






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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a		:	Dict      					=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a		:	str      					=			[]
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}.multihead_attn.out_proj.weight""",
               f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
           )
       )
       rename_keys.append(
           (
               f"""transformer.decoder.layers.{i}.multihead_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"""))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
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"),
    ]
)






def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[int]  ,		__lowerCamelCase      :					Union[str, Any]  ,		__lowerCamelCase      :					Optional[Any]		):
  __UpperCAmelCase							:     Any									=  state_dict.pop(__lowerCamelCase		)
  __UpperCAmelCase							:     Tuple									=  val






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

  return new_state_dict






def     lowerCamelCase__				(   __lowerCamelCase      :					List[str]  ,		__lowerCamelCase      :					int=False		):
  __UpperCAmelCase							:     Union[str, Any]									=  """"""
  if is_panoptic:
    __UpperCAmelCase							:     Union[str, Any]									=  """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							:     List[Any]									=  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""		)
    __UpperCAmelCase							:     Optional[int]									=  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							:     Tuple									=  in_proj_weight[:256, :]
    __UpperCAmelCase							:     List[str]									=  in_proj_bias[:256]
    __UpperCAmelCase							:     Optional[Any]									=  in_proj_weight[256:512, :]
    __UpperCAmelCase							:     str									=  in_proj_bias[256:512]
    __UpperCAmelCase							:     Optional[Any]									=  in_proj_weight[-256:, :]
    __UpperCAmelCase							:     Optional[Any]									=  in_proj_bias[-256:]
  # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  for i in range(6		):
    # read in weights + bias of input projection layer of self-attention
    __UpperCAmelCase							:     Optional[Any]									=  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""		)
    __UpperCAmelCase							:     Optional[Any]									=  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""		)
    # next, add query, keys and values (in that order) to the state dict
    __UpperCAmelCase							:     Tuple									=  in_proj_weight[:256, :]
    __UpperCAmelCase							:     Any									=  in_proj_bias[:256]
    __UpperCAmelCase							:     Optional[int]									=  in_proj_weight[256:512, :]
    __UpperCAmelCase							:     Optional[int]									=  in_proj_bias[256:512]
    __UpperCAmelCase							:     Tuple									=  in_proj_weight[-256:, :]
    __UpperCAmelCase							:     Optional[int]									=  in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    __UpperCAmelCase							:     List[Any]									=  state_dict.pop(
        f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""		)
    __UpperCAmelCase							:     Dict									=  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""		)
    # next, add query, keys and values (in that order) of cross-attention to the state dict
    __UpperCAmelCase							:     str									=  in_proj_weight_cross_attn[:256, :]
    __UpperCAmelCase							:     Tuple									=  in_proj_bias_cross_attn[:256]
    __UpperCAmelCase							:     Union[str, Any]									=  in_proj_weight_cross_attn[256:512, :]
    __UpperCAmelCase							:     List[str]									=  in_proj_bias_cross_attn[256:512]
    __UpperCAmelCase							:     Optional[int]									=  in_proj_weight_cross_attn[-256:, :]
    __UpperCAmelCase							:     List[str]									=  in_proj_bias_cross_attn[-256:]






def     lowerCamelCase__				(   ):
  __UpperCAmelCase							:     Any									=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  __UpperCAmelCase							:     Dict									=  Image.open(requests.get(__lowerCamelCase  ,		stream=__lowerCamelCase		).raw		)

  return im






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

  # load image processor
  __UpperCAmelCase							:     Optional[Any]									=  """coco_panoptic""" if is_panoptic else """coco_detection"""
  __UpperCAmelCase							:     Dict									=  DetrImageProcessor(format=__lowerCamelCase		)

  # prepare image
  __UpperCAmelCase							:     List[Any]									=  prepare_img()
  __UpperCAmelCase							:     Optional[Any]									=  image_processor(images=__lowerCamelCase  ,		return_tensors="""pt"""		)
  __UpperCAmelCase							:     Optional[int]									=  encoding["""pixel_values"""]

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

  # load original model from torch hub
  __UpperCAmelCase							:     Any									=  torch.hub.load("""facebookresearch/detr"""  ,		__lowerCamelCase  ,		pretrained=__lowerCamelCase		).eval()
  __UpperCAmelCase							:     Optional[Any]									=  detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      __UpperCAmelCase							:     List[str]									=  """detr.""" + src
    rename_key(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase		)
  __UpperCAmelCase							:     Dict									=  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							:     int									=  """detr.model.""" if is_panoptic else """model."""
  for key in state_dict.copy().keys():
    if is_panoptic:
      if (
          key.startswith("""detr"""		)
          and not key.startswith("""class_labels_classifier"""		)
          and not key.startswith("""bbox_predictor"""		)
      ):
        __UpperCAmelCase							:     str									=  state_dict.pop(__lowerCamelCase		)
        __UpperCAmelCase							:     Optional[int]									=  val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        __UpperCAmelCase							:     str									=  state_dict.pop(__lowerCamelCase		)
        __UpperCAmelCase							:     Optional[int]									=  val
      elif key.startswith("""bbox_attention"""		) or key.startswith("""mask_head"""		):
        continue
      else:
        __UpperCAmelCase							:     Optional[Any]									=  state_dict.pop(__lowerCamelCase		)
        __UpperCAmelCase							:     Optional[Any]									=  val
    else:
      if not key.startswith("""class_labels_classifier"""		) and not key.startswith("""bbox_predictor"""		):
        __UpperCAmelCase							:     Optional[Any]									=  state_dict.pop(__lowerCamelCase		)
        __UpperCAmelCase							:     Dict									=  val
    # finally, create HuggingFace model and load state dict
  __UpperCAmelCase							:     List[Any]									=  DetrForSegmentation(__lowerCamelCase		) if is_panoptic else DetrForObjectDetection(__lowerCamelCase		)
  model.load_state_dict(__lowerCamelCase		)
  model.eval()
  # verify our conversion
  __UpperCAmelCase							:     Dict									=  detr(__lowerCamelCase		)
  __UpperCAmelCase							:     int									=  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		:	Any      					=			argparse.ArgumentParser()

       parser.add_argument(
           "--model_name", default="detr_resnet50", type=str, help="Name of the 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		:	List[str]      					=			parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










def     lowerCamelCase__				(   ):

  for a in range(300		):
    for b in range(a + 1  ,		400		):
      for c in range(b + 1  ,		500		):
        if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
          return a * b * c

  return -1






def     lowerCamelCase__				(   ):

  for a in range(300		):
    for b in range(400		):
      __UpperCAmelCase							:     Optional[Any]									=  1000 - a - b
      if a < b < c and (a**2) + (b**2) == (c**2):
        return a * b * c

  return -1






def     lowerCamelCase__				(   ):
  import timeit

  print(
      timeit.timeit("""solution()"""  ,		setup="""from __main__ import solution"""  ,		number=1000		)		)
  print(
      timeit.timeit(
          """solution_fast()"""  ,		setup="""from __main__ import solution_fast"""  ,		number=1000		)		)


if __name__ == "__main__":
       print(f"""{solution() = }""")


