


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


logging.set_verbosity_info()
__lowerCamelCase       				=	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase       				=	[]
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 					a__    (				UpperCamelCase_	:			Optional[int],      UpperCamelCase_	:			str,      UpperCamelCase_	:			List[str]	):
   UpperCAmelCase__  :Optional[Any]            =			state_dict.pop(_SCREAMING_SNAKE_CASE	)
   UpperCAmelCase__  :Any            =			val


def 					a__    (				UpperCamelCase_	:			Any	):
   UpperCAmelCase__  :Tuple            =			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__  :Union[str, Any]            =			value
      else:
         UpperCAmelCase__  :Optional[Any]            =			value

   return new_state_dict


def 					a__    (				UpperCamelCase_	:			Tuple,      UpperCamelCase_	:			List[str]=False	):
   UpperCAmelCase__  :List[Any]            =			''
   if is_panoptic:
      UpperCAmelCase__  :Dict            =			'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__  :Optional[int]            =			state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
      UpperCAmelCase__  :Optional[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
      UpperCAmelCase__  :List[Any]            =			in_proj_weight[:256, :]
      UpperCAmelCase__  :Any            =			in_proj_bias[:256]
      UpperCAmelCase__  :Optional[int]            =			in_proj_weight[256:512, :]
      UpperCAmelCase__  :Tuple            =			in_proj_bias[256:512]
      UpperCAmelCase__  :Tuple            =			in_proj_weight[-256:, :]
      UpperCAmelCase__  :int            =			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__  :List[Any]            =			state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
      UpperCAmelCase__  :List[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__  :str            =			in_proj_bias[:256]
      UpperCAmelCase__  :Optional[int]            =			in_proj_weight[256:512, :]
      UpperCAmelCase__  :Dict            =			in_proj_bias[256:512]
      UpperCAmelCase__  :Optional[Any]            =			in_proj_weight[-256:, :]
      UpperCAmelCase__  :List[Any]            =			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__  :int            =			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__  :Any            =			in_proj_weight_cross_attn[:256, :]
      UpperCAmelCase__  :Tuple            =			in_proj_bias_cross_attn[:256]
      UpperCAmelCase__  :Any            =			in_proj_weight_cross_attn[256:512, :]
      UpperCAmelCase__  :Tuple            =			in_proj_bias_cross_attn[256:512]
      UpperCAmelCase__  :Tuple            =			in_proj_weight_cross_attn[-256:, :]
      UpperCAmelCase__  :Dict            =			in_proj_bias_cross_attn[-256:]


def 					a__    (				):
   UpperCAmelCase__  :str            =			'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCAmelCase__  :Optional[Any]            =			Image.open(requests.get(_SCREAMING_SNAKE_CASE,      stream=_SCREAMING_SNAKE_CASE	).raw	)

   return im


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

   # load image processor
   UpperCAmelCase__  :Any            =			'coco_panoptic' if is_panoptic else 'coco_detection'
   UpperCAmelCase__  :Union[str, Any]            =			DetrImageProcessor(format=_SCREAMING_SNAKE_CASE	)

   # prepare image
   UpperCAmelCase__  :Union[str, Any]            =			prepare_img()
   UpperCAmelCase__  :List[Any]            =			image_processor(images=_SCREAMING_SNAKE_CASE,      return_tensors='''pt'''	)
   UpperCAmelCase__  :Dict            =			encoding['pixel_values']

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

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





'''simple docstring'''





from scipy.constants import g


def 					a__    (				UpperCamelCase_	:			float,      UpperCamelCase_	:			float	):
   if mass < 0:
      # handling of negative values of mass
      raise ValueError('''The mass of a body cannot be negative'''	)
   if height < 0:
      # handling of negative values of height
      raise ValueError('''The height above the ground cannot be negative'''	)
   return mass * g * height


if __name__ == "__main__":
    from doctest import testmod

    testmod(name='''potential_energy''')



