,			atol=1e-4)
			assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,			expected_boxes.to(a_)						,			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(a_).mkdir(exist_ok=a_)
						model.save_pretrained(a_)
						processor.save_pretrained(a_)

			# 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__":
			A__     :   Optional[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."""
			)
			A__     :   int           =							parser.parse_args()
			convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__           =							logging.get_logger(__name__)

A__           =							{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'roberta-prelayernorm'







							def __init__(				self     :    str	,					__snake_case     :    List[str]=50265	,					__snake_case     :    Union[str, Any]=768	,					__snake_case     :    Tuple=12	,					__snake_case     :    int=12	,					__snake_case     :    Any=3072	,					__snake_case     :    Optional[int]="gelu"	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    int=0.1	,					__snake_case     :    Union[str, Any]=512	,					__snake_case     :    Dict=2	,					__snake_case     :    int=0.0_2	,					__snake_case     :    Any=1e-1_2	,					__snake_case     :    Optional[int]=1	,					__snake_case     :    Dict=0	,					__snake_case     :    Optional[int]=2	,					__snake_case     :    Any="absolute"	,					__snake_case     :    Union[str, Any]=True	,					__snake_case     :    List[str]=None	,					**__snake_case     :    Optional[int]	,					):
										super().__init__(pad_token_id=__snake_case	,					bos_token_id=__snake_case	,					eos_token_id=__snake_case	,					**__snake_case		)

										lowerCamelCase						:Optional[int]						  =     vocab_size
										lowerCamelCase						:Dict						  =     hidden_size
										lowerCamelCase						:Tuple						  =     num_hidden_layers
										lowerCamelCase						:Optional[int]						  =     num_attention_heads
										lowerCamelCase						:Any						  =     hidden_act
										lowerCamelCase						:List[Any]						  =     intermediate_size
										lowerCamelCase						:Union[str, Any]						  =     hidden_dropout_prob
										lowerCamelCase						:str						  =     attention_probs_dropout_prob
										lowerCamelCase						:Tuple						  =     max_position_embeddings
										lowerCamelCase						:int						  =     type_vocab_size
										lowerCamelCase						:Optional[Any]						  =     initializer_range
										lowerCamelCase						:Union[str, Any]						  =     layer_norm_eps
										lowerCamelCase						:Dict						  =     position_embedding_type
										lowerCamelCase						:List[Any]						  =     use_cache
										lowerCamelCase						:Optional[int]						  =     classifier_dropout


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							@property
							def 				snake_case  (				self     :    Any		):
										if self.task == "multiple-choice":
													lowerCamelCase						:Optional[Any]						  =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
													lowerCamelCase						:List[str]						  =     {0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]		)



