


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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_			       =      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_			       =      []
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}.cross_attn.out_proj.weight""",
               F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
           )
       )
       rename_keys.append(
           (
               F"""transformer.decoder.layers.{i}.cross_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"""))

       # q, k, v projections in self/cross-attention in decoder for conditional DETR
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
       )
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
       )
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
       )
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
       )
       rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
       )
       # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
       )
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
       )
       rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
       )

       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
       )
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
       )
       rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
       rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
       rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
       )
       # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
       )
       rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
       rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
       rename_keys.append(
           (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
       )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		):
 __lowerCamelCase						:				Any						   =		state_dict.pop(UpperCamelCase__		)
 __lowerCamelCase						:				Optional[int]						   =		val
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__		):
 __lowerCamelCase						:				Any						   =		OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   __lowerCamelCase						:				Optional[Any]						   =		key.replace('backbone.0.body'     ,			'backbone.conv_encoder.model'		)
   __lowerCamelCase						:				List[str]						   =		value
  else:
   __lowerCamelCase						:				int						   =		value

 return new_state_dict
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__=False		):
 __lowerCamelCase						:				Optional[int]						   =		''
 if is_panoptic:
  __lowerCamelCase						:				List[str]						   =		'conditional_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						:				int						   =		state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
  __lowerCamelCase						:				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
  __lowerCamelCase						:				List[str]						   =		in_proj_weight[:256, :]
  __lowerCamelCase						:				List[Any]						   =		in_proj_bias[:256]
  __lowerCamelCase						:				Any						   =		in_proj_weight[256:512, :]
  __lowerCamelCase						:				Any						   =		in_proj_bias[256:512]
  __lowerCamelCase						:				List[str]						   =		in_proj_weight[-256:, :]
  __lowerCamelCase						:				Tuple						   =		in_proj_bias[-256:]
def  UpperCamelCase__	(    ):
 __lowerCamelCase						:				Dict						   =		'http://images.cocodataset.org/val2017/000000039769.jpg'
 __lowerCamelCase						:				Union[str, Any]						   =		Image.open(requests.get(UpperCamelCase__     ,			stream=UpperCamelCase__		).raw		)

 return im
@torch.no_grad()
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		):
 __lowerCamelCase						:				int						   =		ConditionalDetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  __lowerCamelCase						:				Tuple						   =		'resnet101'
 if "dc5" in model_name:
  __lowerCamelCase						:				int						   =		True
 __lowerCamelCase						:				str						   =		'panoptic' in model_name
 if is_panoptic:
  __lowerCamelCase						:				List[str]						   =		250
 else:
  __lowerCamelCase						:				int						   =		91
  __lowerCamelCase						:				Tuple						   =		'huggingface/label-files'
  __lowerCamelCase						:				List[str]						   =		'coco-detection-id2label.json'
  __lowerCamelCase						:				Optional[Any]						   =		json.load(open(hf_hub_download(UpperCamelCase__     ,			UpperCamelCase__     ,			repo_type='dataset'		)     ,			'r'		)		)
  __lowerCamelCase						:				Optional[Any]						   =		{int(UpperCamelCase__		): v for k, v in idalabel.items()}
  __lowerCamelCase						:				Tuple						   =		idalabel
  __lowerCamelCase						:				Union[str, Any]						   =		{v: k for k, v in idalabel.items()}

 # load image processor
 __lowerCamelCase						:				List[Any]						   =		'coco_panoptic' if is_panoptic else 'coco_detection'
 __lowerCamelCase						:				Any						   =		ConditionalDetrImageProcessor(format=UpperCamelCase__		)

 # prepare image
 __lowerCamelCase						:				List[Any]						   =		prepare_img()
 __lowerCamelCase						:				Tuple						   =		image_processor(images=UpperCamelCase__     ,			return_tensors='pt'		)
 __lowerCamelCase						:				str						   =		encoding['pixel_values']

