






import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification






def  UpperCAmelCase__						(    lowerCamelCase	:			Union[str, Any]    ):
   lowerCamelCase									=[
       """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  UpperCAmelCase__						(    lowerCamelCase	:			Tuple    ):
   lowerCamelCase				,       lowerCamelCase									=emb.weight.shape
   lowerCamelCase									=nn.Linear(lowerCamelCase			,lowerCamelCase			,bias=lowerCamelCase    )
   lowerCamelCase									=emb.weight.data
   return lin_layer






def  UpperCAmelCase__						(    lowerCamelCase	:			str			,lowerCamelCase	:			Optional[int]="uclanlp/plbart-base"			,lowerCamelCase	:			Optional[int]=False			,lowerCamelCase	:			str=False    ):
   lowerCamelCase									=torch.load(lowerCamelCase			,map_location="""cpu"""    )["""model"""]
   remove_ignore_keys_(lowerCamelCase    )
   lowerCamelCase									=state_dict["""encoder.embed_tokens.weight"""].shape[0]

   lowerCamelCase									=PLBartConfig.from_pretrained(lowerCamelCase			,vocab_size=lowerCamelCase    )

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

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

   return model


if __name__ == "__main__":
   _UpperCAmelCase					:						Optional[int]         =     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					:						List[Any]         =     parser.parse_args()
   _UpperCAmelCase					:						str         =     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 json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    List[str] 	=     HerbertTokenizer
     __lowerCamelCase       :    Union[str, Any] 	=     HerbertTokenizerFast
     __lowerCamelCase       :    Union[str, Any] 	=     True





     def 						lowerCamelCase							(	self  )		->				int:
        super().setUp()

        # Use a simpler test file without japanese/chinese characters
        with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''							, encoding="""utf-8"""  ) as f_data:
           lowerCamelCase									=f_data.read().replace("""\n\n"""							, """\n"""  ).strip()

        lowerCamelCase									=[
            """<s>""",
            """</s>""",
            """l""",
            """o""",
            """w""",
            """e""",
            """r""",
            """s""",
            """t""",
            """i""",
            """d""",
            """n""",
            """w</w>""",
            """r</w>""",
            """t</w>""",
            """lo""",
            """low""",
            """er</w>""",
            """low</w>""",
            """lowest</w>""",
            """newer</w>""",
            """wider</w>""",
            """,</w>""",
            """<unk>""",
        ]
        lowerCamelCase									=dict(zip(_UpperCAmelCase							, range(len(_UpperCAmelCase  )  )  )  )
        lowerCamelCase									=["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

        lowerCamelCase									=os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["""vocab_file"""]  )
        lowerCamelCase									=os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["""merges_file"""]  )
        with open(self.vocab_file							, """w"""  ) as fp:
           fp.write(json.dumps(_UpperCAmelCase  )  )
        with open(self.merges_file							, """w"""  ) as fp:
           fp.write("""\n""".join(_UpperCAmelCase  )  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Optional[Any]:
        lowerCamelCase									="""lower newer"""
        lowerCamelCase									="""lower newer"""
        return input_text, output_text





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.tokenizer_class(vocab_file=self.vocab_file							, merges_file=self.merges_file  )

        lowerCamelCase									="""lower"""
        lowerCamelCase									=["""low""", """er</w>"""]
        lowerCamelCase									=tokenizer.tokenize(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )

        lowerCamelCase									=tokens + ["""<unk>"""]
        lowerCamelCase									=[16, 17, 23]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase  )							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        if not self.test_rust_tokenizer:
           return

        lowerCamelCase									=self.get_tokenizer()
        lowerCamelCase									=self.get_rust_tokenizer()

        lowerCamelCase									="""lower,newer"""

        lowerCamelCase									=tokenizer.tokenize(_UpperCAmelCase  )
        lowerCamelCase									=rust_tokenizer.tokenize(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )

        lowerCamelCase									=tokenizer.encode(_UpperCAmelCase							, add_special_tokens=_UpperCAmelCase  )
        lowerCamelCase									=rust_tokenizer.encode(_UpperCAmelCase							, add_special_tokens=_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )

        lowerCamelCase									=self.get_rust_tokenizer()
        lowerCamelCase									=tokenizer.encode(_UpperCAmelCase  )
        lowerCamelCase									=rust_tokenizer.encode(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.tokenizer_class.from_pretrained("""allegro/herbert-base-cased"""  )

        lowerCamelCase									=tokenizer.encode("""konstruowanie sekwencji"""							, add_special_tokens=_UpperCAmelCase  )
        lowerCamelCase									=tokenizer.encode("""konstruowanie wielu sekwencji"""							, add_special_tokens=_UpperCAmelCase  )

        lowerCamelCase									=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase  )
        lowerCamelCase									=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase							, _UpperCAmelCase  )

        assert encoded_sentence == [0] + text + [2]
        assert encoded_pair == [0] + text + [2] + text_a + [2]