


'''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()
_UpperCamelCase 							=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCamelCase 							=  []
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 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Union[str, Any]      =							state_dict.pop(SCREAMING_SNAKE_CASE							)
     __A	:       List[Any]      =							val


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





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

     return new_state_dict


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE=False							):




     '''simple docstring'''





     __A	:       Union[str, Any]      =							""
     if is_panoptic:
          __A	:       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)
          __A	:       List[Any]      =							state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"							)
          __A	:       List[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
          __A	:       List[str]      =							in_proj_weight[:256, :]
          __A	:       Tuple      =							in_proj_bias[:256]
          __A	:       Optional[Any]      =							in_proj_weight[256:512, :]
          __A	:       List[Any]      =							in_proj_bias[256:512]
          __A	:       str      =							in_proj_weight[-256:, :]
          __A	:       int      =							in_proj_bias[-256:]


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[int]      =							"http://images.cocodataset.org/val2017/000000039769.jpg"
     __A	:       Dict      =							Image.open(requests.get(SCREAMING_SNAKE_CASE       ,					stream=SCREAMING_SNAKE_CASE							).raw							)

     return im


@torch.no_grad()
def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





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

     # load image processor
     __A	:       Optional[int]      =							"coco_panoptic" if is_panoptic else "coco_detection"
     __A	:       List[Any]      =							ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE							)

     # prepare image
     __A	:       List[Any]      =							prepare_img()
     __A	:       Dict      =							image_processor(images=SCREAMING_SNAKE_CASE       ,					return_tensors="pt"							)
     __A	:       List[Any]      =							encoding["pixel_values"]

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

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


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





'''simple docstring'''


import unittest

import numpy as np


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = None       ,					):




     '''simple docstring'''





     __A	:       List[Any]      =							np.shape(SCREAMING_SNAKE_CASE							)
     __A	:       List[str]      =							np.shape(SCREAMING_SNAKE_CASE							)
     __A	:       Dict      =							np.shape(SCREAMING_SNAKE_CASE							)

     if shape_a[0] != shape_b[0]:
          __A	:       Tuple      =							(
              "Expected the same number of rows for A and B. "
              f"Instead found A of size {shape_a} and B of size {shape_b}"
          )
          raise ValueError(SCREAMING_SNAKE_CASE							)

     if shape_b[1] != shape_c[1]:
          __A	:       Optional[Any]      =							(
              "Expected the same number of columns for B and C. "
              f"Instead found B of size {shape_b} and C of size {shape_c}"
          )
          raise ValueError(SCREAMING_SNAKE_CASE							)

     __A	:       Tuple      =							pseudo_inv
     if a_inv is None:
          try:
               __A	:       Optional[Any]      =							np.linalg.inv(SCREAMING_SNAKE_CASE							)
          except np.linalg.LinAlgError:
               raise ValueError(
                   "Input matrix A is not invertible. Cannot compute Schur complement."							)

     return mat_c - mat_b.T @ a_inv @ mat_b







class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       Tuple      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       Tuple      =							np.array([[2, 1], [6, 3]]		)

          __A	:       Optional[Any]      =							schur_complement(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)

          __A	:       int      =							np.block([[a, b], [b.T, c]]		)

          __A	:       int      =							np.linalg.det(lowerCamelCase		)
          __A	:       List[str]      =							np.linalg.det(lowerCamelCase		)
          __A	:       Any      =							np.linalg.det(lowerCamelCase		)

          self.assertAlmostEqual(lowerCamelCase			,     det_a * det_s		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       List[Any]      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       List[str]      =							np.array([[2, 1], [6, 3]]		)

          with self.assertRaises(lowerCamelCase		):
               schur_complement(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       Union[str, Any]      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       Optional[Any]      =							np.array([[2, 1, 3], [6, 3, 5]]		)

          with self.assertRaises(lowerCamelCase		):
               schur_complement(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()

