





"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case					:		str							=			logging.get_logger(__name__)






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

							return orig_key






def  A__    (      UpperCamelCase	):
							# transformer decoder self-attention layers
							for i in range(6	):
														# read in weights + bias of input projection layer of self-attention
														A		=  state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight"	)
														A		=  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
														A		=  in_proj_weight[:256, :]
														A		=  in_proj_bias[:256]
														A		=  in_proj_weight[256:512, :]
														A		=  in_proj_bias[256:512]
														A		=  in_proj_weight[-256:, :]
														A		=  in_proj_bias[-256:]






def  A__    (      ):
							A		=  "http://images.cocodataset.org/val2017/000000039769.jpg"
							A		=  Image.open(requests.get(_SCREAMING_SNAKE_CASE ,  stream=_SCREAMING_SNAKE_CASE	).raw	)

							return im






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

							# load image processor
							A		=  DeformableDetrImageProcessor(format="coco_detection"	)

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

							logger.info("Converting model..."	)

							# load original state dict
							A		=  torch.load(_SCREAMING_SNAKE_CASE ,  map_location="cpu"	)["model"]
							# rename keys
							for key in state_dict.copy().keys():
														A		=  state_dict.pop(_SCREAMING_SNAKE_CASE	)
														A		=  val
							# query, key and value matrices need special treatment
							read_in_q_k_v(_SCREAMING_SNAKE_CASE	)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							A		=  "model."
							for key in state_dict.copy().keys():
														if not key.startswith("class_embed"	) and not key.startswith("bbox_embed"	):
																					A		=  state_dict.pop(_SCREAMING_SNAKE_CASE	)
																					A		=  val
    # finally, create HuggingFace model and load state dict
							A		=  DeformableDetrForObjectDetection(_SCREAMING_SNAKE_CASE	)
							model.load_state_dict(_SCREAMING_SNAKE_CASE	)
							model.eval()

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

							A		=  torch.tensor(
							    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]	)
							A		=  torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]	)

							if single_scale:
														A		=  torch.tensor(
														    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]	)
														A		=  torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]	)

							if single_scale and dilation:
														A		=  torch.tensor(
														    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]	)
														A		=  torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]	)

							if with_box_refine:
														A		=  torch.tensor(
														    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]	)
														A		=  torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]	)

							if with_box_refine and two_stage:
														A		=  torch.tensor(
														    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]	)
														A		=  torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]	)

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

							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!"	)

							# Save model and image processor
							logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."	)
							Path(_SCREAMING_SNAKE_CASE	).mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)
							model.save_pretrained(_SCREAMING_SNAKE_CASE	)
							image_processor.save_pretrained(_SCREAMING_SNAKE_CASE	)

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


if __name__ == "__main__":
		_snake_case					:		Optional[int]							=			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					:		str							=			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,
		)









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Optional[int]							=			logging.get_logger(__name__)

_snake_case					:		List[Any]							=			{
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''tvlt'''


					def __init__( self      :Dict					,	__UpperCamelCase      :Tuple=2_24					,	__UpperCamelCase      :Dict=20_48					,	__UpperCamelCase      :Optional[Any]=1_28					,	__UpperCamelCase      :Optional[int]=[16, 16]					,	__UpperCamelCase      :Any=[16, 16]					,	__UpperCamelCase      :List[str]=3					,	__UpperCamelCase      :str=1					,	__UpperCamelCase      :List[str]=8					,	__UpperCamelCase      :str=7_68					,	__UpperCamelCase      :List[str]=12					,	__UpperCamelCase      :Tuple=12					,	__UpperCamelCase      :Union[str, Any]=30_72					,	__UpperCamelCase      :List[Any]="gelu"					,	__UpperCamelCase      :List[str]=0.0					,	__UpperCamelCase      :Union[str, Any]=0.0					,	__UpperCamelCase      :List[str]=0.02					,	__UpperCamelCase      :Dict=1e-6					,	__UpperCamelCase      :Any=True					,	__UpperCamelCase      :List[str]=False					,	__UpperCamelCase      :str=16					,	__UpperCamelCase      :List[str]=5_12					,	__UpperCamelCase      :int=8					,	__UpperCamelCase      :int=20_48					,	__UpperCamelCase      :int=0.75					,	__UpperCamelCase      :Tuple=0.15					,	__UpperCamelCase      :Optional[Any]="frame-level"					,	__UpperCamelCase      :int=True					,	__UpperCamelCase      :str=True					,	__UpperCamelCase      :Tuple="classification"					,	**__UpperCamelCase      :Any					,	):
												super().__init__(**__UpperCamelCase		)

												if audio_mask_type not in ("frame-level", "patch_level"):
																			raise ValueError(
																			    "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
																			    f"got {audio_mask_type}"		)

												A		=  image_size
												A		=  spectrogram_length
												A		=  frequency_length
												A		=  image_patch_size
												A		=  audio_patch_size
												A		=  num_image_channels
												A		=  num_audio_channels
												A		=  num_frames

												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  layer_norm_eps
												A		=  qkv_bias
												A		=  use_mean_pooling

												A		=  decoder_num_attention_heads
												A		=  decoder_hidden_size
												A		=  decoder_num_hidden_layers
												A		=  decoder_intermediate_size
												A		=  pixel_mask_ratio
												A		=  audio_mask_ratio
												A		=  audio_mask_type

												A		=  task_matching
												A		=  task_mae
												A		=  loss_type


