"""simple docstring"""






from __future__ import annotations

from scipy.special import comb  # type: ignore





class 				_A		:

      def __init__(		self	, __lowerCAmelCase		):







            """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	, __lowerCAmelCase		):







            """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	, __lowerCAmelCase		):







            """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	, __lowerCAmelCase = 0.0_1		):







            """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




"""simple docstring"""






import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					Optional[int]:
      '''simple docstring'''


      return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Union[str, Any]	)				->					str:
      '''simple docstring'''


      lowercase														=			{}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            lowercase														=			key.replace("""heads.cmd.mim_head.cls.predictions"""      ,	"""mmm_image_head"""	)
            lowercase														=			key.replace("""heads.cmd.mlm_head.cls.predictions"""      ,	"""mmm_text_head"""	)
            lowercase														=			key.replace("""heads.cmd.itm_head.cls"""      ,	"""itm_head"""	)
            lowercase														=			key.replace("""heads.cmd.itm_head.pooler"""      ,	"""itm_head.pooler"""	)
            lowercase														=			key.replace("""heads.cmd.clip_head.logit_scale"""      ,	"""flava.logit_scale"""	)
            lowercase														=			key.replace("""heads.fairseq_mlm.cls.predictions"""      ,	"""mlm_head"""	)
            lowercase														=			key.replace("""heads.imagenet.mim_head.cls.predictions"""      ,	"""mim_head"""	)
            lowercase														=			key.replace("""mm_text_projection"""      ,	"""flava.text_to_mm_projection"""	)
            lowercase														=			key.replace("""mm_image_projection"""      ,	"""flava.image_to_mm_projection"""	)
            lowercase														=			key.replace("""image_encoder.module"""      ,	"""flava.image_model"""	)
            lowercase														=			key.replace("""text_encoder.module"""      ,	"""flava.text_model"""	)
            lowercase														=			key.replace("""mm_encoder.module.encoder.cls_token"""      ,	"""flava.multimodal_model.cls_token"""	)
            lowercase														=			key.replace("""mm_encoder.module"""      ,	"""flava.multimodal_model"""	)
            lowercase														=			key.replace("""text_projection"""      ,	"""flava.text_projection"""	)
            lowercase														=			key.replace("""image_projection"""      ,	"""flava.image_projection"""	)

            lowercase														=			value.float()

      for key, value in codebook_state_dict.items():
            lowercase														=			value

      return upgrade


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Optional[int]      ,	lowerCAmelCase__  :str=None	)				->					int:
      '''simple docstring'''


      if config_path is not None:
            lowercase														=			FlavaConfig.from_pretrained(lowerCAmelCase__	)
      else:
            lowercase														=			FlavaConfig()

      lowercase														=			FlavaForPreTraining(lowerCAmelCase__	).eval()

      lowercase														=			convert_dalle_checkpoint(lowerCAmelCase__      ,	lowerCAmelCase__      ,	save_checkpoint=lowerCAmelCase__	)

      if os.path.exists(lowerCAmelCase__	):
            lowercase														=			torch.load(lowerCAmelCase__      ,	map_location="""cpu"""	)
      else:
            lowercase														=			torch.hub.load_state_dict_from_url(lowerCAmelCase__      ,	map_location="""cpu"""	)

      lowercase														=			upgrade_state_dict(lowerCAmelCase__      ,	lowerCAmelCase__	)
      hf_model.load_state_dict(lowerCAmelCase__	)
      lowercase														=			hf_model.state_dict()
      lowercase														=			count_parameters(lowerCAmelCase__	)
      lowercase														=			count_parameters(lowerCAmelCase__	) + count_parameters(lowerCAmelCase__	)

      assert torch.allclose(lowerCAmelCase__      ,	lowerCAmelCase__      ,	atol=1e-3	)

      hf_model.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: Tuple        =argparse.ArgumentParser()
       parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
       parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
       parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
       parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
       __lowerCAmelCase					: List[str]        =parser.parse_args()

       convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)



