


'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


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







def     UpperCamelCase_      (   A__ :       str ):
		'''simple docstring'''





		lowerCAmelCase_      :				List[str]      =       YolosConfig()

		# size of the architecture
		if "yolos_ti" in yolos_name:
				lowerCAmelCase_      :				List[str]      =       1_92
				lowerCAmelCase_      :				Optional[int]      =       7_68
				lowerCAmelCase_      :				Optional[int]      =       12
				lowerCAmelCase_      :				Optional[Any]      =       3
				lowerCAmelCase_      :				str      =       [8_00, 13_33]
				lowerCAmelCase_      :				int      =       False
		elif yolos_name == "yolos_s_dWr":
				lowerCAmelCase_      :				Optional[int]      =       3_30
				lowerCAmelCase_      :				Dict      =       14
				lowerCAmelCase_      :				List[Any]      =       6
				lowerCAmelCase_      :				Optional[int]      =       13_20
		elif "yolos_s" in yolos_name:
				lowerCAmelCase_      :				Any      =       3_84
				lowerCAmelCase_      :				List[str]      =       15_36
				lowerCAmelCase_      :				Optional[int]      =       12
				lowerCAmelCase_      :				List[str]      =       6
		elif "yolos_b" in yolos_name:
				lowerCAmelCase_      :				Any      =       [8_00, 13_44]

		lowerCAmelCase_      :				int      =       91
		lowerCAmelCase_      :				str      =       """huggingface/label-files"""
		lowerCAmelCase_      :				Optional[int]      =       """coco-detection-id2label.json"""
		lowerCAmelCase_      :				List[Any]      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
		lowerCAmelCase_      :				str      =       {int(A__ ): v for k, v in idalabel.items()}
		lowerCAmelCase_      :				Tuple      =       idalabel
		lowerCAmelCase_      :				Optional[Any]      =       {v: k for k, v in idalabel.items()}

		return config







def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       int							,   A__ :       Union[str, Any] = False ):
		'''simple docstring'''





		for i in range(config.num_hidden_layers ):
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowerCAmelCase_      :				List[Any]      =       state_dict.pop(f'blocks.{i}.attn.qkv.weight' )
				lowerCAmelCase_      :				List[str]      =       state_dict.pop(f'blocks.{i}.attn.qkv.bias' )
				# next, add query, keys and values (in that order) to the state dict
				lowerCAmelCase_      :				str      =       in_proj_weight[: config.hidden_size, :]
				lowerCAmelCase_      :				Union[str, Any]      =       in_proj_bias[: config.hidden_size]
				lowerCAmelCase_      :				List[str]      =       in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase_      :				List[str]      =       in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCAmelCase_      :				Optional[int]      =       in_proj_weight[-config.hidden_size :, :]
				lowerCAmelCase_      :				List[str]      =       in_proj_bias[-config.hidden_size :]







def     UpperCamelCase_      (   A__ :       Tuple ):
		'''simple docstring'''





