





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()
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}.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   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		state_dict.pop(_UpperCAmelCase      )
      SCREAMING_SNAKE_CASE						=		val



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  SCREAMING_SNAKE_CASE						=		key.replace("""backbone.0.body"""					,		"""backbone.conv_encoder.model"""      )
                  SCREAMING_SNAKE_CASE						=		value
            else:
                  SCREAMING_SNAKE_CASE						=		value

      return new_state_dict



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE=False      )					->  int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		''
      if is_panoptic:
            SCREAMING_SNAKE_CASE						=		'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)
            SCREAMING_SNAKE_CASE						=		state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""      )
            SCREAMING_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
            SCREAMING_SNAKE_CASE						=		in_proj_weight[:2_56, :]
            SCREAMING_SNAKE_CASE						=		in_proj_bias[:2_56]
            SCREAMING_SNAKE_CASE						=		in_proj_weight[2_56:5_12, :]
            SCREAMING_SNAKE_CASE						=		in_proj_bias[2_56:5_12]
            SCREAMING_SNAKE_CASE						=		in_proj_weight[-2_56:, :]
            SCREAMING_SNAKE_CASE						=		in_proj_bias[-2_56:]
      # 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
            SCREAMING_SNAKE_CASE						=		state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""      )
            SCREAMING_SNAKE_CASE						=		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
            SCREAMING_SNAKE_CASE						=		in_proj_weight[:2_56, :]
            SCREAMING_SNAKE_CASE						=		in_proj_bias[:2_56]
            SCREAMING_SNAKE_CASE						=		in_proj_weight[2_56:5_12, :]
            SCREAMING_SNAKE_CASE						=		in_proj_bias[2_56:5_12]
            SCREAMING_SNAKE_CASE						=		in_proj_weight[-2_56:, :]
            SCREAMING_SNAKE_CASE						=		in_proj_bias[-2_56:]
            # read in weights + bias of input projection layer of cross-attention
            SCREAMING_SNAKE_CASE						=		state_dict.pop(
                F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""      )
            SCREAMING_SNAKE_CASE						=		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
            SCREAMING_SNAKE_CASE						=		in_proj_weight_cross_attn[:2_56, :]
            SCREAMING_SNAKE_CASE						=		in_proj_bias_cross_attn[:2_56]
            SCREAMING_SNAKE_CASE						=		in_proj_weight_cross_attn[2_56:5_12, :]
            SCREAMING_SNAKE_CASE						=		in_proj_bias_cross_attn[2_56:5_12]
            SCREAMING_SNAKE_CASE						=		in_proj_weight_cross_attn[-2_56:, :]
            SCREAMING_SNAKE_CASE						=		in_proj_bias_cross_attn[-2_56:]



def   __lowercase  ( )					->  Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE						=		Image.open(requests.get(_UpperCAmelCase					,		stream=_UpperCAmelCase      ).raw      )

      return im



@torch.no_grad()
def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		DetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            SCREAMING_SNAKE_CASE						=		'resnet101'
      if "dc5" in model_name:
            SCREAMING_SNAKE_CASE						=		True
      SCREAMING_SNAKE_CASE						=		'panoptic' in model_name
      if is_panoptic:
            SCREAMING_SNAKE_CASE						=		2_50
      else:
            SCREAMING_SNAKE_CASE						=		91
            SCREAMING_SNAKE_CASE						=		'huggingface/label-files'
            SCREAMING_SNAKE_CASE						=		'coco-detection-id2label.json'
            SCREAMING_SNAKE_CASE						=		json.load(open(hf_hub_download(_UpperCAmelCase					,		_UpperCAmelCase					,		repo_type="""dataset"""      )					,		"""r"""      )      )
            SCREAMING_SNAKE_CASE						=		{int(_UpperCAmelCase      ): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE						=		idalabel
            SCREAMING_SNAKE_CASE						=		{v: k for k, v in idalabel.items()}

      # load image processor
      SCREAMING_SNAKE_CASE						=		'coco_panoptic' if is_panoptic else 'coco_detection'
      SCREAMING_SNAKE_CASE						=		DetrImageProcessor(format=_UpperCAmelCase      )

