





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


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






def     lowerCamelCase__			(__lowerCamelCase	):
     # initialize config
     if "resnet-50" in model_name:
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							ResNetConfig.from_pretrained("microsoft/resnet-50"	)
     elif "resnet-101" in model_name:
          _SCREAMING_SNAKE_CASE       :	Any						    =							ResNetConfig.from_pretrained("microsoft/resnet-101"	)
     else:
          raise ValueError("Model name should include either resnet50 or resnet101"	)

     _SCREAMING_SNAKE_CASE       :	Any						    =							DetrConfig(use_timm_backbone=__lowerCamelCase,       backbone_config=__lowerCamelCase	)

     # set label attributes
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							"panoptic" in model_name
     if is_panoptic:
          _SCREAMING_SNAKE_CASE       :	int						    =							250
     else:
          _SCREAMING_SNAKE_CASE       :	int						    =							91
          _SCREAMING_SNAKE_CASE       :	str						    =							"huggingface/label-files"
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							"coco-detection-id2label.json"
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							json.load(open(hf_hub_download(__lowerCamelCase,       __lowerCamelCase,       repo_type="dataset"	),       "r"	)	)
          _SCREAMING_SNAKE_CASE       :	str						    =							{int(__lowerCamelCase	): v for k, v in idalabel.items()}
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							idalabel
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							{v: k for k, v in idalabel.items()}

     return config, is_panoptic






def     lowerCamelCase__			(__lowerCamelCase	):
     # here we list all keys to be renamed (original name on the left, our name on the right)
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							[]

     # stem
     # fmt: off
     rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")	)
     rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")	)
     rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")	)
     rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")	)
     rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")	)
     # stages
     for stage_idx in range(len(config.backbone_config.depths	)	):
          for layer_idx in range(config.backbone_config.depths[stage_idx]	):
               # shortcut
               if layer_idx == 0:
                    rename_keys.append(
                        (
                            f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                            f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                        )	)
                    rename_keys.append(
                        (
                            f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                            f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                        )	)
                    rename_keys.append(
                        (
                            f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                            f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                        )	)
                    rename_keys.append(
                        (
                            f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                            f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                        )	)
                    rename_keys.append(
                        (
                            f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                            f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                        )	)
               # 3 convs
               for i in range(3	):
                    rename_keys.append(
                        (
                            f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                            f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                        )	)
                    rename_keys.append(
                        (
                            f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                            f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                        )	)
                    rename_keys.append(
                        (
                            f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                            f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                        )	)
                    rename_keys.append(
                        (
                            f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                            f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                        )	)
                    rename_keys.append(
                        (
                            f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                            f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                        )	)
    # fmt: on

     for i in range(config.encoder_layers	):
          # 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"),
         ]	)

     return rename_keys






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							state_dict.pop(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Any						    =							val






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






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	str						    =							"http://images.cocodataset.org/val2017/000000039769.jpg"
     _SCREAMING_SNAKE_CASE       :	Dict						    =							Image.open(requests.get(__lowerCamelCase,       stream=__lowerCamelCase	).raw	)

     return im






@torch.no_grad()
def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase=None,       __lowerCamelCase=False	):
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							get_detr_config(__lowerCamelCase	)

     # load original model from torch hub
     _SCREAMING_SNAKE_CASE       :	Dict						    =							{
         "detr-resnet-50": "detr_resnet50",
         "detr-resnet-101": "detr_resnet101",
     }
     logger.info(f"""Converting model {model_name}..."""	)
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.hub.load("facebookresearch/detr",       model_name_to_original_name[model_name],       pretrained=__lowerCamelCase	).eval()
     _SCREAMING_SNAKE_CASE       :	str						    =							detr.state_dict()
     # rename keys
     for src, dest in create_rename_keys(__lowerCamelCase	):
          if is_panoptic:
               _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"detr." + src
          rename_key(__lowerCamelCase,       __lowerCamelCase,       __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
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"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       :	Union[str, Any]						    =							state_dict.pop(__lowerCamelCase	)
                    _SCREAMING_SNAKE_CASE       :	str						    =							val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    _SCREAMING_SNAKE_CASE       :	Any						    =							state_dict.pop(__lowerCamelCase	)
                    _SCREAMING_SNAKE_CASE       :	List[str]						    =							val
               elif key.startswith("bbox_attention"	) or key.startswith("mask_head"	):
                    continue
               else:
                    _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							state_dict.pop(__lowerCamelCase	)
                    _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							val
          else:
               if not key.startswith("class_labels_classifier"	) and not key.startswith("bbox_predictor"	):
                    _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							state_dict.pop(__lowerCamelCase	)
                    _SCREAMING_SNAKE_CASE       :	List[Any]						    =							val

