


"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification







def     lowerCamelCase_(						_lowerCamelCase    )  ->			str:
   '''simple docstring'''





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







def     lowerCamelCase_(						_lowerCamelCase    )  ->			Union[str, Any]:
   '''simple docstring'''





   _lowerCamelCase, _lowerCamelCase					:							List[Any]				=			emb.weight.shape
   _lowerCamelCase					:							List[str]				=			nn.Linear(lowerCAmelCase_       ,						lowerCAmelCase_       ,						bias=lowerCAmelCase_    )
   _lowerCamelCase					:							Tuple				=			emb.weight.data
   return lin_layer







def     lowerCamelCase_(						_lowerCamelCase       ,						_lowerCamelCase="uclanlp/plbart-base"       ,						_lowerCamelCase=False       ,						_lowerCamelCase=False    )  ->			int:
   '''simple docstring'''





   _lowerCamelCase					:							Any				=			torch.load(lowerCAmelCase_       ,						map_location="cpu"    )["model"]
   remove_ignore_keys_(lowerCAmelCase_    )
   _lowerCamelCase					:							Tuple				=			state_dict["encoder.embed_tokens.weight"].shape[0]

   _lowerCamelCase					:							List[Any]				=			PLBartConfig.from_pretrained(lowerCAmelCase_       ,						vocab_size=lowerCAmelCase_    )

   _lowerCamelCase					:							Tuple				=			state_dict["decoder.embed_tokens.weight"]
   if not classification:
      _lowerCamelCase					:							str				=			PLBartForConditionalGeneration(lowerCAmelCase_    )
      model.model.load_state_dict(lowerCAmelCase_    )
      if finetuned:
         _lowerCamelCase					:							List[Any]				=			make_linear_from_emb(model.model.shared    )

   else:
      _lowerCamelCase					:							Optional[Any]				=			{}
      for key, value in state_dict.copy().items():
         if key.startswith("classification_heads.sentence_classification_head"    ):
            _lowerCamelCase					:							List[str]				=			value
            state_dict.pop(lowerCAmelCase_    )
      _lowerCamelCase					:							Optional[Any]				=			PLBartForSequenceClassification(lowerCAmelCase_    )
      model.model.load_state_dict(lowerCAmelCase_    )
      model.classification_head.load_state_dict(lowerCAmelCase_    )

   return model


if __name__ == "__main__":
  _lowerCAmelCase				:  Union[str, Any] 					=     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'''
  )
  _lowerCAmelCase				:  Optional[Any] 					=     parser.parse_args()
  _lowerCAmelCase				:  Tuple 					=     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)





import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class  __lowerCamelCase       (    unittest.TestCase    ):




    """simple docstring"""

    snake_case__					=      EsmTokenizer


    def    a      (       self     :     str   )      ->       Tuple:
         super().setUp()
         lowerCAmelCase__		= tempfile.mkdtemp()
         # fmt: off
         lowerCAmelCase__		= ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
         # fmt: on
         lowerCAmelCase__		= os.path.join(self.tmpdirname   ,	VOCAB_FILES_NAMES["vocab_file"]   )
         with open(self.vocab_file   ,	"w"   ,	encoding="utf-8"   ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )


    def    a      (       self     :     Dict   ,	**SCREAMING_SNAKE_CASE__     :     Optional[Any]   )      ->       List[PreTrainedTokenizerBase]:
         return [self.get_tokenizer(**SCREAMING_SNAKE_CASE__   )]


    def    a      (       self     :     List[str]   ,	**SCREAMING_SNAKE_CASE__     :     str   )      ->       PreTrainedTokenizer:
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,	**SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     str   )      ->       Tuple:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= tokenizer.tokenize("LAGVS"   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	["L", "A", "G", "V", "S"]   )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )   ,	[4, 5, 6, 7, 8]   )


    def    a      (       self     :     int   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= "LAGVS"
         self.assertListEqual(tokenizer.encode(SCREAMING_SNAKE_CASE__   )   ,	[0, 4, 5, 6, 7, 8, 2]   )


    def    a      (       self     :     Union[str, Any]   )      ->       str:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= ["LAGVS", "WCB"]
         lowerCAmelCase__		= tokenizer(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_SNAKE_CASE__   )["input_ids"]

         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]   )


    def    a      (       self     :     Any   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= ["LAGVS", "WCB"]
         lowerCAmelCase__		= tokenizer(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_SNAKE_CASE__   )["input_ids"]

         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]   )


    def    a      (       self     :     str   )      ->       Optional[Any]:
         lowerCAmelCase__		= self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__   )
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                   lowerCAmelCase__		= "<unk>"
                   lowerCAmelCase__		= "<mask>"

                   lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
                   lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )

                   self.assertEqual(len(SCREAMING_SNAKE_CASE__   )   ,	1   )
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__   )   ,	1   )
                   self.assertEqual(token_a[0]   ,	SCREAMING_SNAKE_CASE__   )
                   self.assertEqual(token_a[0]   ,	SCREAMING_SNAKE_CASE__   )
