


'''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()
a_  :				Optional[Any]						= logging.get_logger(__name__)





def __snake_case					(      UpperCAmelCase_						:						List[Any]		):
 # initialize config
 if "resnet-50" in model_name:
  lowerCamelCase_      =  ResNetConfig.from_pretrained("microsoft/resnet-50"		)
 elif "resnet-101" in model_name:
  lowerCamelCase_      =  ResNetConfig.from_pretrained("microsoft/resnet-101"		)
 else:
  raise ValueError("Model name should include either resnet50 or resnet101"		)

 lowerCamelCase_      =  DetrConfig(use_timm_backbone=UpperCAmelCase_					,				backbone_config=UpperCAmelCase_		)

 # set label attributes
 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()}

 return config, is_panoptic





def __snake_case					(      UpperCAmelCase_						:						List[str]		):
 # here we list all keys to be renamed (original name on the left, our name on the right)
 lowerCamelCase_      =  []

 # 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 __snake_case					(      UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Optional[Any]		):
 lowerCamelCase_      =  state_dict.pop(UpperCAmelCase_		)
 lowerCamelCase_      =  val





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						List[Any]=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 __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 __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						List[Any]=None					,				UpperCAmelCase_						:						Optional[Any]=False		):
 lowerCamelCase_ ,lowerCamelCase_      =  get_detr_config(UpperCAmelCase_		)

 # load original model from torch hub
 lowerCamelCase_      =  {
     "detr-resnet-50": "detr_resnet50",
     "detr-resnet-101": "detr_resnet101",
 }
 logger.info(F'''Converting model {model_name}...'''		)
 lowerCamelCase_      =  torch.hub.load("facebookresearch/detr"					,				model_name_to_original_name[model_name]					,				pretrained=UpperCAmelCase_		).eval()
 lowerCamelCase_      =  detr.state_dict()
 # rename keys
 for src, dest in create_rename_keys(UpperCAmelCase_		):
  if is_panoptic:
   lowerCamelCase_      =  "detr." + src
  rename_key(UpperCAmelCase_					,				UpperCAmelCase_					,				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 on an image
 lowerCamelCase_      =  "coco_panoptic" if is_panoptic else "coco_detection"
 lowerCamelCase_      =  DetrImageProcessor(format=UpperCAmelCase_		)

 lowerCamelCase_      =  processor(images=prepare_img()					,				return_tensors="pt"		)
 lowerCamelCase_      =  encoding["pixel_values"]

 lowerCamelCase_      =  detr(UpperCAmelCase_		)
 lowerCamelCase_      =  model(UpperCAmelCase_		)

 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(UpperCAmelCase_		).mkdir(exist_ok=UpperCAmelCase_		)
  model.save_pretrained(UpperCAmelCase_		)
  processor.save_pretrained(UpperCAmelCase_		)

 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__":
     a_  :				Optional[int]						= 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.""")
     a_  :				Optional[Any]						= parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''

import os





def __snake_case					(      UpperCAmelCase_						:						str = "matrix.txt"		):
 with open(os.path.join(os.path.dirname(UpperCAmelCase_		)					,				UpperCAmelCase_		)		) as in_file:
  lowerCamelCase_      =  in_file.read()

 lowerCamelCase_      =  [[int(UpperCAmelCase_		) for cell in row.split(","		)] for row in data.strip().splitlines()]
 lowerCamelCase_      =  [[0 for cell in row] for row in grid]
 lowerCamelCase_      =  len(grid[0]		)

 lowerCamelCase_      =  [[0 for i in range(UpperCAmelCase_		)] for j in range(UpperCAmelCase_		)]
 lowerCamelCase_      =  grid[0][0]
 for i in range(1					,				UpperCAmelCase_		):
  lowerCamelCase_      =  grid[0][i] + dp[0][i - 1]
 for i in range(1					,				UpperCAmelCase_		):
  lowerCamelCase_      =  grid[i][0] + dp[i - 1][0]

 for i in range(1					,				UpperCAmelCase_		):
  for j in range(1					,				UpperCAmelCase_		):
   lowerCamelCase_      =  grid[i][j] + min(dp[i - 1][j]					,				dp[i][j - 1]		)

 return dp[-1][-1]


if __name__ == "__main__":
     print(f'''{solution() = }''')



