





'''simple docstring'''




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()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE							     =			[]
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 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							List[str]		)		->  Dict:
   snake_case 						=   state_dict.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str		)		->  int:
   snake_case 						=   OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         snake_case 						=   key.replace("""backbone.0.body"""      ,    """backbone.conv_encoder.model"""		)
         snake_case 						=   value
      else:
         snake_case 						=   value

   return new_state_dict





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Union[str, Any]=False		)		->  Dict:
   snake_case 						=   """"""
   if is_panoptic:
      snake_case 						=   """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 						=   state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''		)
      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
      snake_case 						=   in_proj_weight[:2_56, :]
      snake_case 						=   in_proj_bias[:2_56]
      snake_case 						=   in_proj_weight[2_56:5_12, :]
      snake_case 						=   in_proj_bias[2_56:5_12]
      snake_case 						=   in_proj_weight[-2_56:, :]
      snake_case 						=   in_proj_bias[-2_56:]





def 	__lowerCamelCase			(      )		->  Optional[Any]:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)

   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							List[str]		)		->  str:
   snake_case 						=   ConditionalDetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      snake_case 						=   """resnet101"""
   if "dc5" in model_name:
      snake_case 						=   True
   snake_case 						=   """panoptic""" in model_name
   if is_panoptic:
      snake_case 						=   2_50
   else:
      snake_case 						=   91
      snake_case 						=   """huggingface/label-files"""
      snake_case 						=   """coco-detection-id2label.json"""
      snake_case 						=   json.load(open(hf_hub_download(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)      ,    """r"""		)		)
      snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case 						=   idalabel
      snake_case 						=   {v: k for k, v in idalabel.items()}

   # load image processor
   snake_case 						=   """coco_panoptic""" if is_panoptic else """coco_detection"""
   snake_case 						=   ConditionalDetrImageProcessor(format=__lowerCAmelCase		)

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

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

   # load original model from torch hub
   snake_case 						=   torch.hub.load("""DeppMeng/ConditionalDETR"""      ,    __lowerCAmelCase      ,    pretrained=__lowerCAmelCase		).eval()
   snake_case 						=   conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         snake_case 						=   """conditional_detr.""" + src
      rename_key(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   snake_case 						=   rename_backbone_keys(__lowerCAmelCase		)
   # query, key and value matrices need special treatment
   read_in_q_k_v(__lowerCAmelCase      ,    is_panoptic=__lowerCAmelCase		)
   # 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 						=   """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 						=   state_dict.pop(__lowerCAmelCase		)
            snake_case 						=   val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            snake_case 						=   state_dict.pop(__lowerCAmelCase		)
            snake_case 						=   val
         elif key.startswith("""bbox_attention"""		) or key.startswith("""mask_head"""		):
            continue
         else:
            snake_case 						=   state_dict.pop(__lowerCAmelCase		)
            snake_case 						=   val
      else:
         if not key.startswith("""class_labels_classifier"""		) and not key.startswith("""bbox_predictor"""		):
            snake_case 						=   state_dict.pop(__lowerCAmelCase		)
            snake_case 						=   val
    # finally, create HuggingFace model and load state dict
   snake_case 						=   ConditionalDetrForSegmentation(__lowerCAmelCase		) if is_panoptic else ConditionalDetrForObjectDetection(__lowerCAmelCase		)
   model.load_state_dict(__lowerCAmelCase		)
   model.eval()
   model.push_to_hub(repo_id=__lowerCAmelCase      ,    organization="""DepuMeng"""      ,    commit_message="""Add model"""		)
   # verify our conversion
   snake_case 						=   conditional_detr(__lowerCAmelCase		)
   snake_case 						=   model(__lowerCAmelCase		)
   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(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
   model.save_pretrained(__lowerCAmelCase		)
   image_processor.save_pretrained(__lowerCAmelCase		)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			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."
 )
 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








'''simple docstring'''




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


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "trajectory_transformer"
     snake_case_						      =   ["past_key_values"]
     snake_case_						      =   {
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }



     def __init__(   self							:			Optional[Any]   ,       __snake_case							:			int=1_00   ,       __snake_case							:			List[str]=5   ,       __snake_case							:			str=1   ,       __snake_case							:			Union[str, Any]=1   ,       __snake_case							:			Union[str, Any]=2_49   ,       __snake_case							:			List[Any]=6   ,       __snake_case							:			Optional[int]=17   ,       __snake_case							:			Optional[Any]=25   ,       __snake_case							:			Union[str, Any]=4   ,       __snake_case							:			List[Any]=4   ,       __snake_case							:			Optional[int]=1_28   ,       __snake_case							:			Optional[Any]=0.1   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			int=0.1   ,       __snake_case							:			Union[str, Any]=0.00_06   ,       __snake_case							:			Tuple=5_12   ,       __snake_case							:			int=0.02   ,       __snake_case							:			Any=1e-12   ,       __snake_case							:			Optional[Any]=1   ,       __snake_case							:			List[Any]=True   ,       __snake_case							:			Dict=1   ,       __snake_case							:			Dict=5_02_56   ,       __snake_case							:			Union[str, Any]=5_02_56   ,       **__snake_case							:			Tuple   ,       )->	Optional[int]:
        snake_case 						=   vocab_size
        snake_case 						=   action_weight
        snake_case 						=   reward_weight
        snake_case 						=   value_weight
        snake_case 						=   max_position_embeddings
        snake_case 						=   block_size
        snake_case 						=   action_dim
        snake_case 						=   observation_dim
        snake_case 						=   transition_dim
        snake_case 						=   learning_rate
        snake_case 						=   n_layer
        snake_case 						=   n_head
        snake_case 						=   n_embd
        snake_case 						=   embd_pdrop
        snake_case 						=   attn_pdrop
        snake_case 						=   resid_pdrop
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   kaiming_initializer_range
        snake_case 						=   use_cache
        super().__init__(pad_token_id=__snake_case   ,       bos_token_id=__snake_case   ,       eos_token_id=__snake_case   ,       **__snake_case						)

