




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Any   )						->							Union[str, Any]:
    UpperCAmelCase_											=  [
        '''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       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		List[Any]   )						->							Optional[Any]:
    UpperCAmelCase_				,							UpperCAmelCase_											=  emb.weight.shape
    UpperCAmelCase_											=  nn.Linear(__UpperCamelCase ,  __UpperCamelCase ,  bias=__UpperCamelCase   )
    UpperCAmelCase_											=  emb.weight.data
    return lin_layer




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Any ,  __UpperCamelCase    :		List[str]="uclanlp/plbart-base" ,  __UpperCamelCase    :		List[Any]=False ,  __UpperCamelCase    :		List[Any]=False   )						->							Tuple:
    UpperCAmelCase_											=  torch.load(__UpperCamelCase ,  map_location='''cpu'''   )['''model''']
    remove_ignore_keys_(__UpperCamelCase   )
    UpperCAmelCase_											=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

    UpperCAmelCase_											=  PLBartConfig.from_pretrained(__UpperCamelCase ,  vocab_size=__UpperCamelCase   )

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

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

    return model


if __name__ == "__main__":
 _lowerCamelCase    				=		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    				=		parser.parse_args()
 _lowerCamelCase    				=		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 unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils



class     a  (     unittest.TestCase    ):

     '''simple docstring'''





     @patch('''diffusers.utils.hub_utils.get_full_repo_name'''       )
     def 						lowerCamelCase_     (			self     :    List[Any]      ,					__snake_case     :    Mock       ):
         UpperCAmelCase_											=  '''full_repo_name'''
         with TemporaryDirectory() as tmpdir:
             # Dummy args values
             UpperCAmelCase_											=  Mock()
             UpperCAmelCase_											=  tmpdir
             UpperCAmelCase_											=  0
             UpperCAmelCase_											=  '''hub_token'''
             UpperCAmelCase_											=  '''dataset_name'''
             UpperCAmelCase_											=  0.01
             UpperCAmelCase_											=  10_00_00
             UpperCAmelCase_											=  1_00_00
             UpperCAmelCase_											=  0.01
             UpperCAmelCase_											=  0.02
             UpperCAmelCase_											=  0.03
             UpperCAmelCase_											=  0.0_005
             UpperCAmelCase_											=  0.000_001
             UpperCAmelCase_											=  1
             UpperCAmelCase_											=  10
             UpperCAmelCase_											=  0.001
             UpperCAmelCase_											=  0.1
             UpperCAmelCase_											=  0.2
             UpperCAmelCase_											=  True

             # Model card mush be rendered and saved
             diffusers.utils.hub_utils.create_model_card(__snake_case      ,					model_name='''model_name'''       )
             self.assertTrue((Path(__snake_case       ) / '''README.md''').is_file()       )





