






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





def 				snake_case  (     lowerCamelCase					):




 '''simple docstring'''







 __lowercase             =  YolosConfig()

 # size of the architecture
 if "yolos_ti" in yolos_name:
  __lowercase             =  192
  __lowercase             =  768
  __lowercase             =  12
  __lowercase             =  3
  __lowercase             =  [800, 1_333]
  __lowercase             =  False
 elif yolos_name == "yolos_s_dWr":
  __lowercase             =  330
  __lowercase             =  14
  __lowercase             =  6
  __lowercase             =  1_320
 elif "yolos_s" in yolos_name:
  __lowercase             =  384
  __lowercase             =  1_536
  __lowercase             =  12
  __lowercase             =  6
 elif "yolos_b" in yolos_name:
  __lowercase             =  [800, 1_344]

 __lowercase             =  91
 __lowercase             =  """huggingface/label-files"""
 __lowercase             =  """coco-detection-id2label.json"""
 __lowercase             =  json.load(open(hf_hub_download(lowerCamelCase				,  lowerCamelCase				,  repo_type="""dataset"""					)				,  """r"""					)					)
 __lowercase             =  {int(lowerCamelCase					): v for k, v in idalabel.items()}
 __lowercase             =  idalabel
 __lowercase             =  {v: k for k, v in idalabel.items()}

 return config





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = 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)
  __lowercase             =  state_dict.pop(F'blocks.{i}.attn.qkv.weight'					)
  __lowercase             =  state_dict.pop(F'blocks.{i}.attn.qkv.bias'					)
  # next, add query, keys and values (in that order) to the state dict
  __lowercase             =  in_proj_weight[: config.hidden_size, :]
  __lowercase             =  in_proj_bias[: config.hidden_size]
  __lowercase             =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __lowercase             =  in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __lowercase             =  in_proj_weight[-config.hidden_size :, :]
  __lowercase             =  in_proj_bias[-config.hidden_size :]





def 				snake_case  (     lowerCamelCase					):




 '''simple docstring'''







 if "backbone" in name:
  __lowercase             =  name.replace("""backbone"""				,  """vit"""					)
 if "cls_token" in name:
  __lowercase             =  name.replace("""cls_token"""				,  """embeddings.cls_token"""					)
 if "det_token" in name:
  __lowercase             =  name.replace("""det_token"""				,  """embeddings.detection_tokens"""					)
 if "mid_pos_embed" in name:
  __lowercase             =  name.replace("""mid_pos_embed"""				,  """encoder.mid_position_embeddings"""					)
 if "pos_embed" in name:
  __lowercase             =  name.replace("""pos_embed"""				,  """embeddings.position_embeddings"""					)
 if "patch_embed.proj" in name:
  __lowercase             =  name.replace("""patch_embed.proj"""				,  """embeddings.patch_embeddings.projection"""					)
 if "blocks" in name:
  __lowercase             =  name.replace("""blocks"""				,  """encoder.layer"""					)
 if "attn.proj" in name:
  __lowercase             =  name.replace("""attn.proj"""				,  """attention.output.dense"""					)
 if "attn" in name:
  __lowercase             =  name.replace("""attn"""				,  """attention.self"""					)
 if "norm1" in name:
  __lowercase             =  name.replace("""norm1"""				,  """layernorm_before"""					)
 if "norm2" in name:
  __lowercase             =  name.replace("""norm2"""				,  """layernorm_after"""					)
 if "mlp.fc1" in name:
  __lowercase             =  name.replace("""mlp.fc1"""				,  """intermediate.dense"""					)
 if "mlp.fc2" in name:
  __lowercase             =  name.replace("""mlp.fc2"""				,  """output.dense"""					)
 if "class_embed" in name:
  __lowercase             =  name.replace("""class_embed"""				,  """class_labels_classifier"""					)
 if "bbox_embed" in name:
  __lowercase             =  name.replace("""bbox_embed"""				,  """bbox_predictor"""					)
 if "vit.norm" in name:
  __lowercase             =  name.replace("""vit.norm"""				,  """vit.layernorm"""					)

 return name





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 for key in orig_state_dict.copy().keys():
  __lowercase             =  orig_state_dict.pop(lowerCamelCase					)

  if "qkv" in key:
   __lowercase             =  key.split("""."""					)
   __lowercase             =  int(key_split[2]					)
   __lowercase             =  model.vit.encoder.layer[layer_num].attention.attention.all_head_size
   if "weight" in key:
    __lowercase             =  val[:dim, :]
    __lowercase             =  val[
        dim : dim * 2, :
    ]
    __lowercase             =  val[-dim:, :]
   else:
    __lowercase             =  val[:dim]
    __lowercase             =  val[dim : dim * 2]
    __lowercase             =  val[-dim:]
  else:
   __lowercase             =  val

 return orig_state_dict





def 				snake_case  (     ):




