"""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()
SCREAMING_SNAKE_CASE__:int			=	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE__:int			=	[]
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 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  state_dict.pop(a_							)
       __a     				=  val



def 					_lowerCamelCase(					a							):
       __a     				=  OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     __a     				=  key.replace("backbone.0.body" ,  "backbone.conv_encoder.model"							)
                     __a     				=  value
              else:
                     __a     				=  value

       return new_state_dict



def 					_lowerCamelCase(					a ,  a=False							):
       __a     				=  ""
       if is_panoptic:
              __a     				=  "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     				=  state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"							)
              __a     				=  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     				=  in_proj_weight[:2_5_6, :]
              __a     				=  in_proj_bias[:2_5_6]
              __a     				=  in_proj_weight[2_5_6:5_1_2, :]
              __a     				=  in_proj_bias[2_5_6:5_1_2]
              __a     				=  in_proj_weight[-2_5_6:, :]
              __a     				=  in_proj_bias[-2_5_6:]



def 					_lowerCamelCase(					):
       __a     				=  "http://images.cocodataset.org/val2017/000000039769.jpg"
       __a     				=  Image.open(requests.get(a_ ,  stream=a_							).raw							)

       return im



@torch.no_grad()
def 					_lowerCamelCase(					a ,  a							):
       __a     				=  ConditionalDetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              __a     				=  "resnet101"
       if "dc5" in model_name:
              __a     				=  True
       __a     				=  "panoptic" in model_name
       if is_panoptic:
              __a     				=  2_5_0
       else:
              __a     				=  9_1
              __a     				=  "huggingface/label-files"
              __a     				=  "coco-detection-id2label.json"
              __a     				=  json.load(open(hf_hub_download(a_ ,  a_ ,  repo_type="dataset"							) ,  "r"							)							)
              __a     				=  {int(a_							): v for k, v in idalabel.items()}
              __a     				=  idalabel
              __a     				=  {v: k for k, v in idalabel.items()}

       # load image processor
       __a     				=  "coco_panoptic" if is_panoptic else "coco_detection"
       __a     				=  ConditionalDetrImageProcessor(format=a_							)

       # prepare image
       __a     				=  prepare_img()
       __a     				=  image_processor(images=a_ ,  return_tensors="pt"							)
       __a     				=  encoding["pixel_values"]

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

       # load original model from torch hub
       __a     				=  torch.hub.load("DeppMeng/ConditionalDETR" ,  a_ ,  pretrained=a_							).eval()
       __a     				=  conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     __a     				=  "conditional_detr." + src
              rename_key(a_ ,  a_ ,  a_							)
       __a     				=  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     				=  "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     				=  state_dict.pop(a_							)
                            __a     				=  val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            __a     				=  state_dict.pop(a_							)
                            __a     				=  val
                     elif key.startswith("bbox_attention"							) or key.startswith("mask_head"							):
                            continue
                     else:
                            __a     				=  state_dict.pop(a_							)
                            __a     				=  val
              else:
                     if not key.startswith("class_labels_classifier"							) and not key.startswith("bbox_predictor"							):
                            __a     				=  state_dict.pop(a_							)
                            __a     				=  val
    # finally, create HuggingFace model and load state dict
       __a     				=  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     				=  conditional_detr(a_							)
       __a     				=  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__":
     SCREAMING_SNAKE_CASE__:Any			=	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."""
     )
     SCREAMING_SNAKE_CASE__:Union[str, Any]			=	parser.parse_args()
     convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

"""simple docstring"""



import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

SCREAMING_SNAKE_CASE__:List[str]			=	3



def 					_lowerCamelCase(					a							):
       print("Generating primitive root of p"							)
       while True:
              __a     				=  random.randrange(3 ,  a							)
              if pow(a ,  2 ,  a							) == 1:
                     continue
              if pow(a ,  a ,  a							) == 1:
                     continue
              return g



def 					_lowerCamelCase(					a							):
       print("Generating prime p..."							)
       __a     				=  rabin_miller.generate_large_prime(a							)  # select large prime number.
       __a     				=  primitive_root(a							)  # one primitive root on modulo p.
       __a     				=  random.randrange(3 ,  a							)  # private_key -> have to be greater than 2 for safety.
       __a     				=  cryptomath.find_mod_inverse(pow(a ,  a ,  a							) ,  a							)

       __a     				=  (key_size, e_a, e_a, p)
       __a     				=  (key_size, d)

       return public_key, private_key



def 					_lowerCamelCase(					a ,  a							):
       if os.path.exists(F"{name}_pubkey.txt"							) or os.path.exists(F"{name}_privkey.txt"							):
              print("\nWARNING:"							)
              print(
                  F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
                  "Use a different name or delete these files and re-run this program."							)
              sys.exit()

       __a				, __a     				=  generate_key(a							)
       print(F"\nWriting public key to file {name}_pubkey.txt..."							)
       with open(F"{name}_pubkey.txt" ,  "w"							) as fo:
              fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"							)

       print(F"Writing private key to file {name}_privkey.txt..."							)
       with open(F"{name}_privkey.txt" ,  "w"							) as fo:
              fo.write(F"{private_key[0]},{private_key[1]}"							)



def 					_lowerCamelCase(					):
       print("Making key files..."							)
       make_key_files("elgamal" ,  2_0_4_8							)
       print("Key files generation successful"							)


if __name__ == "__main__":
   main()
