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__		:					List[Any]					   =					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__		:					Union[str, 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     _a				(      lowercase__  :			Optional[int]     ,  lowercase__  :			Dict     ,  lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		state_dict.pop(SCREAMING_SNAKE_CASE_ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		val







def     _a				(      lowercase__  :			Dict ):







     '''simple docstring'''


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

     return new_state_dict







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			Union[str, Any]=False ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		''
     if is_panoptic:
          SCREAMING_SNAKE_CASE__				:				int			=		'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__				:				str			=		state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
          SCREAMING_SNAKE_CASE__				:				Optional[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
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		in_proj_weight[:2_56, :]
          SCREAMING_SNAKE_CASE__				:				Dict			=		in_proj_bias[:2_56]
          SCREAMING_SNAKE_CASE__				:				List[str]			=		in_proj_weight[2_56:5_12, :]
          SCREAMING_SNAKE_CASE__				:				List[str]			=		in_proj_bias[2_56:5_12]
          SCREAMING_SNAKE_CASE__				:				List[str]			=		in_proj_weight[-2_56:, :]
          SCREAMING_SNAKE_CASE__				:				int			=		in_proj_bias[-2_56:]







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'http://images.cocodataset.org/val2017/000000039769.jpg'
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		Image.open(requests.get(SCREAMING_SNAKE_CASE_     ,  stream=SCREAMING_SNAKE_CASE_ ).raw )

     return im







@torch.no_grad()
def     _a				(      lowercase__  :			Any     ,  lowercase__  :			int ):







     '''simple docstring'''


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

     # load image processor
     SCREAMING_SNAKE_CASE__				:				List[str]			=		'coco_panoptic' if is_panoptic else 'coco_detection'
     SCREAMING_SNAKE_CASE__				:				Tuple			=		ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE_ )

     # prepare image
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		prepare_img()
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(images=SCREAMING_SNAKE_CASE_     ,  return_tensors='pt' )
     SCREAMING_SNAKE_CASE__				:				str			=		encoding['pixel_values']

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

     # load original model from torch hub
     SCREAMING_SNAKE_CASE__				:				Any			=		torch.hub.load('DeppMeng/ConditionalDETR'     ,  SCREAMING_SNAKE_CASE_     ,  pretrained=SCREAMING_SNAKE_CASE_ ).eval()
     SCREAMING_SNAKE_CASE__				:				str			=		conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               SCREAMING_SNAKE_CASE__				:				Any			=		'conditional_detr.' + src
          rename_key(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_ )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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
     SCREAMING_SNAKE_CASE__				:				Dict			=		'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__				:				Optional[Any]			=		state_dict.pop(SCREAMING_SNAKE_CASE_ )
                    SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    SCREAMING_SNAKE_CASE__				:				List[Any]			=		state_dict.pop(SCREAMING_SNAKE_CASE_ )
                    SCREAMING_SNAKE_CASE__				:				Dict			=		val
               elif key.startswith('bbox_attention' ) or key.startswith('mask_head' ):
                    continue
               else:
                    SCREAMING_SNAKE_CASE__				:				Optional[int]			=		state_dict.pop(SCREAMING_SNAKE_CASE_ )
                    SCREAMING_SNAKE_CASE__				:				Dict			=		val
          else:
               if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
                    SCREAMING_SNAKE_CASE__				:				Optional[int]			=		state_dict.pop(SCREAMING_SNAKE_CASE_ )
                    SCREAMING_SNAKE_CASE__				:				Dict			=		val
    # finally, create HuggingFace model and load state dict
     SCREAMING_SNAKE_CASE__				:				int			=		ConditionalDetrForSegmentation(SCREAMING_SNAKE_CASE_ ) if is_panoptic else ConditionalDetrForObjectDetection(SCREAMING_SNAKE_CASE_ )
     model.load_state_dict(SCREAMING_SNAKE_CASE_ )
     model.eval()
     model.push_to_hub(repo_id=SCREAMING_SNAKE_CASE_     ,  organization='DepuMeng'     ,  commit_message='Add model' )
     # verify our conversion
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		conditional_detr(SCREAMING_SNAKE_CASE_ )
     SCREAMING_SNAKE_CASE__				:				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__":
      SCREAMING_SNAKE_CASE__		:					List[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__		:					Optional[Any]					   =					parser.parse_args()
      convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




import requests

SCREAMING_SNAKE_CASE__		:					int						=					"https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		requests.get(_NEWS_API + bbc_news_api_key ).json()
     # each article in the list is a dict
     for i, article in enumerate(bbc_news_page['articles']     ,  1 ):
          print(f'''{i}.) {article['title']}''' )


if __name__ == "__main__":
      fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")



