


'''simple docstring'''




import argparse
import json
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, ResNetConfig
from transformers.utils import logging


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


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   if "resnet-50" in model_name:
      _a					:  Union[str, Any]						=  ResNetConfig.from_pretrained('microsoft/resnet-50'      )
   elif "resnet-101" in model_name:
      _a					:  str						=  ResNetConfig.from_pretrained('microsoft/resnet-101'      )
   else:
      raise ValueError('Model name should include either resnet50 or resnet101'      )

   _a					:  Optional[Any]						=  DetrConfig(use_timm_backbone=__snake_case		,	backbone_config=__snake_case      )

   # set label attributes
   _a					:  List[str]						=  'panoptic' in model_name
   if is_panoptic:
      _a					:  int						=  2_5_0
   else:
      _a					:  List[str]						=  9_1
      _a					:  Tuple						=  'huggingface/label-files'
      _a					:  List[Any]						=  'coco-detection-id2label.json'
      _a					:  Dict						=  json.load(open(hf_hub_download(__snake_case		,	__snake_case		,	repo_type='dataset'      )		,	'r'      )      )
      _a					:  Tuple						=  {int(__snake_case      ): v for k, v in idalabel.items()}
      _a					:  str						=  idalabel
      _a					:  Tuple						=  {v: k for k, v in idalabel.items()}

   return config, is_panoptic


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Any						=  []

   # stem
   # fmt: off
   rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')      )
   rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')      )
   rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')      )
   rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')      )
   rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')      )
   # stages
   for stage_idx in range(len(config.backbone_config.depths      )      ):
      for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
         # shortcut
         if layer_idx == 0:
            rename_keys.append(
                (
                    f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                    f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
                )      )
            rename_keys.append(
                (
                    f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                    f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
                )      )
            rename_keys.append(
                (
                    f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                    f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
                )      )
            rename_keys.append(
                (
                    f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                    f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
                )      )
            rename_keys.append(
                (
                    f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                    f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
                )      )
         # 3 convs
         for i in range(3      ):
            rename_keys.append(
                (
                    f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                    f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
                )      )
            rename_keys.append(
                (
                    f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                    f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
                )      )
            rename_keys.append(
                (
                    f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                    f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
                )      )
            rename_keys.append(
                (
                    f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                    f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
                )      )
            rename_keys.append(
                (
                    f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                    f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                )      )
    # fmt: on

   for i in range(config.encoder_layers      ):
      # 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'),
       ]      )

   return rename_keys


def    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





   _a					:  Optional[Any]						=  state_dict.pop(__snake_case      )
   _a					:  Tuple						=  val


def    UpperCAmelCase_  (       A		,	A=False      ):
   '''simple docstring'''





   _a					:  Optional[int]						=  ''
   if is_panoptic:
      _a					:  Optional[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)
      _a					:  Tuple						=  state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''      )
      _a					:  str						=  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					:  Dict						=  in_proj_weight[:2_5_6, :]
      _a					:  List[Any]						=  in_proj_bias[:2_5_6]
      _a					:  Union[str, Any]						=  in_proj_weight[2_5_6:5_1_2, :]
      _a					:  str						=  in_proj_bias[2_5_6:5_1_2]
      _a					:  Tuple						=  in_proj_weight[-2_5_6:, :]
      _a					:  Optional[Any]						=  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					:  Optional[int]						=  state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''      )
      _a					:  str						=  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					:  List[Any]						=  in_proj_weight[:2_5_6, :]
      _a					:  Optional[int]						=  in_proj_bias[:2_5_6]
      _a					:  Tuple						=  in_proj_weight[2_5_6:5_1_2, :]
      _a					:  int						=  in_proj_bias[2_5_6:5_1_2]
      _a					:  Dict						=  in_proj_weight[-2_5_6:, :]
      _a					:  Optional[Any]						=  in_proj_bias[-2_5_6:]
      # read in weights + bias of input projection layer of cross-attention
      _a					:  Any						=  state_dict.pop(
          f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''      )
      _a					:  List[Any]						=  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					:  Tuple						=  in_proj_weight_cross_attn[:2_5_6, :]
      _a					:  Dict						=  in_proj_bias_cross_attn[:2_5_6]
      _a					:  Dict						=  in_proj_weight_cross_attn[2_5_6:5_1_2, :]
      _a					:  Optional[Any]						=  in_proj_bias_cross_attn[2_5_6:5_1_2]
      _a					:  str						=  in_proj_weight_cross_attn[-2_5_6:, :]
      _a					:  Any						=  in_proj_bias_cross_attn[-2_5_6:]


def    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  Union[str, Any]						=  'http://images.cocodataset.org/val2017/000000039769.jpg'
   _a					:  Dict						=  Image.open(requests.get(__snake_case		,	stream=__snake_case      ).raw      )

   return im


@torch.no_grad()
def    UpperCAmelCase_  (       A		,	A=None		,	A=False      ):
   '''simple docstring'''





