

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_     :    Optional[Any]     		=   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_     :    List[Any]     		=   []
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     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Any						)		->				Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      state_dict.pop(__lowercase						)
 SCREAMING_SNAKE_CASE		    =      val


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('backbone.0.body'		,				'backbone.conv_encoder.model'						)
   SCREAMING_SNAKE_CASE		    =      value
  else:
   SCREAMING_SNAKE_CASE		    =      value

 return new_state_dict


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	int=False						)		->				List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      ''''''
 if is_panoptic:
  SCREAMING_SNAKE_CASE		    =      '''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)
  SCREAMING_SNAKE_CASE		    =      state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""						)
  SCREAMING_SNAKE_CASE		    =      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
  SCREAMING_SNAKE_CASE		    =      in_proj_weight[:2_56, :]
  SCREAMING_SNAKE_CASE		    =      in_proj_bias[:2_56]
  SCREAMING_SNAKE_CASE		    =      in_proj_weight[2_56:5_12, :]
  SCREAMING_SNAKE_CASE		    =      in_proj_bias[2_56:5_12]
  SCREAMING_SNAKE_CASE		    =      in_proj_weight[-2_56:, :]
  SCREAMING_SNAKE_CASE		    =      in_proj_bias[-2_56:]


def 							__lowerCAmelCase     (							)		->				List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 SCREAMING_SNAKE_CASE		    =      Image.open(requests.get(__lowercase		,				stream=__lowercase						).raw						)

 return im


@torch.no_grad()
def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[str]		,				_UpperCamelCase		:	Optional[Any]						)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      ConditionalDetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  SCREAMING_SNAKE_CASE		    =      '''resnet101'''
 if "dc5" in model_name:
  SCREAMING_SNAKE_CASE		    =      True
 SCREAMING_SNAKE_CASE		    =      '''panoptic''' in model_name
 if is_panoptic:
  SCREAMING_SNAKE_CASE		    =      2_50
 else:
  SCREAMING_SNAKE_CASE		    =      91
  SCREAMING_SNAKE_CASE		    =      '''huggingface/label-files'''
  SCREAMING_SNAKE_CASE		    =      '''coco-detection-id2label.json'''
  SCREAMING_SNAKE_CASE		    =      json.load(open(hf_hub_download(__lowercase		,				__lowercase		,				repo_type='dataset'						)		,				'r'						)						)
  SCREAMING_SNAKE_CASE		    =      {int(__lowercase						): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE		    =      idalabel
  SCREAMING_SNAKE_CASE		    =      {v: k for k, v in idalabel.items()}

 # load image processor
 SCREAMING_SNAKE_CASE		    =      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
 SCREAMING_SNAKE_CASE		    =      ConditionalDetrImageProcessor(format=__lowercase						)

 # prepare image
 SCREAMING_SNAKE_CASE		    =      prepare_img()
 SCREAMING_SNAKE_CASE		    =      image_processor(images=__lowercase		,				return_tensors='pt'						)
 SCREAMING_SNAKE_CASE		    =      encoding['''pixel_values''']

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

 # load original model from torch hub
 SCREAMING_SNAKE_CASE		    =      torch.hub.load('DeppMeng/ConditionalDETR'		,				__lowercase		,				pretrained=__lowercase						).eval()
 SCREAMING_SNAKE_CASE		    =      conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   SCREAMING_SNAKE_CASE		    =      '''conditional_detr.''' + src
  rename_key(__lowercase		,				__lowercase		,				__lowercase						)
 SCREAMING_SNAKE_CASE		    =      rename_backbone_keys(__lowercase						)
 # query, key and value matrices need special treatment
 read_in_q_k_v(__lowercase		,				is_panoptic=__lowercase						)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 SCREAMING_SNAKE_CASE		    =      '''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'						)
   ):
    SCREAMING_SNAKE_CASE		    =      state_dict.pop(__lowercase						)
    SCREAMING_SNAKE_CASE		    =      val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    SCREAMING_SNAKE_CASE		    =      state_dict.pop(__lowercase						)
    SCREAMING_SNAKE_CASE		    =      val
   elif key.startswith('bbox_attention'						) or key.startswith('mask_head'						):
    continue
   else:
    SCREAMING_SNAKE_CASE		    =      state_dict.pop(__lowercase						)
    SCREAMING_SNAKE_CASE		    =      val
  else:
   if not key.startswith('class_labels_classifier'						) and not key.startswith('bbox_predictor'						):
    SCREAMING_SNAKE_CASE		    =      state_dict.pop(__lowercase						)
    SCREAMING_SNAKE_CASE		    =      val
    # finally, create HuggingFace model and load state dict
 SCREAMING_SNAKE_CASE		    =      ConditionalDetrForSegmentation(__lowercase						) if is_panoptic else ConditionalDetrForObjectDetection(__lowercase						)
 model.load_state_dict(__lowercase						)
 model.eval()
 model.push_to_hub(repo_id=__lowercase		,				organization='DepuMeng'		,				commit_message='Add model'						)
 # verify our conversion
 SCREAMING_SNAKE_CASE		    =      conditional_detr(__lowercase						)
 SCREAMING_SNAKE_CASE		    =      model(__lowercase						)
 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(__lowercase						).mkdir(exist_ok=__lowercase						)
 model.save_pretrained(__lowercase						)
 image_processor.save_pretrained(__lowercase						)


