


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					:       str			    =     logging.get_logger(__name__)



def UpperCamelCase_				(				__a      )	->   Any:
      # initialize config
      if "resnet-50" in model_name:
            a__	:  int     =   ResNetConfig.from_pretrained("microsoft/resnet-50"      )
      elif "resnet-101" in model_name:
            a__	:  Optional[int]     =   ResNetConfig.from_pretrained("microsoft/resnet-101"      )
      else:
            raise ValueError("Model name should include either resnet50 or resnet101"      )

      a__	:  Tuple     =   DetrConfig(use_timm_backbone=__a			,				backbone_config=__a      )

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

      return config, is_panoptic



def UpperCamelCase_				(				__a      )	->   Dict:
      # here we list all keys to be renamed (original name on the left, our name on the right)
      a__	:  List[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      )	->   int:
      a__	:  Tuple     =   state_dict.pop(__a      )
      a__	:  Any     =   val



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



def UpperCamelCase_				(				)	->   int:
      a__	:  Any     =   "http://images.cocodataset.org/val2017/000000039769.jpg"
      a__	:  Optional[Any]     =   Image.open(requests.get(__a			,				stream=__a      ).raw      )

      return im



@torch.no_grad()
def UpperCamelCase_				(				__a			,				__a=None			,				__a=False      )	->   Optional[int]:
      a__,	a__	:  List[str]     =   get_detr_config(__a      )

      # load original model from torch hub
      a__	:  str     =   {
          "detr-resnet-50": "detr_resnet50",
          "detr-resnet-101": "detr_resnet101",
      }
      logger.info(f'''Converting model {model_name}...'''      )
      a__	:  int     =   torch.hub.load("facebookresearch/detr"			,				model_name_to_original_name[model_name]			,				pretrained=__a      ).eval()
      a__	:  Dict     =   detr.state_dict()
      # rename keys
      for src, dest in create_rename_keys(__a      ):
            if is_panoptic:
                  a__	:  Dict     =   "detr." + src
            rename_key(__a			,				__a			,				__a      )
      # query, key and value matrices need special treatment
      read_in_q_k_v(__a			,				is_panoptic=__a      )
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      a__	:  Optional[int]     =   "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__	:  Any     =   state_dict.pop(__a      )
                        a__	:  List[str]     =   val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        a__	:  Optional[Any]     =   state_dict.pop(__a      )
                        a__	:  int     =   val
                  elif key.startswith("bbox_attention"      ) or key.startswith("mask_head"      ):
                        continue
                  else:
                        a__	:  Dict     =   state_dict.pop(__a      )
                        a__	:  Optional[int]     =   val
            else:
                  if not key.startswith("class_labels_classifier"      ) and not key.startswith("bbox_predictor"      ):
                        a__	:  Tuple     =   state_dict.pop(__a      )
                        a__	:  Union[str, Any]     =   val

    # finally, create HuggingFace model and load state dict
      a__	:  Optional[Any]     =   DetrForSegmentation(__a      ) if is_panoptic else DetrForObjectDetection(__a      )
      model.load_state_dict(__a      )
      model.eval()

      # verify our conversion on an image
      a__	:  Optional[int]     =   "coco_panoptic" if is_panoptic else "coco_detection"
      a__	:  Optional[int]     =   DetrImageProcessor(format=__a      )

      a__	:  Union[str, Any]     =   processor(images=prepare_img()			,				return_tensors="pt"      )
      a__	:  List[str]     =   encoding["pixel_values"]

      a__	:  Tuple     =   detr(__a      )
      a__	:  List[str]     =   model(__a      )

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

      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					:       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					:       List[Any]			    =     parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''







import math





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	str									=   []
				a_		:	Tuple									=   2
				a_		:	Optional[Any]									=   int(math.sqrt(__A   )   )  # Size of every segment
				a_		:	Optional[Any]									=   [True] * (end + 1)
				a_		:	Union[str, Any]									=   []

				while start <= end:
								if temp[start] is True:
												in_prime.append(__A   )
												for i in range(start * start    ,		end + 1    ,		__A   ):
																a_		:	List[Any]									=   False
								start += 1
				prime += in_prime

				a_		:	Any									=   end + 1
				a_		:	Optional[Any]									=   min(2 * end    ,		__A   )

				while low <= n:
								a_		:	Optional[Any]									=   [True] * (high - low + 1)
								for each in in_prime:
												a_		:	Any									=   math.floor(low / each   ) * each
												if t < low:
																t += each

												for j in range(__A    ,		high + 1    ,		__A   ):
																a_		:	Union[str, Any]									=   False

								for j in range(len(__A   )   ):
												if temp[j] is True:
																prime.append(j + low   )

								a_		:	Tuple									=   high + 1
								a_		:	str									=   min(high + end    ,		__A   )

				return prime




print(sieve(10**6))




