


"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 					__magic_name__		(			__UpperCAmelCase     ):






       def __init__(   self  :    Any  ,     snake_case__  :    List[str]=""  ,     snake_case__  :    List[Any]="train"				):

              '''simple docstring'''






              assert os.path.isdir(snake_case__				)

              lowercase		:Union[str, Any]       							=				[]
              lowercase		:str       							=				os.listdir(snake_case__				)
              for story_filename in story_filenames_list:
                     if "summary" in story_filename:
                            continue
                     lowercase		:int       							=				os.path.join(snake_case__  ,     snake_case__				)
                     if not os.path.isfile(snake_case__				):
                            continue
                     self.documents.append(snake_case__				)






       def __len__(   self  :    List[str]				):

              '''simple docstring'''






              return len(self.documents				)







       def __getitem__(   self  :    Union[str, Any]  ,     snake_case__  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Dict       							=				self.documents[idx]
              lowercase		:Optional[int]       							=				document_path.split('''/'''				)[-1]
              with open(snake_case__  ,     encoding='''utf-8'''				) as source:
                     lowercase		:str       							=				source.read()
                     lowercase							,  lowercase		:str       							=				process_story(snake_case__				)
              return document_name, story_lines, summary_lines







def       lowerCamelCase					(a_						:Tuple)     ->							List[Any]:
       lowercase		:Optional[Any]       							=				list(filter(lambda a_: len(a_) != 0				,				[line.strip() for line in raw_story.split('''\n''')]))

       # for some unknown reason some lines miss a period, add it
       lowercase		:Tuple       							=				[_add_missing_period(a_) for line in nonempty_lines]

       # gather article lines
       lowercase		:Optional[int]       							=				[]
       lowercase		:str       							=				deque(a_)
       while True:
              try:
                     lowercase		:Optional[Any]       							=				lines.popleft()
                     if element.startswith('''@highlight'''):
                            break
                     story_lines.append(a_)
              except IndexError:
                     # if "@highlight" is absent from the file we pop
                     # all elements until there is None, raising an exception.
                     return story_lines, []

    # gather summary lines
       lowercase		:Tuple       							=				list(filter(lambda a_: not t.startswith('''@highlight''')				,				a_))

       return story_lines, summary_lines






def       lowerCamelCase					(a_						:List[Any])     ->							Optional[int]:
       lowercase		:int       							=				['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
       if line.startswith('''@highlight'''):
              return line
       if line[-1] in END_TOKENS:
              return line
       return line + "."






def       lowerCamelCase					(a_						:List[Any]				,				a_						:Optional[Any]				,				a_						:Union[str, Any])     ->							List[Any]:
       if len(a_) > block_size:
              return sequence[:block_size]
       else:
              sequence.extend([pad_token_id] * (block_size - len(a_)))
              return sequence






def       lowerCamelCase					(a_						:Tuple				,				a_						:Union[str, Any])     ->							Union[str, Any]:
       lowercase		:Any       							=				torch.ones_like(a_)
       lowercase		:Union[str, Any]       							=				sequence == pad_token_id
       lowercase		:str       							=				0
       return mask






def       lowerCamelCase					(a_						:Optional[int]				,				a_						:Any				,				a_						:Union[str, Any])     ->							List[str]:
       lowercase		:Optional[Any]       							=				[tokenizer.encode(a_) for line in story_lines]
       lowercase		:int       							=				[token for sentence in story_lines_token_ids for token in sentence]
       lowercase		:int       							=				[tokenizer.encode(a_) for line in summary_lines]
       lowercase		:Optional[int]       							=				[token for sentence in summary_lines_token_ids for token in sentence]

       return story_token_ids, summary_token_ids






def       lowerCamelCase					(a_						:Dict				,				a_						:str)     ->							Optional[int]:
       lowercase		:int       							=				[]
       for sequence in batch:
              lowercase		:Dict       							=				-1
              lowercase		:List[str]       							=				[]
              for s in sequence:
                     if s == separator_token_id:
                            sentence_num += 1
                     embeddings.append(sentence_num % 2)
              batch_embeddings.append(a_)
       return torch.tensor(a_)





"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def       lowerCamelCase					(a_						:Dict)     ->							Dict:
       lowercase		:Tuple       							=				[
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(a_				,				a_)






def       lowerCamelCase					(a_						:Union[str, Any])     ->							str:
       lowercase							,  lowercase		:Tuple       							=				emb.weight.shape
       lowercase		:List[str]       							=				nn.Linear(a_				,				a_				,				bias=a_)
       lowercase		:List[str]       							=				emb.weight.data
       return lin_layer






def       lowerCamelCase					(a_						:int				,				a_						:Union[str, Any]="facebook/mbart-large-en-ro"				,				a_						:Union[str, Any]=False				,				a_						:List[Any]=False)     ->							List[Any]:
       lowercase		:List[Any]       							=				torch.load(a_				,				map_location='''cpu''')['''model''']
       remove_ignore_keys_(a_)
       lowercase		:Dict       							=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase		:Tuple       							=				MBartConfig.from_pretrained(a_				,				vocab_size=a_)
       if mbart_aa and finetuned:
              lowercase		:List[Any]       							=				'''relu'''

       lowercase		:Optional[int]       							=				state_dict['''decoder.embed_tokens.weight''']
       lowercase		:Union[str, Any]       							=				MBartForConditionalGeneration(a_)
       model.model.load_state_dict(a_)

       if finetuned:
              lowercase		:Dict       							=				make_linear_from_emb(model.model.shared)

       return model


if __name__ == "__main__":
 UpperCAmelCase      		=			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='''facebook/mbart-large-cc25''',
     type=str,
     help='''Which huggingface architecture to use: mbart-large''',
 )
 parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
 parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
 UpperCAmelCase      		=			parser.parse_args()
 UpperCAmelCase      		=			convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)

