





"""simple docstring"""






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()
A 			=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A 			=  []
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     __A      (			a_						:List[Any]   ,       a_						:Dict   ,       a_						:List[str])		->		str:
				__a :						List[Any]			       =  state_dict.pop(a_)
				__a :						str			       =  val

def     __A      (			a_						:Union[str, Any])		->		str:
				__a :						List[Any]			       =  OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												__a :						List[Any]			       =  key.replace('''backbone.0.body'''   ,       '''backbone.conv_encoder.model''')
												__a :						Optional[Any]			       =  value
								else:
												__a :						Any			       =  value

				return new_state_dict

def     __A      (			a_						:Union[str, Any]   ,       a_						:Optional[int]=False)		->		List[Any]:
				__a :						Union[str, Any]			       =  ''''''
				if is_panoptic:
								__a :						Union[str, Any]			       =  '''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)
								__a :						Any			       =  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""")
								__a :						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 :						List[str]			       =  in_proj_weight[:2_56, :]
								__a :						Optional[Any]			       =  in_proj_bias[:2_56]
								__a :						Optional[Any]			       =  in_proj_weight[2_56:5_12, :]
								__a :						Optional[Any]			       =  in_proj_bias[2_56:5_12]
								__a :						Tuple			       =  in_proj_weight[-2_56:, :]
								__a :						List[Any]			       =  in_proj_bias[-2_56:]

def     __A      (			)		->		Tuple:
				__a :						Union[str, Any]			       =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__a :						Any			       =  Image.open(requests.get(a_   ,       stream=a_).raw)

				return im

@torch.no_grad()
def     __A      (			a_						:Optional[Any]   ,       a_						:List[str])		->		Union[str, Any]:
				__a :						str			       =  ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								__a :						Dict			       =  '''resnet101'''
				if "dc5" in model_name:
								__a :						List[str]			       =  True
				__a :						Optional[Any]			       =  '''panoptic''' in model_name
				if is_panoptic:
								__a :						Dict			       =  2_50
				else:
								__a :						str			       =  91
								__a :						Optional[Any]			       =  '''huggingface/label-files'''
								__a :						int			       =  '''coco-detection-id2label.json'''
								__a :						Dict			       =  json.load(open(hf_hub_download(a_   ,       a_   ,       repo_type='''dataset''')   ,       '''r'''))
								__a :						List[str]			       =  {int(a_): v for k, v in idalabel.items()}
								__a :						int			       =  idalabel
								__a :						Optional[int]			       =  {v: k for k, v in idalabel.items()}

				# load image processor
				__a :						Union[str, Any]			       =  '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				__a :						str			       =  ConditionalDetrImageProcessor(format=a_)

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

				logger.info(F"""Converting model {model_name}...""")

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


if __name__ == "__main__":
		A 			=  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.'''
		)
		A 			=  parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






"""simple docstring"""






from __future__ import annotations

import typing
from collections import Counter

def     __A      (			a_						:int)		->		typing.Counter[int]:
				__a :						typing.Counter[int]			       =  Counter()
				for base in range(1   ,       max_perimeter + 1):
								for perpendicular in range(a_   ,       max_perimeter + 1):
												__a :						Any			       =  (base * base + perpendicular * perpendicular) ** 0.5
												if hypotenuse == int(a_):
																__a :						List[Any]			       =  int(base + perpendicular + hypotenuse)
																if perimeter > max_perimeter:
																				continue
																triplets[perimeter] += 1
				return triplets

def     __A      (			a_						:int = 10_00)		->		int:
				__a :						Dict			       =  pythagorean_triple(a_)
				return triplets.most_common(1)[0][0]


if __name__ == "__main__":
		print(F'Perimeter {solution()} has maximum solutions')