




import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize







def 				lowerCAmelCase_	(     snake_case_     ):
      return np.dot(snake_case_,snake_case_     )







class 						lowercase							:

     def __init__(  self		,     *,
         _a = np.inf		,     _a = "linear"		,     _a = 0.0		,     )						->							None:
           _A							:     List[Any]			=						regularization
           _A							:     Optional[int]			=						gamma
           if kernel == "linear":
                 _A							:     List[str]			=						self.__linear
           elif kernel == "rbf":
                 if self.gamma == 0:
                       raise ValueError("""rbf kernel requires gamma"""				)
                 if not isinstance(self.gamma		,     (float, int)				):
                       raise ValueError("""gamma must be float or int"""				)
                 if not self.gamma > 0:
                       raise ValueError("""gamma must be > 0"""				)
                 _A							:     Optional[int]			=						self.__rbf
                 # in the future, there could be a default value like in sklearn
                 # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
                 # previously it was 1/(n_features)
           else:
                 _A							:     Dict			=						F'''Unknown kernel: {kernel}'''
                 raise ValueError(_a				)

     def 					a__	(  self		,     _a		,     _a				)						->							float:
           return np.dot(_a		,     _a				)

     def 					a__	(  self		,     _a		,     _a				)						->							float:
           return np.exp(-(self.gamma * norm_squared(vectora - vectora				))				)

     def 					a__	(  self		,     _a		,     _a				)						->							None:
           _A							:     List[Any]			=						observations
           _A							:     Optional[int]			=						classes

           # using Wolfe's Dual to calculate w.
           # Primal problem: minimize 1/2*norm_squared(w)
           #   constraint: yn(w . xn + b) >= 1
           #
           # With l a vector
           # Dual problem: maximize sum_n(ln) -
           #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
           #   constraint: self.C >= ln >= 0
           #           and sum_n(ln*yn) = 0
           # Then we get w using w = sum_n(ln*yn*xn)
           # At the end we can get b ~= mean(yn - w . xn)
           #
           # Since we use kernels, we only need l_star to calculate b
           # and to classify observations

           ((_A)			,   )							:     Union[str, Any]			=						np.shape(_a				)

           def to_minimize(_a				) -> float:
                 _A							:     str			=						0
                 ((_A)			,   )							:     Optional[int]			=						np.shape(_a				)
                 for i in range(_a				):
                       for j in range(_a				):
                             s += (
                                 candidate[i]
                                 * candidate[j]
                                 * classes[i]
                                 * classes[j]
                                 * self.kernel(observations[i]		,     observations[j]				)
                             )
                 return 1 / 2 * s - sum(_a				)

           _A							:     Optional[int]			=						LinearConstraint(_a		,     0		,     0				)
           _A							:     List[str]			=						Bounds(0		,     self.regularization				)

           _A							:     Dict			=						minimize(
               _a		,     np.ones(_a				)		,     bounds=_a		,     constraints=[ly_contraint]				).x
           _A							:     Union[str, Any]			=						l_star

           # calculating mean offset of separation plane to points
           _A							:     Optional[int]			=						0
           for i in range(_a				):
                 for j in range(_a				):
                       s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                           observations[i]		,     observations[j]				)
           _A							:     str			=						s / n



     def 					a__	(  self		,     _a				)						->							int:
           _A							:     Union[str, Any]			=						sum(
               self.optimum[n]
               * self.classes[n]
               * self.kernel(self.observations[n]		,     _a				)
               for n in range(len(self.classes				)				)				)
           return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
  import doctest

  doctest.testmod()



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			(	a_						)		->	List[str]:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= {}

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

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

								__A   			= value.float()

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

				return upgrade


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_=None						)		->	Tuple:







				"""simple docstring"""



				if config_path is not None:
								__A   			= FlavaConfig.from_pretrained(a_						)
				else:
								__A   			= FlavaConfig()

				__A   			= FlavaForPreTraining(a_						).eval()

				__A   			= convert_dalle_checkpoint(a_		,   a_		,   save_checkpoint=a_						)

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

				__A   			= upgrade_state_dict(a_		,   a_						)
				hf_model.load_state_dict(a_						)
				__A   			= hf_model.state_dict()
				__A   			= count_parameters(a_						)
				__A   			= count_parameters(a_						) + count_parameters(a_						)

				assert torch.allclose(a_		,   a_		,   atol=1E-3						)

				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Any							 =				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')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

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