





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()
__UpperCamelCase    :	Optional[int]						=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase    :	Any						=     []
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 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Dict     ,    _UpperCAmelCase				:						Optional[Any]     ,    _UpperCAmelCase				:						Optional[Any] ):
 lowerCAmelCase													=   state_dict.pop(_UpperCAmelCase )
 lowerCAmelCase													=   val

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Union[str, Any] ):
 lowerCAmelCase													=   OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   lowerCAmelCase													=   key.replace('backbone.0.body'     ,    'backbone.conv_encoder.model' )
   lowerCAmelCase													=   value
  else:
   lowerCAmelCase													=   value

 return new_state_dict

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Any     ,    _UpperCAmelCase				:						int=False ):
 lowerCAmelCase													=   ''
 if is_panoptic:
  lowerCAmelCase													=   '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)
  lowerCAmelCase													=   state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
  lowerCAmelCase													=   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
  lowerCAmelCase													=   in_proj_weight[:256, :]
  lowerCAmelCase													=   in_proj_bias[:256]
  lowerCAmelCase													=   in_proj_weight[256:512, :]
  lowerCAmelCase													=   in_proj_bias[256:512]
  lowerCAmelCase													=   in_proj_weight[-256:, :]
  lowerCAmelCase													=   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
  lowerCAmelCase													=   state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
  lowerCAmelCase													=   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
  lowerCAmelCase													=   in_proj_weight[:256, :]
  lowerCAmelCase													=   in_proj_bias[:256]
  lowerCAmelCase													=   in_proj_weight[256:512, :]
  lowerCAmelCase													=   in_proj_bias[256:512]
  lowerCAmelCase													=   in_proj_weight[-256:, :]
  lowerCAmelCase													=   in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  lowerCAmelCase													=   state_dict.pop(
      F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
  lowerCAmelCase													=   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
  lowerCAmelCase													=   in_proj_weight_cross_attn[:256, :]
  lowerCAmelCase													=   in_proj_bias_cross_attn[:256]
  lowerCAmelCase													=   in_proj_weight_cross_attn[256:512, :]
  lowerCAmelCase													=   in_proj_bias_cross_attn[256:512]
  lowerCAmelCase													=   in_proj_weight_cross_attn[-256:, :]
  lowerCAmelCase													=   in_proj_bias_cross_attn[-256:]

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

 return im

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

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

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

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

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












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase    :	Dict						=     logging.get_logger(__name__)

__UpperCamelCase    :	List[Any]						=     {
    '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
}







class 			a       (      a__     ):
 snake_case__								=					'''mpnet'''



 def __init__(    self							,					_snake_case=3_05_27							,					_snake_case=7_68							,					_snake_case=12							,					_snake_case=12							,					_snake_case=30_72							,					_snake_case="gelu"							,					_snake_case=0.1							,					_snake_case=0.1							,					_snake_case=5_12							,					_snake_case=0.02							,					_snake_case=1E-12							,					_snake_case=32							,					_snake_case=1							,					_snake_case=0							,					_snake_case=2							,					**_snake_case							,					):







  """simple docstring"""






  super().__init__(pad_token_id=_snake_case							,					bos_token_id=_snake_case							,					eos_token_id=_snake_case							,					**_snake_case    )

  lowerCAmelCase													=   vocab_size
  lowerCAmelCase													=   hidden_size
  lowerCAmelCase													=   num_hidden_layers
  lowerCAmelCase													=   num_attention_heads
  lowerCAmelCase													=   hidden_act
  lowerCAmelCase													=   intermediate_size
  lowerCAmelCase													=   hidden_dropout_prob
  lowerCAmelCase													=   attention_probs_dropout_prob
  lowerCAmelCase													=   max_position_embeddings
  lowerCAmelCase													=   initializer_range
  lowerCAmelCase													=   layer_norm_eps
  lowerCAmelCase													=   relative_attention_num_buckets





