






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase__							=     []
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}.cross_attn.out_proj.weight''',
             f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
         )
     )
     rename_keys.append(
         (
             f'''transformer.decoder.layers.{i}.cross_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'''))

     # q, k, v projections in self/cross-attention in decoder for conditional DETR
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
     )
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
     )
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
     )
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
     )
     rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
     )
     # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
     )
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
     )
     rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
     )

     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
     )
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
     )
     rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
     rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
     rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
     )
     # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
     )
     rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
     rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
     rename_keys.append(
         (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
     )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)






def   UpperCamelCase  (       snake_case__     :   List[str]	,snake_case__     :   Optional[Any]	,snake_case__     :   Optional[int]  ):




     '''simple docstring'''






     __snake_case			:str          =						state_dict.pop(snake_case__  )
     __snake_case			:str          =						val






def   UpperCamelCase  (       snake_case__     :   Optional[Any]  ):




     '''simple docstring'''






     __snake_case			:Optional[int]          =						OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               __snake_case			:Dict          =						key.replace("""backbone.0.body"""	,"""backbone.conv_encoder.model"""  )
               __snake_case			:Dict          =						value
          else:
               __snake_case			:List[str]          =						value

     return new_state_dict






def   UpperCamelCase  (       snake_case__     :   Dict	,snake_case__     :   Any=False  ):




     '''simple docstring'''






     __snake_case			:List[str]          =						""""""
     if is_panoptic:
          __snake_case			:List[Any]          =						"""conditional_detr."""

     # 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)
          __snake_case			:int          =						state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''  )
          __snake_case			:Optional[Any]          =						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
          __snake_case			:Any          =						in_proj_weight[:256, :]
          __snake_case			:int          =						in_proj_bias[:256]
          __snake_case			:Union[str, Any]          =						in_proj_weight[256:512, :]
          __snake_case			:Any          =						in_proj_bias[256:512]
          __snake_case			:str          =						in_proj_weight[-256:, :]
          __snake_case			:Dict          =						in_proj_bias[-256:]






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[str]          =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     __snake_case			:int          =						Image.open(requests.get(snake_case__	,stream=snake_case__  ).raw  )

     return im






@torch.no_grad()
def   UpperCamelCase  (       snake_case__     :   Optional[Any]	,snake_case__     :   Tuple  ):




     '''simple docstring'''






     __snake_case			:Dict          =						ConditionalDetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          __snake_case			:int          =						"""resnet101"""
     if "dc5" in model_name:
          __snake_case			:List[str]          =						True
     __snake_case			:Optional[int]          =						"""panoptic""" in model_name
     if is_panoptic:
          __snake_case			:Any          =						250
     else:
          __snake_case			:Any          =						91
          __snake_case			:Any          =						"""huggingface/label-files"""
          __snake_case			:Tuple          =						"""coco-detection-id2label.json"""
          __snake_case			:List[str]          =						json.load(open(hf_hub_download(snake_case__	,snake_case__	,repo_type="""dataset"""  )	,"""r"""  )  )
          __snake_case			:Optional[Any]          =						{int(snake_case__  ): v for k, v in idalabel.items()}
          __snake_case			:Optional[Any]          =						idalabel
          __snake_case			:List[Any]          =						{v: k for k, v in idalabel.items()}

     # load image processor
     __snake_case			:Dict          =						"""coco_panoptic""" if is_panoptic else """coco_detection"""
     __snake_case			:int          =						ConditionalDetrImageProcessor(format=snake_case__  )

     # prepare image
     __snake_case			:Union[str, Any]          =						prepare_img()
     __snake_case			:Tuple          =						image_processor(images=snake_case__	,return_tensors="""pt"""  )
     __snake_case			:List[Any]          =						encoding["""pixel_values"""]

     logger.info(f'''Converting model {model_name}...'''  )

