





'''simple docstring'''


import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def 				UpperCAmelCase_    (		__lowerCamelCase   :   Optional[Any]					):
   return np.dot(__lowerCamelCase      ,__lowerCamelCase					)
class  a_  :





     def __init__(					self						:   int      , *,
         lowercase						:   float = np.inf      , lowercase						:   str = "linear"      , lowercase						:   float = 0.0      , ):





        """simple docstring"""




        lowercase_     :Optional[int]            =					regularization
        lowercase_     :int            =					gamma
        if kernel == "linear":
           lowercase_     :Optional[int]            =					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"		)
           lowercase_     :Union[str, Any]            =					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:
           lowercase_     :Optional[Any]            =					F'Unknown kernel: {kernel}'
           raise ValueError(snake_case__		)





     def 				lowercase__ (					self						:   Tuple      , lowercase						:   ndarray      , lowercase						:   ndarray		):





        """simple docstring"""




        return np.dot(snake_case__      , snake_case__		)





     def 				lowercase__ (					self						:   int      , lowercase						:   ndarray      , lowercase						:   ndarray		):





        """simple docstring"""




        return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)





     def 				lowercase__ (					self						:   int      , lowercase						:   list[ndarray]      , lowercase						:   ndarray		):





        """simple docstring"""




        lowercase_     :Tuple            =					observations
        lowercase_     :Optional[Any]            =					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

        ((lowercase_				)     ,								)     :Tuple            =					np.shape(snake_case__		)

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

        lowercase_     :List[Any]            =					LinearConstraint(snake_case__      , 0      , 0		)
        lowercase_     :List[str]            =					Bounds(0      , self.regularization		)

        lowercase_     :Union[str, Any]            =					minimize(
            snake_case__      , np.ones(snake_case__		)      , bounds=snake_case__      , constraints=[ly_contraint]		).x
        lowercase_     :Optional[Any]            =					l_star

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







     def 				lowercase__ (					self						:   Any      , lowercase						:   ndarray		):





        """simple docstring"""




        lowercase_     :Optional[int]            =					sum(
            self.optimum[n]
            * self.classes[n]
            * self.kernel(self.observations[n]      , snake_case__		)
            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
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


SCREAMING_SNAKE_CASE:     Optional[int]									= [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]


def 					_a							(					lowerCAmelCase )->	str:
		for pegasus_name, hf_name in PATTERNS:
				SCREAMING_SNAKE_CASE_					      =							k.replace(lowerCAmelCase    ,						lowerCAmelCase )
		return k


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	PegasusForConditionalGeneration:
		SCREAMING_SNAKE_CASE_					      =							DEFAULTS.copy()
		cfg_kwargs.update(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							PegasusConfig(**lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							PegasusForConditionalGeneration(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							torch_model.model.state_dict()
		SCREAMING_SNAKE_CASE_					      =							{}
		for k, v in tf_weights.items():
				SCREAMING_SNAKE_CASE_					      =							rename_state_dict_key(lowerCAmelCase )
				if new_k not in sd:
						raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )

				if "dense" in k or "proj" in new_k:
						SCREAMING_SNAKE_CASE_					      =							v.T
				SCREAMING_SNAKE_CASE_					      =							torch.tensor(lowerCAmelCase    ,						dtype=sd[new_k].dtype )
				assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
		# make sure embedding.padding_idx is respected
		SCREAMING_SNAKE_CASE_					      =							torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
		SCREAMING_SNAKE_CASE_					      =							mapping['shared.weight']
		SCREAMING_SNAKE_CASE_					      =							mapping['shared.weight']
		SCREAMING_SNAKE_CASE_					      =							{k: torch.zeros_like(lowerCAmelCase ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
		mapping.update(**lowerCAmelCase )
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							torch_model.model.load_state_dict(lowerCAmelCase    ,						strict=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							[
		    k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
		]
		assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
		assert extra == [], F'''no matches found for the following tf keys {extra}'''
		return torch_model


def 					_a							(					lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" )->	Dict:
		SCREAMING_SNAKE_CASE_					      =							tf.train.list_variables(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							{}
		SCREAMING_SNAKE_CASE_					      =							['Adafactor', 'global_step']
		for name, shape in tqdm(lowerCAmelCase    ,						desc='converting tf checkpoint to dict' ):
				SCREAMING_SNAKE_CASE_					      =							any(pat in name for pat in ignore_name )
				if skip_key:
						continue
				SCREAMING_SNAKE_CASE_					      =							tf.train.load_variable(lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							array
		return tf_weights


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Optional[Any]:
		# save tokenizer first
		SCREAMING_SNAKE_CASE_					      =							Path(lowerCAmelCase ).parent.name
		SCREAMING_SNAKE_CASE_					      =							task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
		SCREAMING_SNAKE_CASE_					      =							PegasusTokenizer.from_pretrained('sshleifer/pegasus'    ,						model_max_length=lowerCAmelCase )
		assert tok.model_max_length == desired_max_model_length
		tok.save_pretrained(lowerCAmelCase )

		# convert model
		SCREAMING_SNAKE_CASE_					      =							get_tf_weights_as_numpy(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							task_specific_params[F'''summarization_{dataset}''']
		if dataset == "large":
				SCREAMING_SNAKE_CASE_					      =							task_specific_params
		SCREAMING_SNAKE_CASE_					      =							convert_pegasus(lowerCAmelCase    ,						lowerCAmelCase )
		torch_model.save_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							torch_model.state_dict()
		sd.pop('model.decoder.embed_positions.weight' )
		sd.pop('model.encoder.embed_positions.weight' )
		torch.save(lowerCAmelCase    ,						Path(lowerCAmelCase ) / 'pytorch_model.bin' )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
						parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
						SCREAMING_SNAKE_CASE:     int									= parser.parse_args()
						if args.save_dir is None:
												SCREAMING_SNAKE_CASE:     List[Any]									= Path(args.tf_ckpt_path).parent.name
												SCREAMING_SNAKE_CASE:     Tuple									= os.path.join('''pegasus''', dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)