



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


logging.set_verbosity_info()
_UpperCAmelCase       =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCAmelCase       =     []
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        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	List[str]            =state_dict.pop(lowercase	)
   SCREAMING_SNAKE_CASE_:	Dict            =val





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Any            =OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         SCREAMING_SNAKE_CASE_:	int            =key.replace("""backbone.0.body"""							,     """backbone.conv_encoder.model"""	)
         SCREAMING_SNAKE_CASE_:	List[str]            =value
      else:
         SCREAMING_SNAKE_CASE_:	Dict            =value

   return new_state_dict





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





def        __magic_name__			(							):
   SCREAMING_SNAKE_CASE_:	str            ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
   SCREAMING_SNAKE_CASE_:	Any            =Image.open(requests.get(lowercase							,     stream=lowercase	).raw	)

   return im





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

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

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

   # load original model from torch hub
   SCREAMING_SNAKE_CASE_:	Optional[int]            =torch.hub.load("""facebookresearch/detr"""							,     lowercase							,     pretrained=lowercase	).eval()
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         SCREAMING_SNAKE_CASE_:	List[str]            ="""detr.""" + src
      rename_key(lowercase							,     lowercase							,     lowercase	)
   SCREAMING_SNAKE_CASE_:	List[str]            =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_:	Optional[int]            ="""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"""	)
         ):
            SCREAMING_SNAKE_CASE_:	str            =state_dict.pop(lowercase	)
            SCREAMING_SNAKE_CASE_:	Union[str, Any]            =val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            SCREAMING_SNAKE_CASE_:	List[str]            =state_dict.pop(lowercase	)
            SCREAMING_SNAKE_CASE_:	List[Any]            =val
         elif key.startswith("""bbox_attention"""	) or key.startswith("""mask_head"""	):
            continue
         else:
            SCREAMING_SNAKE_CASE_:	Union[str, Any]            =state_dict.pop(lowercase	)
            SCREAMING_SNAKE_CASE_:	Tuple            =val
      else:
         if not key.startswith("""class_labels_classifier"""	) and not key.startswith("""bbox_predictor"""	):
            SCREAMING_SNAKE_CASE_:	Optional[int]            =state_dict.pop(lowercase	)
            SCREAMING_SNAKE_CASE_:	Tuple            =val
    # finally, create HuggingFace model and load state dict
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =DetrForSegmentation(lowercase	) if is_panoptic else DetrForObjectDetection(lowercase	)
   model.load_state_dict(lowercase	)
   model.eval()
   # verify our conversion
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =detr(lowercase	)
   SCREAMING_SNAKE_CASE_:	Tuple            =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__":
   _UpperCAmelCase       =     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."""
   )
   _UpperCAmelCase       =     parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""simple docstring"""
from __future__ import annotations

import sys
class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   int						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =letter
     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	dict[str, str]            ={}





  def __repr__(  self	:   Optional[Any]						)       ->   str:


     '''simple docstring'''




     return f'''{self.letter}:{self.freq}'''







class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Letter | TreeNode    ,						lowerCAmelCase	:   Letter | TreeNode						)       ->   List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =left
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =right







def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	dict[str, int]            ={}
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	List[Any]            =f.read(1	)
         if not c:
            break
         SCREAMING_SNAKE_CASE_:	Any            =chars[c] + 1 if c in chars else 1
   return sorted((Letter(lowercase							,     lowercase	) for c, f in chars.items())							,     key=lambda lowercase	: x.freq	)





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	list[Letter | TreeNode]            =letters  # type: ignore
   while len(lowercase	) > 1:
      SCREAMING_SNAKE_CASE_:	Any            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =left.freq + right.freq
      SCREAMING_SNAKE_CASE_:	Any            =TreeNode(lowercase							,     lowercase							,     lowercase	)
      response.append(lowercase	)
      response.sort(key=lambda lowercase	: x.freq	)
   return response[0]





def        __magic_name__			(							lowercase							,     lowercase	):
   if isinstance(lowercase							,     lowercase	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =bitstring
      return [root]
   SCREAMING_SNAKE_CASE_:	TreeNode            =root  # type: ignore
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[]
   letters += traverse_tree(treenode.left							,     bitstring + """0"""	)
   letters += traverse_tree(treenode.right							,     bitstring + """1"""	)
   return letters





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =parse_file(lowercase	)
   SCREAMING_SNAKE_CASE_:	str            =build_tree(lowercase	)
   SCREAMING_SNAKE_CASE_:	List[Any]            ={
       k: v for letter in traverse_tree(lowercase							,     """"""	) for k, v in letter.bitstring.items()
   }
   print(f'''Huffman Coding  of {file_path}: '''	)
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	Any            =f.read(1	)
         if not c:
            break
         print(letters[c]							,     end=""" """	)
   print()


if __name__ == "__main__":
   # pass the file path to the huffman function
   huffman(sys.argv[1])



