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()
lowercase_  : Tuple								=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_  : Optional[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__   (						snake_case_			:		int							,					snake_case_			:		Optional[int]							,					snake_case_			:		List[str]   ):
 SCREAMING_SNAKE_CASE__: Optional[Any]=   state_dict.pop(snake_case_   )
 SCREAMING_SNAKE_CASE__: int=   val







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

 return new_state_dict







def       A__   (						snake_case_			:		Tuple							,					snake_case_			:		Dict=False   ):
 SCREAMING_SNAKE_CASE__: str=   ''''''
 if is_panoptic:
  SCREAMING_SNAKE_CASE__: Dict=   '''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__: Union[str, Any]=   state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'   )
  SCREAMING_SNAKE_CASE__: List[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
  SCREAMING_SNAKE_CASE__: Optional[int]=   in_proj_weight[:256, :]
  SCREAMING_SNAKE_CASE__: Any=   in_proj_bias[:256]
  SCREAMING_SNAKE_CASE__: Optional[Any]=   in_proj_weight[256:512, :]
  SCREAMING_SNAKE_CASE__: List[Any]=   in_proj_bias[256:512]
  SCREAMING_SNAKE_CASE__: List[Any]=   in_proj_weight[-256:, :]
  SCREAMING_SNAKE_CASE__: List[Any]=   in_proj_bias[-256:]







def       A__   (						):
 SCREAMING_SNAKE_CASE__: int=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 SCREAMING_SNAKE_CASE__: Dict=   Image.open(requests.get(snake_case_							,					stream=snake_case_   ).raw   )

 return im







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

 # load image processor
 SCREAMING_SNAKE_CASE__: Optional[int]=   '''coco_panoptic''' if is_panoptic else '''coco_detection'''
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   ConditionalDetrImageProcessor(format=snake_case_   )

 # prepare image
 SCREAMING_SNAKE_CASE__: Dict=   prepare_img()
 SCREAMING_SNAKE_CASE__: str=   image_processor(images=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__: Optional[int]=   torch.hub.load('''DeppMeng/ConditionalDETR'''							,					snake_case_							,					pretrained=snake_case_   ).eval()
 SCREAMING_SNAKE_CASE__: List[str]=   conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   SCREAMING_SNAKE_CASE__: Optional[Any]=   '''conditional_detr.''' + src
  rename_key(snake_case_							,					snake_case_							,					snake_case_   )
 SCREAMING_SNAKE_CASE__: Tuple=   rename_backbone_keys(snake_case_   )
 # query, key and value matrices need special treatment
 read_in_q_k_v(snake_case_							,					is_panoptic=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__: List[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'''   )
   ):
    SCREAMING_SNAKE_CASE__: Dict=   state_dict.pop(snake_case_   )
    SCREAMING_SNAKE_CASE__: int=   val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    SCREAMING_SNAKE_CASE__: Dict=   state_dict.pop(snake_case_   )
    SCREAMING_SNAKE_CASE__: int=   val
   elif key.startswith('''bbox_attention'''   ) or key.startswith('''mask_head'''   ):
    continue
   else:
    SCREAMING_SNAKE_CASE__: Union[str, Any]=   state_dict.pop(snake_case_   )
    SCREAMING_SNAKE_CASE__: int=   val
  else:
   if not key.startswith('''class_labels_classifier'''   ) and not key.startswith('''bbox_predictor'''   ):
    SCREAMING_SNAKE_CASE__: Dict=   state_dict.pop(snake_case_   )
    SCREAMING_SNAKE_CASE__: str=   val
    # finally, create HuggingFace model and load state dict
 SCREAMING_SNAKE_CASE__: List[Any]=   ConditionalDetrForSegmentation(snake_case_   ) if is_panoptic else ConditionalDetrForObjectDetection(snake_case_   )
 model.load_state_dict(snake_case_   )
 model.eval()
 model.push_to_hub(repo_id=snake_case_							,					organization='''DepuMeng'''							,					commit_message='''Add model'''   )
 # verify our conversion
 SCREAMING_SNAKE_CASE__: Any=   conditional_detr(snake_case_   )
 SCREAMING_SNAKE_CASE__: Any=   model(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(snake_case_   ).mkdir(exist_ok=snake_case_   )
 model.save_pretrained(snake_case_   )
 image_processor.save_pretrained(snake_case_   )


if __name__ == "__main__":
      lowercase_  : Union[str, 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.'
      )
      lowercase_  : Optional[Any]								=					parser.parse_args()
      convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



def    a      ( a	)     ->List[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= len(a	)
			while cur > 1:
						# Find the maximum number in arr
						SCREAMING_SNAKE_CASE     						= arr.index(max(arr[0:cur]	)	)
						# Reverse from 0 to mi
						SCREAMING_SNAKE_CASE     						= arr[mi::-1] + arr[mi + 1 : len(a	)]
						# Reverse whole list
						SCREAMING_SNAKE_CASE     						= arr[cur - 1 :: -1] + arr[cur : len(a	)]
						cur -= 1
			return arr


if __name__ == "__main__":
							__lowerCAmelCase					   =       input('Enter numbers separated by a comma:\n').strip()
							__lowerCAmelCase					   =       [int(item) for item in user_input.split(',')]
							print(pancake_sort(unsorted))