


'''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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__A		=[]
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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
   UpperCAmelCase__					:   Optional[Any]       				=       state_dict.pop(lowerCAmelCase__   )
   UpperCAmelCase__					:   int       				=       val







def       _UpperCamelCase (						UpperCamelCase__   ):
   UpperCAmelCase__					:   List[Any]       				=       OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         UpperCAmelCase__					:   Optional[int]       				=       key.replace("""backbone.0.body"""	, """backbone.conv_encoder.model"""   )
         UpperCAmelCase__					:   str       				=       value
      else:
         UpperCAmelCase__					:   int       				=       value

   return new_state_dict







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__=False   ):
   UpperCAmelCase__					:   Any       				=       ''''''
   if is_panoptic:
      UpperCAmelCase__					:   Optional[Any]       				=       '''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)
      UpperCAmelCase__					:   List[Any]       				=       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''   )
      UpperCAmelCase__					:   Tuple       				=       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
      UpperCAmelCase__					:   List[str]       				=       in_proj_weight[:2_5_6, :]
      UpperCAmelCase__					:   str       				=       in_proj_bias[:2_5_6]
      UpperCAmelCase__					:   Optional[Any]       				=       in_proj_weight[2_5_6:5_1_2, :]
      UpperCAmelCase__					:   List[Any]       				=       in_proj_bias[2_5_6:5_1_2]
      UpperCAmelCase__					:   Optional[Any]       				=       in_proj_weight[-2_5_6:, :]
      UpperCAmelCase__					:   int       				=       in_proj_bias[-2_5_6:]







def       _UpperCamelCase (						):
   UpperCAmelCase__					:   str       				=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCAmelCase__					:   List[str]       				=       Image.open(requests.get(lowerCAmelCase__	, stream=lowerCAmelCase__   ).raw   )

   return im







@torch.no_grad()
def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
   UpperCAmelCase__					:   Optional[Any]       				=       ConditionalDetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      UpperCAmelCase__					:   Tuple       				=       '''resnet101'''
   if "dc5" in model_name:
      UpperCAmelCase__					:   int       				=       True
   UpperCAmelCase__					:   List[Any]       				=       '''panoptic''' in model_name
   if is_panoptic:
      UpperCAmelCase__					:   Optional[Any]       				=       2_5_0
   else:
      UpperCAmelCase__					:   Optional[int]       				=       9_1
      UpperCAmelCase__					:   str       				=       '''huggingface/label-files'''
      UpperCAmelCase__					:   Optional[int]       				=       '''coco-detection-id2label.json'''
      UpperCAmelCase__					:   Tuple       				=       json.load(open(hf_hub_download(lowerCAmelCase__	, lowerCAmelCase__	, repo_type="""dataset"""   )	, """r"""   )   )
      UpperCAmelCase__					:   List[Any]       				=       {int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
      UpperCAmelCase__					:   int       				=       idalabel
      UpperCAmelCase__					:   Union[str, Any]       				=       {v: k for k, v in idalabel.items()}

   # load image processor
   UpperCAmelCase__					:   Optional[int]       				=       '''coco_panoptic''' if is_panoptic else '''coco_detection'''
   UpperCAmelCase__					:   Optional[int]       				=       ConditionalDetrImageProcessor(format=lowerCAmelCase__   )

   # prepare image
   UpperCAmelCase__					:   Union[str, Any]       				=       prepare_img()
   UpperCAmelCase__					:   Tuple       				=       image_processor(images=lowerCAmelCase__	, return_tensors="""pt"""   )
   UpperCAmelCase__					:   int       				=       encoding['''pixel_values''']

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

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


if __name__ == "__main__":
  __A		=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		=parser.parse_args()
  convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



'''simple docstring'''



import functools







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):

			# Validation
			if not isinstance(UpperCamelCase__	, UpperCamelCase__   ) or not all(isinstance(UpperCamelCase__	, UpperCamelCase__   ) for day in days   ):
						raise ValueError("""The parameter days should be a list of integers"""   )

			if len(UpperCamelCase__   ) != 3 or not all(isinstance(UpperCamelCase__	, UpperCamelCase__   ) for cost in costs   ):
						raise ValueError("""The parameter costs should be a list of three integers"""   )

			if len(UpperCamelCase__   ) == 0:
						return 0

			if min(UpperCamelCase__   ) <= 0:
						raise ValueError("""All days elements should be greater than 0"""   )

			if max(UpperCamelCase__   ) >= 3_6_6:
						raise ValueError("""All days elements should be less than 366"""   )

			UpperCAmelCase__					:   Union[str, Any]       				=       set(UpperCamelCase__   )

			@functools.cache
			def dynamic_programming(UpperCamelCase__   ) -> int:
						if index > 3_6_5:
									return 0

						if index not in days_set:
									return dynamic_programming(index + 1   )

						return min(
						    costs[0] + dynamic_programming(index + 1   )	, costs[1] + dynamic_programming(index + 7   )	, costs[2] + dynamic_programming(index + 3_0   )	, )

			return dynamic_programming(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()