




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()
SCREAMING_SNAKE_CASE__	:				str  =       logging.get_logger(__name__)

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			Tuple:
       if "backbone.0.body" in orig_key:
              lowerCamelCase       :				List[Any]      			= orig_key.replace("backbone.0.body"			,"backbone.conv_encoder.model"   )
       if "transformer" in orig_key:
              lowerCamelCase       :				Dict      			= orig_key.replace("transformer."			,""   )
       if "norm1" in orig_key:
              if "encoder" in orig_key:
                     lowerCamelCase       :				Dict      			= orig_key.replace("norm1"			,"self_attn_layer_norm"   )
              else:
                     lowerCamelCase       :				int      			= orig_key.replace("norm1"			,"encoder_attn_layer_norm"   )
       if "norm2" in orig_key:
              if "encoder" in orig_key:
                     lowerCamelCase       :				List[str]      			= orig_key.replace("norm2"			,"final_layer_norm"   )
              else:
                     lowerCamelCase       :				Union[str, Any]      			= orig_key.replace("norm2"			,"self_attn_layer_norm"   )
       if "norm3" in orig_key:
              lowerCamelCase       :				List[str]      			= orig_key.replace("norm3"			,"final_layer_norm"   )
       if "linear1" in orig_key:
              lowerCamelCase       :				Any      			= orig_key.replace("linear1"			,"fc1"   )
       if "linear2" in orig_key:
              lowerCamelCase       :				Tuple      			= orig_key.replace("linear2"			,"fc2"   )
       if "query_embed" in orig_key:
              lowerCamelCase       :				int      			= orig_key.replace("query_embed"			,"query_position_embeddings"   )
       if "cross_attn" in orig_key:
              lowerCamelCase       :				List[str]      			= orig_key.replace("cross_attn"			,"encoder_attn"   )

       return orig_key

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			Optional[int]:
       # transformer decoder self-attention layers
       for i in range(6   ):
              # read in weights + bias of input projection layer of self-attention
              lowerCamelCase       :				Dict      			= state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''   )
              lowerCamelCase       :				Optional[Any]      			= 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
              lowerCamelCase       :				Union[str, Any]      			= in_proj_weight[:256, :]
              lowerCamelCase       :				Union[str, Any]      			= in_proj_bias[:256]
              lowerCamelCase       :				List[Any]      			= in_proj_weight[256:512, :]
              lowerCamelCase       :				Tuple      			= in_proj_bias[256:512]
              lowerCamelCase       :				Optional[Any]      			= in_proj_weight[-256:, :]
              lowerCamelCase       :				Dict      			= in_proj_bias[-256:]

def 					A				(				)     ->			int:
       lowerCamelCase       :				Any      			= "http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase       :				Union[str, Any]      			= Image.open(requests.get(_SCREAMING_SNAKE_CASE			,stream=_SCREAMING_SNAKE_CASE   ).raw   )

       return im

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

       # load image processor
       lowerCamelCase       :				Union[str, Any]      			= DeformableDetrImageProcessor(format="coco_detection"   )

       # prepare image
       lowerCamelCase       :				Optional[int]      			= prepare_img()
       lowerCamelCase       :				Dict      			= image_processor(images=_SCREAMING_SNAKE_CASE			,return_tensors="pt"   )
       lowerCamelCase       :				List[str]      			= encoding["pixel_values"]

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

       # load original state dict
       lowerCamelCase       :				Union[str, Any]      			= torch.load(_SCREAMING_SNAKE_CASE			,map_location="cpu"   )["model"]
       # rename keys
       for key in state_dict.copy().keys():
              lowerCamelCase       :				List[str]      			= state_dict.pop(_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				Dict      			= 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
       lowerCamelCase       :				List[str]      			= "model."
       for key in state_dict.copy().keys():
              if not key.startswith("class_embed"   ) and not key.startswith("bbox_embed"   ):
                     lowerCamelCase       :				Optional[int]      			= state_dict.pop(_SCREAMING_SNAKE_CASE   )
                     lowerCamelCase       :				str      			= val
    # finally, create HuggingFace model and load state dict
       lowerCamelCase       :				Tuple      			= DeformableDetrForObjectDetection(_SCREAMING_SNAKE_CASE   )
       model.load_state_dict(_SCREAMING_SNAKE_CASE   )
       model.eval()

       lowerCamelCase       :				Optional[Any]      			= "cuda" if torch.cuda.is_available() else "cpu"
       model.to(_SCREAMING_SNAKE_CASE   )
       # verify our conversion
       lowerCamelCase       :				int      			= model(pixel_values.to(_SCREAMING_SNAKE_CASE   )   )

       lowerCamelCase       :				Optional[int]      			= torch.tensor(
           [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]   )
       lowerCamelCase       :				Tuple      			= torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]   )

       if single_scale:
              lowerCamelCase       :				Any      			= torch.tensor(
                  [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]   )
              lowerCamelCase       :				Union[str, Any]      			= torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]   )

       if single_scale and dilation:
              lowerCamelCase       :				int      			= torch.tensor(
                  [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]   )
              lowerCamelCase       :				Union[str, Any]      			= torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]   )

       if with_box_refine:
              lowerCamelCase       :				List[str]      			= torch.tensor(
                  [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]   )
              lowerCamelCase       :				int      			= torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]   )

       if with_box_refine and two_stage:
              lowerCamelCase       :				Optional[int]      			= torch.tensor(
                  [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]   )
              lowerCamelCase       :				List[Any]      			= torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]   )

       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:
              lowerCamelCase       :				Tuple      			= "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__":
       SCREAMING_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.'
       )
       SCREAMING_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,
       )












import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


SCREAMING_SNAKE_CASE__	:				int  =       logging.get_logger(__name__)




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     str     					=				"""speech-encoder-decoder"""
    lowerCamelCase_				:     List[str]     					=				True







    def __init__(   self					,  **UpperCamelCase__					)					->				Dict:
           super().__init__(**UpperCamelCase__					)
           if "encoder" not in kwargs or "decoder" not in kwargs:
                  raise ValueError(
                      F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                      F''' `decoder` sub-configurations are passed, but only {kwargs}'''					)

           lowerCamelCase       :				Dict      			= kwargs.pop("encoder"					)
           lowerCamelCase       :				Tuple      			= encoder_config.pop("model_type"					)
           lowerCamelCase       :				int      			= kwargs.pop("decoder"					)
           lowerCamelCase       :				List[Any]      			= decoder_config.pop("model_type"					)

           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= True







    @classmethod
    def    _lowercase					(   cls					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__					)					->				PretrainedConfig:
           logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"					)
           lowerCamelCase       :				Dict      			= True
           lowerCamelCase       :				Union[str, Any]      			= True

           return cls(encoder=encoder_config.to_dict()					,  decoder=decoder_config.to_dict()					,  **UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Tuple:
           lowerCamelCase       :				Tuple      			= copy.deepcopy(self.__dict__					)
           lowerCamelCase       :				Optional[int]      			= self.encoder.to_dict()
           lowerCamelCase       :				List[str]      			= self.decoder.to_dict()
           lowerCamelCase       :				Dict      			= self.__class__.model_type
           return output