      # prepare image
      SCREAMING_SNAKE_CASE						=		prepare_img()
      SCREAMING_SNAKE_CASE						=		image_processor(images=_UpperCAmelCase					,		return_tensors="""pt"""      )
      SCREAMING_SNAKE_CASE						=		encoding['pixel_values']

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

      # load original model from torch hub
      SCREAMING_SNAKE_CASE						=		torch.hub.load("""facebookresearch/detr"""					,		_UpperCAmelCase					,		pretrained=_UpperCAmelCase      ).eval()
      SCREAMING_SNAKE_CASE						=		detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  SCREAMING_SNAKE_CASE						=		'detr.' + src
            rename_key(_UpperCAmelCase					,		_UpperCAmelCase					,		_UpperCAmelCase      )
      SCREAMING_SNAKE_CASE						=		rename_backbone_keys(_UpperCAmelCase      )
      # query, key and value matrices need special treatment
      read_in_q_k_v(_UpperCAmelCase					,		is_panoptic=_UpperCAmelCase      )
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      SCREAMING_SNAKE_CASE						=		'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"""      )
                  ):
                        SCREAMING_SNAKE_CASE						=		state_dict.pop(_UpperCAmelCase      )
                        SCREAMING_SNAKE_CASE						=		val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        SCREAMING_SNAKE_CASE						=		state_dict.pop(_UpperCAmelCase      )
                        SCREAMING_SNAKE_CASE						=		val
                  elif key.startswith("""bbox_attention"""      ) or key.startswith("""mask_head"""      ):
                        continue
                  else:
                        SCREAMING_SNAKE_CASE						=		state_dict.pop(_UpperCAmelCase      )
                        SCREAMING_SNAKE_CASE						=		val
            else:
                  if not key.startswith("""class_labels_classifier"""      ) and not key.startswith("""bbox_predictor"""      ):
                        SCREAMING_SNAKE_CASE						=		state_dict.pop(_UpperCAmelCase      )
                        SCREAMING_SNAKE_CASE						=		val
    # finally, create HuggingFace model and load state dict
      SCREAMING_SNAKE_CASE						=		DetrForSegmentation(_UpperCAmelCase      ) if is_panoptic else DetrForObjectDetection(_UpperCAmelCase      )
      model.load_state_dict(_UpperCAmelCase      )
      model.eval()
      # verify our conversion
      SCREAMING_SNAKE_CASE						=		detr(_UpperCAmelCase      )
      SCREAMING_SNAKE_CASE						=		model(_UpperCAmelCase      )
      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(_UpperCAmelCase      ).mkdir(exist_ok=_UpperCAmelCase      )
      model.save_pretrained(_UpperCAmelCase      )
      image_processor.save_pretrained(_UpperCAmelCase      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		  =			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."""
      )
      SCREAMING_SNAKE_CASE_		  =			parser.parse_args()
      convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







"""simple docstring"""




from __future__ import annotations

# Define the Gravitational Constant G and the function
_UpperCamelCase:					Tuple     				=	6.6_7_4_3e-1_1  # unit of G : m^3 * kg^-1 * s^-2


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					)  ->      dict[str, float]:






      '''simple docstring'''


      lowercase	:   str											= mass_a * mass_a

      if (force, mass_a, mass_a, distance).count(0					) != 1:
            raise ValueError('One and only one argument must be 0'					)
      if force < 0:
            raise ValueError('Gravitational force can not be negative'					)
      if distance < 0:
            raise ValueError('Distance can not be negative'					)
      if mass_a < 0 or mass_a < 0:
            raise ValueError('Mass can not be negative'					)
      if force == 0:
            lowercase	:   str											= GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
            return {"force": force}
      elif mass_a == 0:
            lowercase	:   Optional[int]											= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_1": mass_a}
      elif mass_a == 0:
            lowercase	:   Optional[int]											= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_2": mass_a}
      elif distance == 0:
            lowercase	:   List[Any]											= (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
            return {"distance": distance}
      raise ValueError('One and only one argument must be 0'					)


# Run doctest
if __name__ == "__main__":
     import doctest

     doctest.testmod()