		if "backbone" in name:
				lowerCAmelCase_      :				List[str]      =       name.replace("""backbone"""							,   """vit""" )
		if "cls_token" in name:
				lowerCAmelCase_      :				Optional[Any]      =       name.replace("""cls_token"""							,   """embeddings.cls_token""" )
		if "det_token" in name:
				lowerCAmelCase_      :				List[Any]      =       name.replace("""det_token"""							,   """embeddings.detection_tokens""" )
		if "mid_pos_embed" in name:
				lowerCAmelCase_      :				List[str]      =       name.replace("""mid_pos_embed"""							,   """encoder.mid_position_embeddings""" )
		if "pos_embed" in name:
				lowerCAmelCase_      :				Optional[int]      =       name.replace("""pos_embed"""							,   """embeddings.position_embeddings""" )
		if "patch_embed.proj" in name:
				lowerCAmelCase_      :				Dict      =       name.replace("""patch_embed.proj"""							,   """embeddings.patch_embeddings.projection""" )
		if "blocks" in name:
				lowerCAmelCase_      :				Union[str, Any]      =       name.replace("""blocks"""							,   """encoder.layer""" )
		if "attn.proj" in name:
				lowerCAmelCase_      :				int      =       name.replace("""attn.proj"""							,   """attention.output.dense""" )
		if "attn" in name:
				lowerCAmelCase_      :				Tuple      =       name.replace("""attn"""							,   """attention.self""" )
		if "norm1" in name:
				lowerCAmelCase_      :				List[str]      =       name.replace("""norm1"""							,   """layernorm_before""" )
		if "norm2" in name:
				lowerCAmelCase_      :				List[Any]      =       name.replace("""norm2"""							,   """layernorm_after""" )
		if "mlp.fc1" in name:
				lowerCAmelCase_      :				Union[str, Any]      =       name.replace("""mlp.fc1"""							,   """intermediate.dense""" )
		if "mlp.fc2" in name:
				lowerCAmelCase_      :				List[str]      =       name.replace("""mlp.fc2"""							,   """output.dense""" )
		if "class_embed" in name:
				lowerCAmelCase_      :				Union[str, Any]      =       name.replace("""class_embed"""							,   """class_labels_classifier""" )
		if "bbox_embed" in name:
				lowerCAmelCase_      :				List[Any]      =       name.replace("""bbox_embed"""							,   """bbox_predictor""" )
		if "vit.norm" in name:
				lowerCAmelCase_      :				Optional[int]      =       name.replace("""vit.norm"""							,   """vit.layernorm""" )

		return name







def     UpperCamelCase_      (   A__ :       Tuple							,   A__ :       str ):
		'''simple docstring'''





		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_      :				Union[str, Any]      =       orig_state_dict.pop(A__ )

				if "qkv" in key:
						lowerCAmelCase_      :				List[str]      =       key.split(""".""" )
						lowerCAmelCase_      :				Union[str, Any]      =       int(key_split[2] )
						lowerCAmelCase_      :				Union[str, Any]      =       model.vit.encoder.layer[layer_num].attention.attention.all_head_size
						if "weight" in key:
								lowerCAmelCase_      :				Optional[int]      =       val[:dim, :]
								lowerCAmelCase_      :				Tuple      =       val[
								    dim : dim * 2, :
								]
								lowerCAmelCase_      :				List[str]      =       val[-dim:, :]
						else:
								lowerCAmelCase_      :				Tuple      =       val[:dim]
								lowerCAmelCase_      :				Any      =       val[dim : dim * 2]
								lowerCAmelCase_      :				List[str]      =       val[-dim:]
				else:
						lowerCAmelCase_      :				str      =       val

		return orig_state_dict







def     UpperCamelCase_      (   ):
		'''simple docstring'''





		lowerCAmelCase_      :				Union[str, Any]      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowerCAmelCase_      :				Any      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
		return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       List[str]							,   A__ :       Any							,   A__ :       List[Any] = False ):
		'''simple docstring'''





		lowerCAmelCase_      :				Dict      =       get_yolos_config(A__ )

		# load original state_dict
		lowerCAmelCase_      :				Optional[Any]      =       torch.load(A__							,   map_location="""cpu""" )["""model"""]

		# load 🤗 model
		lowerCAmelCase_      :				Optional[Any]      =       YolosForObjectDetection(A__ )
		model.eval()
		lowerCAmelCase_      :				Any      =       convert_state_dict(A__							,   A__ )
		model.load_state_dict(A__ )

