

'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification

def 				__UpperCamelCase							(	lowercase__						:  Any ):






     '''simple docstring'''





     __lowercase					=[
         "encoder.version",
         "decoder.version",
         "model.encoder.version",
         "model.decoder.version",
         "_float_tensor",
         "decoder.output_projection.weight",
     ]
     for k in ignore_keys:
          state_dict.pop(UpperCamelCase__,					UpperCamelCase__ )

def 				__UpperCamelCase							(	lowercase__						:  Dict ):






     '''simple docstring'''





     __lowercase					=emb.weight.shape
     __lowercase					=nn.Linear(UpperCamelCase__,					UpperCamelCase__,					bias=UpperCamelCase__ )
     __lowercase					=emb.weight.data
     return lin_layer

def 				__UpperCamelCase							(	lowercase__						:  List[Any],					lowercase__						:  List[str]="uclanlp/plbart-base",					lowercase__						:  List[Any]=False,					lowercase__						:  Dict=False ):






     '''simple docstring'''





     __lowercase					=torch.load(UpperCamelCase__,					map_location='cpu' )["model"]
     remove_ignore_keys_(UpperCamelCase__ )
     __lowercase					=state_dict["encoder.embed_tokens.weight"].shape[0]

     __lowercase					=PLBartConfig.from_pretrained(UpperCamelCase__,					vocab_size=UpperCamelCase__ )

     __lowercase					=state_dict["decoder.embed_tokens.weight"]
     if not classification:
          __lowercase					=PLBartForConditionalGeneration(UpperCamelCase__ )
          model.model.load_state_dict(UpperCamelCase__ )
          if finetuned:
               __lowercase					=make_linear_from_emb(model.model.shared )

     else:
          __lowercase					={}
          for key, value in state_dict.copy().items():
               if key.startswith('classification_heads.sentence_classification_head' ):
                    __lowercase					=value
                    state_dict.pop(UpperCamelCase__ )
          __lowercase					=PLBartForSequenceClassification(UpperCamelCase__ )
          model.model.load_state_dict(UpperCamelCase__ )
          model.classification_head.load_state_dict(UpperCamelCase__ )

     return model


if __name__ == "__main__":
       UpperCAmelCase        =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('''fairseq_path''', type=str, help='''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='''uclanlp/plbart-base''',
           type=str,
           help='''Which huggingface architecture to use: plbart-base''',
       )
       parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
       parser.add_argument(
           '''--classification''', action='''store_true''', help='''whether the model is a classification checkpoint'''
       )
       UpperCAmelCase        =  parser.parse_args()
       UpperCAmelCase        =  convert_fairseq_plbart_checkpoint_from_disk(
           args.fairseq_path,
           hf_config_path=args.hf_config,
           finetuned=args.finetuned,
           classification=args.classification,
       )
       model.save_pretrained(args.pytorch_dump_folder_path)





'''simple docstring'''

from __future__ import annotations

from pathlib import Path

def 				__UpperCamelCase							(	lowercase__						:  tuple[int, int],					lowercase__						:  tuple[int, int] ):






     '''simple docstring'''





     return pointa[0] * pointa[1] - pointa[1] * pointa[0]

def 				__UpperCamelCase							(	lowercase__						:  int,					lowercase__						:  int,					lowercase__						:  int,					lowercase__						:  int,					lowercase__						:  int,					lowercase__						:  int ):






     '''simple docstring'''





     __lowercase					=(xa, ya)
     __lowercase					=(xa - xa, ya - ya)
     __lowercase					=(xa - xa, ya - ya)
     __lowercase					=-vector_product(lowercase__,					lowercase__ ) / vector_product(
         lowercase__,					lowercase__ )
     __lowercase					=+vector_product(lowercase__,					lowercase__ ) / vector_product(
         lowercase__,					lowercase__ )

     return a > 0 and b > 0 and a + b < 1

def 				__UpperCamelCase							(	lowercase__						:  str = "p102_triangles.txt" ):






     '''simple docstring'''





     __lowercase					=Path(lowercase__ ).parent.joinpath(lowercase__ ).read_text(encoding='utf-8' )

     __lowercase					=[]
     for line in data.strip().split('\n' ):
          triangles.append([int(lowercase__ ) for number in line.split(',' )] )

     __lowercase					=0
     __lowercase					=42

     for triangle in triangles:
          ret += contains_origin(*lowercase__ )

     return ret


if __name__ == "__main__":
       print(F'''{solution() = }''')


