
"""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()
_a		       =							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_a		       =							[]
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 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    state_dict.pop(__lowerCamelCase  )
   UpperCAmelCase_					:					List[Any]			     =    val







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					List[str]			     =    OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         UpperCAmelCase_					:					int			     =    key.replace("backbone.0.body",		"backbone.conv_encoder.model"  )
         UpperCAmelCase_					:					str			     =    value
      else:
         UpperCAmelCase_					:					Any			     =    value

   return new_state_dict







def 	__a     (		__lowerCamelCase,		__lowerCamelCase=False  ):
   UpperCAmelCase_					:					Dict			     =    ""
   if is_panoptic:
      UpperCAmelCase_					:					Optional[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)
      UpperCAmelCase_					:					Dict			     =    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""  )
      UpperCAmelCase_					:					Tuple			     =    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
      UpperCAmelCase_					:					Optional[int]			     =    in_proj_weight[:256, :]
      UpperCAmelCase_					:					Tuple			     =    in_proj_bias[:256]
      UpperCAmelCase_					:					str			     =    in_proj_weight[256:512, :]
      UpperCAmelCase_					:					Dict			     =    in_proj_bias[256:512]
      UpperCAmelCase_					:					List[str]			     =    in_proj_weight[-256:, :]
      UpperCAmelCase_					:					List[str]			     =    in_proj_bias[-256:]







def 	__a     (		):
   UpperCAmelCase_					:					Optional[Any]			     =    "http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase_					:					Dict			     =    Image.open(requests.get(__lowerCamelCase,		stream=__lowerCamelCase  ).raw  )

   return im







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

   # load image processor
   UpperCAmelCase_					:					Optional[Any]			     =    "coco_panoptic" if is_panoptic else "coco_detection"
   UpperCAmelCase_					:					Union[str, Any]			     =    ConditionalDetrImageProcessor(format=__lowerCamelCase  )

   # prepare image
   UpperCAmelCase_					:					Tuple			     =    prepare_img()
   UpperCAmelCase_					:					Optional[Any]			     =    image_processor(images=__lowerCamelCase,		return_tensors="pt"  )
   UpperCAmelCase_					:					Dict			     =    encoding["pixel_values"]

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

   # load original model from torch hub
   UpperCAmelCase_					:					Union[str, Any]			     =    torch.hub.load("DeppMeng/ConditionalDETR",		__lowerCamelCase,		pretrained=__lowerCamelCase  ).eval()
   UpperCAmelCase_					:					int			     =    conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         UpperCAmelCase_					:					List[Any]			     =    "conditional_detr." + src
      rename_key(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
   UpperCAmelCase_					:					str			     =    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
   UpperCAmelCase_					:					Dict			     =    "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"  )
         ):
            UpperCAmelCase_					:					Optional[Any]			     =    state_dict.pop(__lowerCamelCase  )
            UpperCAmelCase_					:					Union[str, Any]			     =    val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            UpperCAmelCase_					:					Dict			     =    state_dict.pop(__lowerCamelCase  )
            UpperCAmelCase_					:					Optional[int]			     =    val
         elif key.startswith("bbox_attention"  ) or key.startswith("mask_head"  ):
            continue
         else:
            UpperCAmelCase_					:					List[Any]			     =    state_dict.pop(__lowerCamelCase  )
            UpperCAmelCase_					:					str			     =    val
      else:
         if not key.startswith("class_labels_classifier"  ) and not key.startswith("bbox_predictor"  ):
            UpperCAmelCase_					:					Optional[int]			     =    state_dict.pop(__lowerCamelCase  )
            UpperCAmelCase_					:					Optional[Any]			     =    val
    # finally, create HuggingFace model and load state dict
   UpperCAmelCase_					:					Union[str, Any]			     =    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
   UpperCAmelCase_					:					List[Any]			     =    conditional_detr(__lowerCamelCase  )
   UpperCAmelCase_					:					List[str]			     =    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__":
    _a		       =							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.'
    )
    _a		       =							parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




"""simple docstring"""





import argparse
from collections import defaultdict







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    f"""{file}_{class_name}_{test_name}"""
   done_test[_id] += 1

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					List[Any]			     =    f.readlines()

   UpperCAmelCase_					:					int			     =    f"""class {class_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{4 * " "}def {test_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{8 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					Tuple			     =    f"""{16 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					int			     =    False
   UpperCAmelCase_					:					Union[str, Any]			     =    False
   UpperCAmelCase_					:					str			     =    False
   UpperCAmelCase_					:					Optional[Any]			     =    False
   UpperCAmelCase_					:					List[str]			     =    0
   UpperCAmelCase_					:					Optional[int]			     =    0

   UpperCAmelCase_					:					int			     =    []
   for line in lines:
      if line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Tuple			     =    True
      elif in_class and line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Optional[int]			     =    True
      elif in_class and in_func and (line.startswith(__lowerCamelCase  ) or line.startswith(__lowerCamelCase  )):
         UpperCAmelCase_					:					Any			     =    len(line.split(correct_line.split()[0]  )[0]  )
         count += 1

         if count == done_test[_id]:
            UpperCAmelCase_					:					Union[str, Any]			     =    True

      if in_class and in_func and in_line:
         if ")" not in line:
            continue
         else:
            UpperCAmelCase_					:					Any			     =    True

      if in_class and in_func and in_line and insert_line:
         new_lines.append(f"""{spaces * " "}{correct_line}"""  )
         UpperCAmelCase_					:					int			     =    False
      else:
         new_lines.append(__lowerCamelCase  )

   with open(__lowerCamelCase,		"w"  ) as f:
      for line in new_lines:
         f.write(__lowerCamelCase  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase=None  ):
   if fail is not None:
      with open(__lowerCamelCase,		"r"  ) as f:
         UpperCAmelCase_					:					Tuple			     =    {l.strip() for l in f.readlines()}
   else:
      UpperCAmelCase_					:					str			     =    None

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					Optional[int]			     =    f.readlines()

   UpperCAmelCase_					:					Any			     =    defaultdict(__lowerCamelCase  )
   for line in correct_lines:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    line.split(";"  )
      if test_failures is None or "::".join([file, class_name, test_name]  ) in test_failures:
         overwrite_file(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )


if __name__ == "__main__":
    _a		       =							argparse.ArgumentParser()
    parser.add_argument('--correct_filename', help='filename of tests with expected result')
    parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
    _a		       =							parser.parse_args()

    main(args.correct_filename, args.fail_filename)