		# Check outputs on an image, prepared by YolosImageProcessor
		lowerCAmelCase_      :				Union[str, Any]      =       8_00 if yolos_name != """yolos_ti""" else 5_12
		lowerCAmelCase_      :				List[str]      =       YolosImageProcessor(format="""coco_detection"""							,   size=A__ )
		lowerCAmelCase_      :				str      =       image_processor(images=prepare_img()							,   return_tensors="""pt""" )
		lowerCAmelCase_      :				Tuple      =       model(**A__ )
		lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       outputs.logits, outputs.pred_boxes

		lowerCAmelCase_,							lowerCAmelCase_      :				str      =       None, None
		if yolos_name == "yolos_ti":
				lowerCAmelCase_      :				Union[str, Any]      =       torch.tensor(
				    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.9769, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]] )
				lowerCAmelCase_      :				str      =       torch.tensor(
				    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]] )
		elif yolos_name == "yolos_s_200_pre":
				lowerCAmelCase_      :				str      =       torch.tensor(
				    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]] )
				lowerCAmelCase_      :				Optional[Any]      =       torch.tensor(
				    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] )
		elif yolos_name == "yolos_s_300_pre":
				lowerCAmelCase_      :				Optional[int]      =       torch.tensor(
				    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]] )
				lowerCAmelCase_      :				Any      =       torch.tensor(
				    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]] )
		elif yolos_name == "yolos_s_dWr":
				lowerCAmelCase_      :				Any      =       torch.tensor(
				    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]] )
				lowerCAmelCase_      :				Tuple      =       torch.tensor(
				    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]] )
		elif yolos_name == "yolos_base":
				lowerCAmelCase_      :				Tuple      =       torch.tensor(
				    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]] )
				lowerCAmelCase_      :				str      =       torch.tensor(
				    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]] )
		else:
				raise ValueError(f'Unknown yolos_name: {yolos_name}' )

		assert torch.allclose(logits[0, :3, :3]							,   A__							,   atol=1E-4 )
		assert torch.allclose(pred_boxes[0, :3, :3]							,   A__							,   atol=1E-4 )

		Path(A__ ).mkdir(exist_ok=A__ )
		print(f'Saving model {yolos_name} to {pytorch_dump_folder_path}' )
		model.save_pretrained(A__ )
		print(f'Saving image processor to {pytorch_dump_folder_path}' )
		image_processor.save_pretrained(A__ )

		if push_to_hub:
				lowerCAmelCase_      :				str      =       {
				    """yolos_ti""": """yolos-tiny""",
				    """yolos_s_200_pre""": """yolos-small""",
				    """yolos_s_300_pre""": """yolos-small-300""",
				    """yolos_s_dWr""": """yolos-small-dwr""",
				    """yolos_base""": """yolos-base""",
				}

				print("""Pushing to the hub...""" )
				lowerCAmelCase_      :				Union[str, Any]      =       model_mapping[yolos_name]
				image_processor.push_to_hub(A__							,   organization="""hustvl""" )
				model.push_to_hub(A__							,   organization="""hustvl""" )


if __name__ == "__main__":
		__A :  Union[str, Any]    							=				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--yolos_name",
		    default="yolos_s_200_pre",
		    type=str,
		    help=(
		        "Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
		        " 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
		    ),
		)
		parser.add_argument(
		    "--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)
		parser.add_argument(
		    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
		)

		__A :  Union[str, Any]    							=				parser.parse_args()
		convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''






from __future__ import annotations







def     UpperCamelCase_      (   A__ :       int | float | str							,   A__ :       int | float | str ):
  '''simple docstring'''





  if nth_term == "":
    return [""]
  lowerCAmelCase_      :				str      =       int(A__ )
  lowerCAmelCase_      :				Tuple      =       int(A__ )
  lowerCAmelCase_      :				list[str]      =       []
  for temp in range(int(A__ ) ):
    series.append(f'1 / {pow(temp + 1							,   int(A__ ) )}' if series else """1""" )
  return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __A :  str    							=				int(input("Enter the last number (nth term) of the P-Series"))
  __A :  Tuple    							=				int(input("Enter the power for  P-Series"))
  print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
  print(p_series(nth_term, power))