    # finally, create HuggingFace model and load state dict
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							DetrForSegmentation(__lowerCamelCase	) if is_panoptic else DetrForObjectDetection(__lowerCamelCase	)
     model.load_state_dict(__lowerCamelCase	)
     model.eval()

     # verify our conversion on an image
     _SCREAMING_SNAKE_CASE       :	int						    =							"coco_panoptic" if is_panoptic else "coco_detection"
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							DetrImageProcessor(format=__lowerCamelCase	)

     _SCREAMING_SNAKE_CASE       :	int						    =							processor(images=prepare_img(),       return_tensors="pt"	)
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							encoding["pixel_values"]

     _SCREAMING_SNAKE_CASE       :	str						    =							detr(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							model(__lowerCamelCase	)

     assert torch.allclose(outputs.logits,       original_outputs["pred_logits"],       atol=1e-3	)
     assert torch.allclose(outputs.pred_boxes,       original_outputs["pred_boxes"],       atol=1e-3	)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks,       original_outputs["pred_masks"],       atol=1e-4	)
     print("Looks ok!"	)

     if pytorch_dump_folder_path is not None:
          # 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	)
          processor.save_pretrained(__lowerCamelCase	)

     if push_to_hub:
          # Upload model and image processor to the hub
          logger.info("Uploading PyTorch model and image processor to the hub..."	)
          model.push_to_hub(f"""nielsr/{model_name}"""	)
          processor.push_to_hub(f"""nielsr/{model_name}"""	)


if __name__ == "__main__":
       UpperCamelCase__      =argparse.ArgumentParser()

       parser.add_argument(
           '--model_name',
           default='detr-resnet-50',
           type=str,
           choices=['detr-resnet-50', 'detr-resnet-101'],
           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.'
       )
       parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
       UpperCamelCase__      =parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






from __future__ import annotations

from scipy.special import comb  # type: ignore





class 			lowerCAmelCase__:






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							list_of_points
            # Degree determines the flexibility of the curve.
            # Degree = 1 will produce a straight line.
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							len(__lowerCamelCase							) - 1




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     list[float]:
            assert 0 <= t <= 1, "Time t must be between 0 and 1."
            _SCREAMING_SNAKE_CASE       :	list[float]						    =							[]
            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     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     tuple[float, float]:
            assert 0 <= t <= 1, "Time t must be between 0 and 1."

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.basis_function(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	str						    =							0.0
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							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     UpperCamelCase_			(      self    ,						__lowerCamelCase = 0.01							)			->     int:
            from matplotlib import pyplot as plt  # type: ignore

            _SCREAMING_SNAKE_CASE       :	list[float]						    =							[]  # x coordinates of points to plot
            _SCREAMING_SNAKE_CASE       :	list[float]						    =							[]  # y coordinates of points to plot

            _SCREAMING_SNAKE_CASE       :	Dict						    =							0.0
            while t <= 1:
                 _SCREAMING_SNAKE_CASE       :	str						    =							self.bezier_curve_function(__lowerCamelCase							)
                 to_plot_x.append(value[0]							)
                 to_plot_y.append(value[1]							)
                 t += step_size

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[i[0] for i in self.list_of_points]
            _SCREAMING_SNAKE_CASE       :	Dict						    =							[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