


'''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 			_lowercase       (				UpperCamelCase__       :					int			):
   # initialize config
   if "resnet-50" in model_name:
      __A  :		Any       =			ResNetConfig.from_pretrained('microsoft/resnet-50'			)
   elif "resnet-101" in model_name:
      __A  :		Tuple       =			ResNetConfig.from_pretrained('microsoft/resnet-101'			)
   else:
      raise ValueError('Model name should include either resnet50 or resnet101'			)

   __A  :		Dict       =			DetrConfig(use_timm_backbone=UpperCamelCase__,				backbone_config=UpperCamelCase__			)

   # set label attributes
   __A  :		int       =			'panoptic' in model_name
   if is_panoptic:
      __A  :		Union[str, Any]       =			250
   else:
      __A  :		int       =			91
      __A  :		str       =			'huggingface/label-files'
      __A  :		Optional[int]       =			'coco-detection-id2label.json'
      __A  :		Any       =			json.load(open(hf_hub_download(UpperCamelCase__,				UpperCamelCase__,				repo_type='dataset'			),				'r'			)			)
      __A  :		int       =			{int(UpperCamelCase__			): v for k, v in idalabel.items()}
      __A  :		int       =			idalabel
      __A  :		List[str]       =			{v: k for k, v in idalabel.items()}

   return config, is_panoptic


def 			_lowercase       (				UpperCamelCase__       :					Dict			):
   # here we list all keys to be renamed (original name on the left, our name on the right)
   __A  :		int       =			[]

   # 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 			_lowercase       (				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					Union[str, Any]			):
   __A  :		int       =			state_dict.pop(UpperCamelCase__			)
   __A  :		int       =			val


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					Tuple=False			):
   __A  :		Union[str, Any]       =			''
   if is_panoptic:
      __A  :		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)
      __A  :		List[Any]       =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""			)
      __A  :		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
      __A  :		Optional[Any]       =			in_proj_weight[:256, :]
      __A  :		Dict       =			in_proj_bias[:256]
      __A  :		Union[str, Any]       =			in_proj_weight[256:512, :]
      __A  :		int       =			in_proj_bias[256:512]
      __A  :		Tuple       =			in_proj_weight[-256:, :]
      __A  :		Union[str, Any]       =			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
      __A  :		Any       =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""			)
      __A  :		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
      __A  :		Optional[Any]       =			in_proj_weight[:256, :]
      __A  :		Optional[Any]       =			in_proj_bias[:256]
      __A  :		int       =			in_proj_weight[256:512, :]
      __A  :		Any       =			in_proj_bias[256:512]
      __A  :		List[Any]       =			in_proj_weight[-256:, :]
      __A  :		Dict       =			in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      __A  :		Dict       =			state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""			)
      __A  :		str       =			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  :		Any       =			in_proj_weight_cross_attn[:256, :]
      __A  :		Optional[int]       =			in_proj_bias_cross_attn[:256]
      __A  :		Union[str, Any]       =			in_proj_weight_cross_attn[256:512, :]
      __A  :		Any       =			in_proj_bias_cross_attn[256:512]
      __A  :		Any       =			in_proj_weight_cross_attn[-256:, :]
      __A  :		int       =			in_proj_bias_cross_attn[-256:]


def 			_lowercase       (				):
   __A  :		int       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
   __A  :		List[Any]       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)

   return im


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					Optional[int]=None,				UpperCamelCase__       :					str=False			):
   __A  :		List[Any]       =			get_detr_config(UpperCamelCase__			)

   # load original model from torch hub
   __A  :		List[Any]       =			{
       '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=UpperCamelCase__			).eval()
   __A  :		Optional[int]       =			detr.state_dict()
   # rename keys
   for src, dest in create_rename_keys(UpperCamelCase__			):
      if is_panoptic:
         __A  :		Optional[Any]       =			'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
   __A  :		Tuple       =			'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  :		List[str]       =			state_dict.pop(UpperCamelCase__			)
            __A  :		int       =			val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            __A  :		Any       =			state_dict.pop(UpperCamelCase__			)
            __A  :		int       =			val
         elif key.startswith('bbox_attention'			) or key.startswith('mask_head'			):
            continue
         else:
            __A  :		Optional[int]       =			state_dict.pop(UpperCamelCase__			)
            __A  :		List[str]       =			val
      else:
         if not key.startswith('class_labels_classifier'			) and not key.startswith('bbox_predictor'			):
            __A  :		Union[str, Any]       =			state_dict.pop(UpperCamelCase__			)
            __A  :		Optional[Any]       =			val

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

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

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

   __A  :		Any       =			detr(UpperCamelCase__			)
   __A  :		str       =			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__":
 UpperCAmelCase_ :     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.')
 UpperCAmelCase_ :     Dict								=      parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''
from __future__ import annotations


def 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 __A  :		Dict       =			[]
 __A     ,__A  :		Dict       =			input_list[low:mid], input_list[mid : high + 1]
 while left and right:
  result.append((left if left[0] <= right[0] else right).pop(0			)			)
 __A  :		Optional[Any]       =			result + left + right
 return input_list


def 			_lowercase       (				UpperCamelCase__       :					list			):
 if len(UpperCamelCase__			) <= 1:
  return input_list
 __A  :		Any       =			list(UpperCamelCase__			)

 # iteration for two-way merging
 __A  :		Dict       =			2
 while p <= len(UpperCamelCase__			):
  # getting low, high and middle value for merge-sort of single list
  for i in range(0,				len(UpperCamelCase__			),				UpperCamelCase__			):
   __A  :		Union[str, Any]       =			i
   __A  :		Tuple       =			i + p - 1
   __A  :		Union[str, Any]       =			(low + high + 1) // 2
   __A  :		Tuple       =			merge(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
  # final merge of last two parts
  if p * 2 >= len(UpperCamelCase__			):
   __A  :		Optional[int]       =			i
   __A  :		str       =			merge(UpperCamelCase__,				0,				UpperCamelCase__,				len(UpperCamelCase__			) - 1			)
   break
  p *= 2

 return input_list


if __name__ == "__main__":
 UpperCAmelCase_ :     Any								=      input('Enter numbers separated by a comma:\n').strip()
 if user_input == "":
  UpperCAmelCase_ :     Union[str, Any]								=      []
 else:
  UpperCAmelCase_ :     str								=      [int(item.strip()) for item in user_input.split(',')]
 print(iter_merge_sort(unsorted))