     # load original model from torch hub
     __snake_case			:Optional[Any]          =						torch.hub.load("""DeppMeng/ConditionalDETR"""	,snake_case__	,pretrained=snake_case__  ).eval()
     __snake_case			:str          =						conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               __snake_case			:Tuple          =						"""conditional_detr.""" + src
          rename_key(snake_case__	,snake_case__	,snake_case__  )
     __snake_case			:Any          =						rename_backbone_keys(snake_case__  )
     # query, key and value matrices need special treatment
     read_in_q_k_v(snake_case__	,is_panoptic=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
     __snake_case			:Optional[Any]          =						"""conditional_detr.model.""" if is_panoptic else """model."""
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith("""conditional_detr"""  )
                   and not key.startswith("""class_labels_classifier"""  )
                   and not key.startswith("""bbox_predictor"""  )
               ):
                    __snake_case			:Tuple          =						state_dict.pop(snake_case__  )
                    __snake_case			:Optional[int]          =						val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    __snake_case			:List[Any]          =						state_dict.pop(snake_case__  )
                    __snake_case			:Tuple          =						val
               elif key.startswith("""bbox_attention"""  ) or key.startswith("""mask_head"""  ):
                    continue
               else:
                    __snake_case			:Any          =						state_dict.pop(snake_case__  )
                    __snake_case			:Union[str, Any]          =						val
          else:
               if not key.startswith("""class_labels_classifier"""  ) and not key.startswith("""bbox_predictor"""  ):
                    __snake_case			:str          =						state_dict.pop(snake_case__  )
                    __snake_case			:int          =						val
    # finally, create HuggingFace model and load state dict
     __snake_case			:Dict          =						ConditionalDetrForSegmentation(snake_case__  ) if is_panoptic else ConditionalDetrForObjectDetection(snake_case__  )
     model.load_state_dict(snake_case__  )
     model.eval()
     model.push_to_hub(repo_id=snake_case__	,organization="""DepuMeng"""	,commit_message="""Add model"""  )
     # verify our conversion
     __snake_case			:int          =						conditional_detr(snake_case__  )
     __snake_case			:Any          =						model(snake_case__  )
     assert torch.allclose(outputs.logits	,original_outputs["""pred_logits"""]	,atol=1e-4  )
     assert torch.allclose(outputs.pred_boxes	,original_outputs["""pred_boxes"""]	,atol=1e-4  )
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks	,original_outputs["""pred_masks"""]	,atol=1e-4  )

     # Save model and image processor
     logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''  )
     Path(snake_case__  ).mkdir(exist_ok=snake_case__  )
     model.save_pretrained(snake_case__  )
     image_processor.save_pretrained(snake_case__  )


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

     parser.add_argument(
         """--model_name""",
         default="""conditional_detr_resnet50""",
         type=str,
         help="""Name of the CONDITIONAL_DETR 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."""
     )
     lowerCamelCase__							=     parser.parse_args()
     convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Optional[int] 				= "visual_bert"



       def __init__(							self			,						a__=3_05_22			,						a__=7_68			,						a__=5_12			,						a__=12			,						a__=12			,						a__=30_72			,						a__="gelu"			,						a__=0.1			,						a__=0.1			,						a__=5_12			,						a__=2			,						a__=0.02			,						a__=1e-12			,						a__=False			,						a__=True			,						a__=1			,						a__=0			,						a__=2			,						**a__			,						) ->      Any:






            '''simple docstring'''





            super().__init__(pad_token_id=a__			,						bos_token_id=a__			,						eos_token_id=a__			,						**a__    )

            __snake_case			:Tuple          =						vocab_size
            __snake_case			:Any          =						max_position_embeddings
            __snake_case			:int          =						hidden_size
            __snake_case			:List[str]          =						visual_embedding_dim
            __snake_case			:List[str]          =						num_hidden_layers
            __snake_case			:Tuple          =						num_attention_heads
            __snake_case			:str          =						intermediate_size
            __snake_case			:Union[str, Any]          =						hidden_act
            __snake_case			:Union[str, Any]          =						hidden_dropout_prob
            __snake_case			:str          =						attention_probs_dropout_prob
            __snake_case			:Tuple          =						initializer_range
            __snake_case			:str          =						type_vocab_size
            __snake_case			:Optional[Any]          =						layer_norm_eps
            __snake_case			:Union[str, Any]          =						bypass_transformer
            __snake_case			:Tuple          =						special_visual_initialize


