


import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


SCREAMING_SNAKE_CASE       :		List[Any]         =				["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
SCREAMING_SNAKE_CASE       :		List[str]         =				{"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
   raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				" Hello world! cécé herlolip"

SCREAMING_SNAKE_CASE       :		Tuple         =				[
    ("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"),
    ("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"),
    ("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"),
    ("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"),
]






def     UpperCamelCase    (      _a		)					->							Dict:
     '''simple docstring'''





     lowercase_       :Dict     =       [
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''_float_tensor''',
     ]
     for k in ignore_keys:
          state_dict.pop(_a       ,		_a		)






def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :List[str]     =       dct.pop(_a		)
     lowercase_       :int     =       val






def     UpperCamelCase    (      _a		)					->							List[Any]:
     '''simple docstring'''





     lowercase_       :Any     =       torch.load(_a       ,		map_location='''cpu'''		)
     lowercase_       :Tuple     =       torch.hub.load('''pytorch/fairseq'''       ,		'''bart.large.cnn'''		).eval()
     hub_interface.model.load_state_dict(sd['''model''']		)
     return hub_interface






def     UpperCamelCase    (      _a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :Tuple     =       emb.weight.shape
     lowercase_       :Optional[Any]     =       nn.Linear(_a       ,		_a       ,		bias=_a		)
     lowercase_       :int     =       emb.weight.data
     return lin_layer






@torch.no_grad()
def     UpperCamelCase    (      _a       ,		_a       ,		_a=None		)					->							int:
     '''simple docstring'''





     if not os.path.exists(_a		):
          lowercase_       :Optional[int]     =       torch.hub.load('''pytorch/fairseq'''       ,		_a		).eval()
     else:
          lowercase_       :Optional[Any]     =       load_xsum_checkpoint(_a		)

     bart.model.upgrade_state_dict(bart.model.state_dict()		)
     if hf_checkpoint_name is None:
          lowercase_       :Dict     =       checkpoint_path.replace('''.'''       ,		'''-'''		)
     lowercase_       :List[str]     =       BartConfig.from_pretrained(_a		)
     lowercase_       :str     =       bart.encode(_a		).unsqueeze(0		)
     lowercase_       :Optional[Any]     =       BartTokenizer.from_pretrained(_a		).encode(_a       ,		return_tensors='''pt'''		).unsqueeze(0		)
     if not torch.eq(_a       ,		_a		).all():
          raise ValueError(
              f"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"		)

     if checkpoint_path == "bart.large.mnli":
          lowercase_       :Optional[Any]     =       bart.state_dict()
          remove_ignore_keys_(_a		)
          lowercase_       :Optional[Any]     =       state_dict['''model.decoder.embed_tokens.weight''']
          for src, dest in mnli_rename_keys:
               rename_key(_a       ,		_a       ,		_a		)
          lowercase_       :Optional[Any]     =       BartForSequenceClassification(_a		).eval()
          model.load_state_dict(_a		)
          lowercase_       :List[Any]     =       bart.predict('''mnli'''       ,		_a       ,		return_logits=_a		)
          lowercase_       :Any     =       model(_a		)[0]  # logits
     else:  # no classification heads to worry about
          lowercase_       :Optional[int]     =       bart.model.state_dict()
          remove_ignore_keys_(_a		)
          lowercase_       :List[Any]     =       state_dict['''decoder.embed_tokens.weight''']
          lowercase_       :str     =       bart.extract_features(_a		)
          if hf_checkpoint_name == "facebook/bart-large":
               lowercase_       :List[str]     =       BartModel(_a		).eval()
               model.load_state_dict(_a		)
               lowercase_       :Optional[int]     =       model(_a		).model[0]
          else:
               lowercase_       :str     =       BartForConditionalGeneration(_a		).eval()  # an existing summarization ckpt
               model.model.load_state_dict(_a		)
               if hasattr(_a       ,		'''lm_head'''		):
                    lowercase_       :Any     =       make_linear_from_emb(model.model.shared		)
               lowercase_       :Optional[Any]     =       model.model(_a		)[0]

    # Check results
     if fairseq_output.shape != new_model_outputs.shape:
          raise ValueError(
              f"`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"		)
     if (fairseq_output != new_model_outputs).any().item():
          raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''		)
     Path(_a		).mkdir(exist_ok=_a		)
     model.save_pretrained(_a		)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		int         =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
   )
   parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
   parser.add_argument(
       "--hf_config", default=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
   )
   SCREAMING_SNAKE_CASE       :		int         =				parser.parse_args()
   convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)





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




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return np.dot(_a       ,		_a		)






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							*,
       UpperCamelCase_ = np.inf   ,							UpperCamelCase_ = "linear"   ,							UpperCamelCase_ = 0.0   ,							):
        lowercase_       :Dict     =       regularization
        lowercase_       :Optional[Any]     =       gamma
        if kernel == "linear":
             lowercase_       :Optional[Any]     =       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_       :str     =       f"Unknown kernel: {kernel}"
             raise ValueError(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.dot(UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Tuple     =       observations
        lowercase_       :Union[str, 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_)    ,      )       :Optional[Any]     =       np.shape(UpperCamelCase_		)

        def to_minimize(UpperCamelCase_		) -> float:
             lowercase_       :Dict     =       0
             ((lowercase_)    ,      )       :Tuple     =       np.shape(UpperCamelCase_		)
             for i in range(UpperCamelCase_		):
                  for j in range(UpperCamelCase_		):
                       s += (
                           candidate[i]
                           * candidate[j]
                           * classes[i]
                           * classes[j]
                           * self.kernel(observations[i]   ,							observations[j]		)
                       )
             return 1 / 2 * s - sum(UpperCamelCase_		)

        lowercase_       :Dict     =       LinearConstraint(UpperCamelCase_   ,							0   ,							0		)
        lowercase_       :Optional[Any]     =       Bounds(0   ,							self.regularization		)

        lowercase_       :Any     =       minimize(
            UpperCamelCase_   ,							np.ones(UpperCamelCase_		)   ,							bounds=UpperCamelCase_   ,							constraints=[ly_contraint]		).x
        lowercase_       :Tuple     =       l_star

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




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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