   _a		,						_a					:  Dict						=  get_detr_config(__snake_case      )

   # load original model from torch hub
   _a					:  List[str]						=  {
       'detr-resnet-50': 'detr_resnet50',
       'detr-resnet-101': 'detr_resnet101',
   }
   logger.info(f'''Converting model {model_name}...'''      )
   _a					:  Optional[Any]						=  torch.hub.load('facebookresearch/detr'		,	model_name_to_original_name[model_name]		,	pretrained=__snake_case      ).eval()
   _a					:  List[str]						=  detr.state_dict()
   # rename keys
   for src, dest in create_rename_keys(__snake_case      ):
      if is_panoptic:
         _a					:  str						=  'detr.' + src
      rename_key(__snake_case		,	__snake_case		,	__snake_case      )
   # query, key and value matrices need special treatment
   read_in_q_k_v(__snake_case		,	is_panoptic=__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					:  List[str]						=  '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					:  Optional[int]						=  state_dict.pop(__snake_case      )
            _a					:  Dict						=  val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _a					:  Optional[int]						=  state_dict.pop(__snake_case      )
            _a					:  List[Any]						=  val
         elif key.startswith('bbox_attention'      ) or key.startswith('mask_head'      ):
            continue
         else:
            _a					:  Optional[int]						=  state_dict.pop(__snake_case      )
            _a					:  Optional[int]						=  val
      else:
         if not key.startswith('class_labels_classifier'      ) and not key.startswith('bbox_predictor'      ):
            _a					:  Dict						=  state_dict.pop(__snake_case      )
            _a					:  Any						=  val

    # finally, create HuggingFace model and load state dict
   _a					:  Tuple						=  DetrForSegmentation(__snake_case      ) if is_panoptic else DetrForObjectDetection(__snake_case      )
   model.load_state_dict(__snake_case      )
   model.eval()

   # verify our conversion on an image
   _a					:  str						=  'coco_panoptic' if is_panoptic else 'coco_detection'
   _a					:  int						=  DetrImageProcessor(format=__snake_case      )

   _a					:  Dict						=  processor(images=prepare_img()		,	return_tensors='pt'      )
   _a					:  Optional[Any]						=  encoding['pixel_values']

   _a					:  int						=  detr(__snake_case      )
   _a					:  Tuple						=  model(__snake_case      )

   assert torch.allclose(outputs.logits		,	original_outputs['pred_logits']		,	atol=1E-3      )
   assert torch.allclose(outputs.pred_boxes		,	original_outputs['pred_boxes']		,	atol=1E-3      )
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks		,	original_outputs['pred_masks']		,	atol=1E-4      )
   print('Looks ok!'      )

   if pytorch_dump_folder_path is not None:
      # Save model and image processor
      logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''      )
      Path(__snake_case      ).mkdir(exist_ok=__snake_case      )
      model.save_pretrained(__snake_case      )
      processor.save_pretrained(__snake_case      )

   if push_to_hub:
      # Upload model and image processor to the hub
      logger.info('Uploading PyTorch model and image processor to the hub...'      )
      model.push_to_hub(f'''nielsr/{model_name}'''      )
      processor.push_to_hub(f'''nielsr/{model_name}'''      )


if __name__ == "__main__":
 UpperCAmelCase_      :		Any          =		argparse.ArgumentParser()

 parser.add_argument(
     "--model_name",
     default="detr-resnet-50",
     type=str,
     choices=["detr-resnet-50", "detr-resnet-101"],
     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."
 )
 parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
 UpperCAmelCase_      :		Optional[int]          =		parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''

def snake_case_      (					__snake_case  :							int = 200)			->					int:
   lowerCAmelCase_			=      [1, 2, 5, 10, 20, 50, 100, 200]
   lowerCAmelCase_			=      [0] * (pence + 1)
   lowerCAmelCase_			=      1  # base case: 1 way to make 0 pence

   for coin in coins:
      for i in range(__snake_case	,   pence + 1	,   1):
         number_of_ways[i] += number_of_ways[i - coin]
   return number_of_ways[pence]


if __name__ == "__main__":
 assert solution(2_00) == 7_36_82