if __name__ == "__main__":
      a_     :    Dict     		=   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_     :    str     		=   parser.parse_args()
      convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_UpperCamelCase			:	Union[str, Any]								={'UserAgent': UserAgent().random}



def  a__ (__lowercase							:Optional[Any]   )		-> dict:
				_A	:				str   =      script.contents[0]
				_A	:				Dict   =      json.loads(data[data.find('''{"config"'''   ) : -1]   )
				return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]






class 						UpperCAmelCase__ :







		def __init__(					self       ,A__		):
						_A	:				Any   =      f"""https://www.instagram.com/{username}/"""
						_A	:				Optional[Any]   =      self.get_json()







		def  A__      (					self		):
						_A	:				str   =      requests.get(self.url       ,headers=A__		).text
						_A	:				Dict   =      BeautifulSoup(A__       ,'''html.parser'''		).find_all('''script'''		)
						try:
										return extract_user_profile(scripts[4]		)
						except (json.decoder.JSONDecodeError, KeyError):
										return extract_user_profile(scripts[3]		)







		def __repr__(					self		):
						return f"""{self.__class__.__name__}('{self.username}')"""







		def __str__(					self		):
						return f"""{self.fullname} ({self.username}) is {self.biography}"""







		@property
		def  A__      (					self		):
						return self.user_data["username"]







		@property
		def  A__      (					self		):
						return self.user_data["full_name"]







		@property
		def  A__      (					self		):
						return self.user_data["biography"]







		@property
		def  A__      (					self		):
						return self.user_data["business_email"]







		@property
		def  A__      (					self		):
						return self.user_data["external_url"]







		@property
		def  A__      (					self		):
						return self.user_data["edge_followed_by"]["count"]







		@property
		def  A__      (					self		):
						return self.user_data["edge_follow"]["count"]







		@property
		def  A__      (					self		):
						return self.user_data["edge_owner_to_timeline_media"]["count"]







		@property
		def  A__      (					self		):
						return self.user_data["profile_pic_url_hd"]







		@property
		def  A__      (					self		):
						return self.user_data["is_verified"]







		@property
		def  A__      (					self		):
						return self.user_data["is_private"]



def  a__ (__lowercase							:str = "github"   )		-> None:
				import os

				if os.environ.get('''CI'''   ):
								return  # test failing on GitHub Actions
				_A	:				Optional[int]   =      InstagramUser(__lowercase   )
				assert instagram_user.user_data
				assert isinstance(instagram_user.user_data					,  __lowercase   )
				assert instagram_user.username == username
				if username != "github":
								return
				assert instagram_user.fullname == "GitHub"
				assert instagram_user.biography == "Built for developers."
				assert instagram_user.number_of_posts > 150
				assert instagram_user.number_of_followers > 12_0000
				assert instagram_user.number_of_followings > 15
				assert instagram_user.email == "support@github.com"
				assert instagram_user.website == "https://github.com/readme"
				assert instagram_user.profile_picture_url.startswith('''https://instagram.'''   )
				assert instagram_user.is_verified is True
				assert instagram_user.is_private is False


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_UpperCamelCase			:	List[Any]								=InstagramUser('github')
	print(instagram_user)
	print(f'''{instagram_user.number_of_posts = }''')
	print(f'''{instagram_user.number_of_followers = }''')
	print(f'''{instagram_user.number_of_followings = }''')
	print(f'''{instagram_user.email = }''')
	print(f'''{instagram_user.website = }''')
	print(f'''{instagram_user.profile_picture_url = }''')
	print(f'''{instagram_user.is_verified = }''')
	print(f'''{instagram_user.is_private = }''')



