
import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase						:      List[str]							=							{"UserAgent": UserAgent().random}
lowercase						:      List[str]							=							"https://www.mywaifulist.moe/random"







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					None:
       lowercase							:  Dict 			=   requests.get(snake_case_      ,			headers=snake_case_       )
       with open(snake_case_      ,			"""wb"""       ) as file:
              file.write(image.content       )







def 		_snake_case(						)				->					tuple[str, str, str]:
       lowercase							:  Union[str, Any] 			=   BeautifulSoup(requests.get(snake_case_      ,			headers=snake_case_       ).text      ,			"""html.parser"""       )
       lowercase							:  int 			=   soup.find("""meta"""      ,			attrs={"""property""": """og:title"""}       ).attrs['''content''']
       lowercase							:  int 			=   soup.find("""meta"""      ,			attrs={"""property""": """og:image"""}       ).attrs['''content''']
       lowercase							:  List[Any] 			=   soup.find("""p"""      ,			id="""description"""       ).get_text()
       lowercase							:  Union[str, Any] 			=   os.path.splitext(os.path.basename(snake_case_       )       )
       lowercase							:  List[Any] 			=   title.strip().replace(""" """      ,			"""_"""       )
       lowercase							:  int 			=   f"{image_title}{image_extension}"
       save_image(snake_case_      ,			snake_case_       )
       return (title, description, image_title)


if __name__ == "__main__":
       lowercase						:      List[str]							=							random_anime_character()
       print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')


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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase						:      Union[str, Any]							=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase						:      Tuple							=							[]
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}.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"""),
    ]
)







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       lowercase							:  Optional[int] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
       lowercase							:  str 			=   val







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  int 			=   OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowercase							:  Optional[Any] 			=   key.replace("""backbone.0.body"""      ,			"""backbone.conv_encoder.model"""       )
                     lowercase							:  Optional[int] 			=   value
              else:
                     lowercase							:  Dict 			=   value

       return new_state_dict







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







def 		_snake_case(						)				->					str:
       lowercase							:  Tuple 			=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:  int 			=   Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,			stream=SCREAMING_SNAKE_CASE__       ).raw       )

       return im







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

       # load image processor
       lowercase							:  Any 			=   """coco_panoptic""" if is_panoptic else """coco_detection"""
       lowercase							:  Dict 			=   DetrImageProcessor(format=SCREAMING_SNAKE_CASE__       )

       # prepare image
       lowercase							:  Optional[Any] 			=   prepare_img()
       lowercase							:  int 			=   image_processor(images=SCREAMING_SNAKE_CASE__      ,			return_tensors="""pt"""       )
       lowercase							:  int 			=   encoding["""pixel_values"""]

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

       # load original model from torch hub
       lowercase							:  Optional[int] 			=   torch.hub.load("""facebookresearch/detr"""      ,			SCREAMING_SNAKE_CASE__      ,			pretrained=SCREAMING_SNAKE_CASE__       ).eval()
       lowercase							:  Optional[int] 			=   detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowercase							:  Optional[Any] 			=   """detr.""" + src
              rename_key(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
       lowercase							:  str 			=   rename_backbone_keys(SCREAMING_SNAKE_CASE__       )
       # query, key and value matrices need special treatment
       read_in_q_k_v(SCREAMING_SNAKE_CASE__      ,			is_panoptic=SCREAMING_SNAKE_CASE__       )
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       lowercase							:  List[Any] 			=   """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"""       )
                     ):
                            lowercase							:  Optional[Any] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Optional[Any] 			=   val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowercase							:  Tuple 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  str 			=   val
                     elif key.startswith("""bbox_attention"""       ) or key.startswith("""mask_head"""       ):
                            continue
                     else:
                            lowercase							:  Any 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Dict 			=   val
              else:
                     if not key.startswith("""class_labels_classifier"""       ) and not key.startswith("""bbox_predictor"""       ):
                            lowercase							:  List[str] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Union[str, Any] 			=   val
    # finally, create HuggingFace model and load state dict
       lowercase							:  int 			=   DetrForSegmentation(SCREAMING_SNAKE_CASE__       ) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__       )
       model.load_state_dict(SCREAMING_SNAKE_CASE__       )
       model.eval()
       # verify our conversion
       lowercase							:  Union[str, Any] 			=   detr(SCREAMING_SNAKE_CASE__       )
       lowercase							:  int 			=   model(SCREAMING_SNAKE_CASE__       )
       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(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
       model.save_pretrained(SCREAMING_SNAKE_CASE__       )
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
  lowercase						:      Any							=							argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""", default="""detr_resnet50""", type=str, 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."""
  )
  lowercase						:      List[Any]							=							parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