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

 # load original model from torch hub
 __lowerCamelCase						:				Dict						   =		torch.hub.load('DeppMeng/ConditionalDETR'     ,			UpperCamelCase__     ,			pretrained=UpperCamelCase__		).eval()
 __lowerCamelCase						:				int						   =		conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   __lowerCamelCase						:				List[str]						   =		'conditional_detr.' + src
  rename_key(UpperCamelCase__     ,			UpperCamelCase__     ,			UpperCamelCase__		)
 __lowerCamelCase						:				List[str]						   =		rename_backbone_keys(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						:				Optional[Any]						   =		'conditional_detr.model.' if is_panoptic else 'model.'
 for key in state_dict.copy().keys():
  if is_panoptic:
   if (
       key.startswith('conditional_detr'		)
       and not key.startswith('class_labels_classifier'		)
       and not key.startswith('bbox_predictor'		)
   ):
    __lowerCamelCase						:				Optional[Any]						   =		state_dict.pop(UpperCamelCase__		)
    __lowerCamelCase						:				List[str]						   =		val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    __lowerCamelCase						:				Tuple						   =		state_dict.pop(UpperCamelCase__		)
    __lowerCamelCase						:				List[str]						   =		val
   elif key.startswith('bbox_attention'		) or key.startswith('mask_head'		):
    continue
   else:
    __lowerCamelCase						:				Tuple						   =		state_dict.pop(UpperCamelCase__		)
    __lowerCamelCase						:				Optional[Any]						   =		val
  else:
   if not key.startswith('class_labels_classifier'		) and not key.startswith('bbox_predictor'		):
    __lowerCamelCase						:				Dict						   =		state_dict.pop(UpperCamelCase__		)
    __lowerCamelCase						:				str						   =		val
    # finally, create HuggingFace model and load state dict
 __lowerCamelCase						:				int						   =		ConditionalDetrForSegmentation(UpperCamelCase__		) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase__		)
 model.load_state_dict(UpperCamelCase__		)
 model.eval()
 model.push_to_hub(repo_id=UpperCamelCase__     ,			organization='DepuMeng'     ,			commit_message='Add model'		)
 # verify our conversion
 __lowerCamelCase						:				List[str]						   =		conditional_detr(UpperCamelCase__		)
 __lowerCamelCase						:				Dict						   =		model(UpperCamelCase__		)
 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(UpperCamelCase__		).mkdir(exist_ok=UpperCamelCase__		)
 model.save_pretrained(UpperCamelCase__		)
 image_processor.save_pretrained(UpperCamelCase__		)


if __name__ == "__main__":
       lowercase_			       =      argparse.ArgumentParser()

       parser.add_argument(
           '--model_name',
           default='conditional_detr_resnet50',
           type=str,
           help='Name of the CONDITIONAL_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.'
       )
       lowercase_			       =      parser.parse_args()
       convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








import math


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->list:
       A__		   =		[True] * n
       A__		   =		False
       A__		   =		False
       A__		   =		True

       for i in range(3					,	int(n**0.5 + 1    )					,	2    ):
              A__		   =		i * 2
              while index < n:
                     A__		   =		False
                     A__		   =		index + i

       A__		   =		[2]

       for i in range(3					,	UpperCamelCase__					,	2    ):
              if is_prime[i]:
                     primes.append(UpperCamelCase__    )

       return primes


def       UpperCamelCase__(			UpperCamelCase__     :			int = 99_99_66_66_33_33    )->int:
       A__		   =		math.floor(math.sqrt(UpperCamelCase__    )    ) + 1_00
       A__		   =		prime_sieve(UpperCamelCase__    )

       A__		   =		0
       A__		   =		0
       A__		   =		primes[prime_index]

       while (last_prime**2) <= limit:
              A__		   =		primes[prime_index + 1]

              A__		   =		last_prime**2
              A__		   =		next_prime**2

              # Get numbers divisible by lps(current)
              A__		   =		lower_bound + last_prime
              while upper_bound > current <= limit:
                     matches_sum += current
                     current += last_prime

              # Reset the upper_bound
              while (upper_bound - next_prime) > limit:
                     upper_bound -= next_prime

              # Add the numbers divisible by ups(current)
              A__		   =		upper_bound - next_prime
              while current > lower_bound:
                     matches_sum += current
                     current -= next_prime

              # Remove the numbers divisible by both ups and lps
              A__		   =		0
              while upper_bound > current <= limit:
                     if current <= lower_bound:
                            # Increment the current number
                            current += last_prime * next_prime
                            continue

                     if current > limit:
                            break

                     # Remove twice since it was added by both ups and lps
                     matches_sum -= current * 2

                     # Increment the current number
                     current += last_prime * next_prime

              # Setup for next pair
              A__		   =		next_prime
              prime_index += 1

       return matches_sum


if __name__ == "__main__":
       print(solution())