 '''simple docstring'''







 __lowercase             =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
 __lowercase             =  Image.open(requests.get(lowerCamelCase				,  stream=lowerCamelCase					).raw					)
 return im





@torch.no_grad()
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = False					):




 '''simple docstring'''







 __lowercase             =  get_yolos_config(lowerCamelCase					)

 # load original state_dict
 __lowercase             =  torch.load(lowerCamelCase				,  map_location="""cpu"""					)["""model"""]

 # load 🤗 model
 __lowercase             =  YolosForObjectDetection(lowerCamelCase					)
 model.eval()
 __lowercase             =  convert_state_dict(lowerCamelCase				,  lowerCamelCase					)
 model.load_state_dict(lowerCamelCase					)

 # Check outputs on an image, prepared by YolosImageProcessor
 __lowercase             =  800 if yolos_name != """yolos_ti""" else 512
 __lowercase             =  YolosImageProcessor(format="""coco_detection"""				,  size=lowerCamelCase					)
 __lowercase             =  image_processor(images=prepare_img()				,  return_tensors="""pt"""					)
 __lowercase             =  model(**lowerCamelCase					)
 __lowercase       ,						__lowercase             =  outputs.logits, outputs.pred_boxes

 __lowercase       ,						__lowercase             =  None, None
 if yolos_name == "yolos_ti":
  __lowercase             =  torch.tensor(
      [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]					)
  __lowercase             =  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":
  __lowercase             =  torch.tensor(
      [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]					)
  __lowercase             =  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":
  __lowercase             =  torch.tensor(
      [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]					)
  __lowercase             =  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":
  __lowercase             =  torch.tensor(
      [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]					)
  __lowercase             =  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":
  __lowercase             =  torch.tensor(
      [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]					)
  __lowercase             =  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]				,  lowerCamelCase				,  atol=1e-4					)
 assert torch.allclose(pred_boxes[0, :3, :3]				,  lowerCamelCase				,  atol=1e-4					)

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

 if push_to_hub:
  __lowercase             =  {
      """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..."""					)
  __lowercase             =  model_mapping[yolos_name]
  image_processor.push_to_hub(lowerCamelCase				,  organization="""hustvl"""					)
  model.push_to_hub(lowerCamelCase				,  organization="""hustvl"""					)


if __name__ == "__main__":
      __UpperCamelCase      :      int     						=						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."""
      )

      __UpperCamelCase      :      Any     						=						parser.parse_args()
      convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









from __future__ import annotations

from scipy.special import comb  # type: ignore


class        __UpperCamelCase    :







	def __init__(  self      :							int       ,				_lowerCAmelCase      :							list[tuple[float, float]]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  list_of_points
		# Degree determines the flexibility of the curve.
		# Degree = 1 will produce a straight line.
		__lowercase             =  len(_lowerCAmelCase			) - 1







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							float			)		-> list[float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."
		__lowercase             =  []
		for i in range(len(self.list_of_points			)			):
			# basis function for each i
			output_values.append(
			    comb(self.degree       ,				_lowerCAmelCase			) * ((1 - t) ** (self.degree - i)) * (t**i)			)
		# the basis must sum up to 1 for it to produce a valid Bezier curve.
		assert round(sum(_lowerCAmelCase			)       ,				5			) == 1
		return output_values







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							float			)		-> tuple[float, float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."

		__lowercase             =  self.basis_function(_lowerCAmelCase			)
		__lowercase             =  0.0
		__lowercase             =  0.0
		for i in range(len(self.list_of_points			)			):
			# For all points, sum up the product of i-th basis function and i-th point.
			x += basis_function[i] * self.list_of_points[i][0]
			y += basis_function[i] * self.list_of_points[i][1]
		return (x, y)







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							float = 0.01			)		-> Union[str, Any]:
		"""simple docstring"""







		from matplotlib import pyplot as plt  # type: ignore

		__lowercase             =  []  # x coordinates of points to plot
		__lowercase             =  []  # y coordinates of points to plot

		__lowercase             =  0.0
		while t <= 1:
			__lowercase             =  self.bezier_curve_function(_lowerCAmelCase			)
			to_plot_x.append(value[0]			)
			to_plot_y.append(value[1]			)
			t += step_size

		__lowercase             =  [i[0] for i in self.list_of_points]
		__lowercase             =  [i[1] for i in self.list_of_points]

		plt.plot(
		    _lowerCAmelCase       ,				_lowerCAmelCase       ,				color="""blue"""       ,				label="""Curve of Degree """ + str(self.degree			)       ,				)
		plt.scatter(_lowerCAmelCase       ,				_lowerCAmelCase       ,				color="""red"""       ,				label="""Control Points"""			)
		plt.legend()
		plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
						BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
						BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

