






'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase   :					Dict   							=					logging.get_logger(__name__)

__lowerCamelCase   :					Any   							=					'''▁'''

__lowerCamelCase   :					List[Any]   							=					{'''vocab_file''': '''sentencepiece.bpe.model'''}

__lowerCamelCase   :					Optional[int]   							=					{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

__lowerCamelCase   :					Any   							=					{
    '''facebook/xglm-564M''': 2048,
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	VOCAB_FILES_NAMES
							a__   		=	PRETRAINED_VOCAB_FILES_MAP
							a__   		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__   		=	['''input_ids''', '''attention_mask''']

							def __init__(		self			:List[str]       ,							lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:int="<s>"       ,							lowerCAmelCase__			:Dict="</s>"       ,							lowerCAmelCase__			:str="</s>"       ,							lowerCAmelCase__			:Union[str, Any]="<s>"       ,							lowerCAmelCase__			:Optional[int]="<unk>"       ,							lowerCAmelCase__			:Union[str, Any]="<pad>"       ,							lowerCAmelCase__			:Optional[Dict[str, Any]] = None       ,							**lowerCAmelCase__			:List[Any]       ,							)				->  None:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       {} if sp_model_kwargs is None else sp_model_kwargs

										# Compatibility with the original tokenizer
										snake_case_				:					Dict   =       7
										snake_case_				:					Dict   =       [F'''<madeupword{i}>''' for i in range(self.num_madeup_words				)]

										snake_case_				:					Optional[Any]   =       kwargs.get("additional_special_tokens"       ,							[]				)
										kwargs["additional_special_tokens"] += [
										    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    bos_token=lowerCAmelCase__       ,							eos_token=lowerCAmelCase__       ,							unk_token=lowerCAmelCase__       ,							sep_token=lowerCAmelCase__       ,							cls_token=lowerCAmelCase__       ,							pad_token=lowerCAmelCase__       ,							sp_model_kwargs=self.sp_model_kwargs       ,							**lowerCAmelCase__       ,							)

										snake_case_				:					List[str]   =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(str(lowerCAmelCase__				)				)
										snake_case_				:					int   =       vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										snake_case_				:					Optional[int]   =       1

										# Mimic fairseq token-to-id alignment for the first 4 token
										snake_case_				:					int   =       {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

										snake_case_				:					Optional[int]   =       len(self.sp_model				)
										snake_case_				:					int   =       {F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words				)}
										self.fairseq_tokens_to_ids.update(lowerCAmelCase__				)

										snake_case_				:					List[Any]   =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

							def __getstate__(		self			:Optional[int]				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.__dict__.copy()
										snake_case_				:					Tuple   =       None
										snake_case_				:					List[str]   =       self.sp_model.serialized_model_proto()
										return state

							def __setstate__(		self			:Dict       ,							lowerCAmelCase__			:int				)				->  int:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       d

										# for backward compatibility
										if not hasattr(self       ,							"sp_model_kwargs"				):
													snake_case_				:					Dict   =       {}

										snake_case_				:					Optional[int]   =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)

							def 							_A	(		self			:List[str]       ,							lowerCAmelCase__			:List[int]       ,							lowerCAmelCase__			:Optional[List[int]] = None				)				->  List[int]:


										'''simple docstring'''




										if token_ids_a is None:
													return [self.sep_token_id] + token_ids_a
										snake_case_				:					Optional[Any]   =       [self.sep_token_id]
										return sep + token_ids_a + sep + sep + token_ids_a

							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:List[int]       ,							lowerCAmelCase__			:Optional[List[int]] = None       ,							lowerCAmelCase__			:bool = False				)				->  List[int]:


										'''simple docstring'''




										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowerCAmelCase__       ,							token_ids_a=lowerCAmelCase__       ,							already_has_special_tokens=lowerCAmelCase__				)

										if token_ids_a is None:
													return [1] + ([0] * len(lowerCAmelCase__				))
										return [1] + ([0] * len(lowerCAmelCase__				)) + [1, 1] + ([0] * len(lowerCAmelCase__				))

							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:List[int]       ,							lowerCAmelCase__			:Optional[List[int]] = None				)				->  List[int]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       [self.sep_token_id]

										if token_ids_a is None:
													return len(sep + token_ids_a				) * [0]
										return len(sep + token_ids_a + sep + sep + token_ids_a				) * [0]

							@property
							def 							_A	(		self			:Dict				)				->  Optional[Any]:


										'''simple docstring'''




										return len(self.sp_model				) + self.fairseq_offset + self.num_madeup_words

							def 							_A	(		self			:Dict				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Dict   =       {self.convert_ids_to_tokens(lowerCAmelCase__				): i for i in range(self.vocab_size				)}
										vocab.update(self.added_tokens_encoder				)
										return vocab

							def 							_A	(		self			:int       ,							lowerCAmelCase__			:str				)				->  List[str]:


										'''simple docstring'''




										return self.sp_model.encode(lowerCAmelCase__       ,							out_type=lowerCAmelCase__				)

							def 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										snake_case_				:					Dict   =       self.sp_model.PieceToId(lowerCAmelCase__				)

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

							def 							_A	(		self			:List[str]       ,							lowerCAmelCase__			:Tuple				)				->  List[str]:


										'''simple docstring'''




										if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset				)

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Optional[int]				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       "".join(lowerCAmelCase__				).replace(lowerCAmelCase__       ,							" "				).strip()
										return out_string






							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:Optional[str] = None				)				->  Tuple[str]:


										'''simple docstring'''




										if not os.path.isdir(lowerCAmelCase__				):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
													return
										snake_case_				:					int   =       os.path.join(
										    lowerCAmelCase__       ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCAmelCase__				) and os.path.isfile(self.vocab_file				):
													copyfile(self.vocab_file       ,							lowerCAmelCase__				)
										elif not os.path.isfile(self.vocab_file				):
													with open(lowerCAmelCase__       ,							"wb"				) as fi:
																snake_case_				:					Any   =       self.sp_model.serialized_model_proto()
																fi.write(lowerCAmelCase__				)

										return (out_vocab_file,)












'''simple docstring'''




import fire

from utils import calculate_rouge, save_json

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=None     ,**__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       [x.strip() for x in open(__magic_name__						).readlines()]
			snake_case_				:					Optional[int]   =       [x.strip() for x in open(__magic_name__						).readlines()][: len(__magic_name__						)]
			snake_case_				:					List[Any]   =       calculate_rouge(__magic_name__     ,__magic_name__     ,**__magic_name__						)
			if save_path is not None:
						save_json(__magic_name__     ,__magic_name__     ,indent=__magic_name__						)
			return metrics  # these print nicely


if __name__ == "__main__":
		fire.Fire(calculate_rouge_path)




