'''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()
_UpperCAmelCase						:    Any     					=					logging.get_logger(__name__)

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





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any							):
     _A								=     OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               _A								=     key.replace('backbone.0.body'      ,		'backbone.conv_encoder.model'							)
               _A								=     value
          else:
               _A								=     value

     return new_state_dict





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : List[str]=False							):
     _A								=     ''
     if is_panoptic:
          _A								=     '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								=     state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'							)
          _A								=     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								=     in_proj_weight[:2_5_6, :]
          _A								=     in_proj_bias[:2_5_6]
          _A								=     in_proj_weight[2_5_6:5_1_2, :]
          _A								=     in_proj_bias[2_5_6:5_1_2]
          _A								=     in_proj_weight[-2_5_6:, :]
          _A								=     in_proj_bias[-2_5_6:]
     # 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
          _A								=     state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'							)
          _A								=     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
          _A								=     in_proj_weight[:2_5_6, :]
          _A								=     in_proj_bias[:2_5_6]
          _A								=     in_proj_weight[2_5_6:5_1_2, :]
          _A								=     in_proj_bias[2_5_6:5_1_2]
          _A								=     in_proj_weight[-2_5_6:, :]
          _A								=     in_proj_bias[-2_5_6:]
          # read in weights + bias of input projection layer of cross-attention
          _A								=     state_dict.pop(
              F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'							)
          _A								=     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
          _A								=     in_proj_weight_cross_attn[:2_5_6, :]
          _A								=     in_proj_bias_cross_attn[:2_5_6]
          _A								=     in_proj_weight_cross_attn[2_5_6:5_1_2, :]
          _A								=     in_proj_bias_cross_attn[2_5_6:5_1_2]
          _A								=     in_proj_weight_cross_attn[-2_5_6:, :]
          _A								=     in_proj_bias_cross_attn[-2_5_6:]





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

     return im





@torch.no_grad()
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : int							):
     _A								=     DetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          _A								=     'resnet101'
     if "dc5" in model_name:
          _A								=     True
     _A								=     'panoptic' in model_name
     if is_panoptic:
          _A								=     2_5_0
     else:
          _A								=     9_1
          _A								=     'huggingface/label-files'
          _A								=     'coco-detection-id2label.json'
          _A								=     json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		repo_type='dataset'							)      ,		'r'							)							)
          _A								=     {int(_SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
          _A								=     idalabel
          _A								=     {v: k for k, v in idalabel.items()}

     # load image processor
     _A								=     'coco_panoptic' if is_panoptic else 'coco_detection'
     _A								=     DetrImageProcessor(format=_SCREAMING_SNAKE_CASE							)

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

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

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




'''simple docstring'''






from sklearn.neural_network import MLPClassifier

_UpperCAmelCase						:    str     					=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
_UpperCAmelCase						:    Any     					=					[0, 1, 0, 0]


_UpperCAmelCase						:    List[Any]     					=					MLPClassifier(
    solver='''lbfgs''', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


_UpperCAmelCase						:    Optional[Any]     					=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
_UpperCAmelCase						:    Tuple     					=					clf.predict(test)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]							):
     return list(__snake_case							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



