






import fire

from utils import calculate_rouge, save_json







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


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













import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


lowerCAmelCase_           = logging.get_logger(__name__)

lowerCAmelCase_           = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_           = {
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_           = {
    'gpt-neox-20b': 2_048,
}
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   VOCAB_FILES_NAMES
		snake_case    =   PRETRAINED_VOCAB_FILES_MAP
		snake_case    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case    =   ["input_ids", "attention_mask"]
		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Dict="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       int="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       Optional[int]="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       List[Any]=False						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,)    ->   int:


						'''simple docstring'''
						super().__init__(
						    _SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,tokenizer_file=_SCREAMING_SNAKE_CASE						,unk_token=_SCREAMING_SNAKE_CASE						,bos_token=_SCREAMING_SNAKE_CASE						,eos_token=_SCREAMING_SNAKE_CASE						,add_prefix_space=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)

						A	 =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
						if pre_tok_state.get('add_prefix_space'						,_SCREAMING_SNAKE_CASE		) != add_prefix_space:
										A	 =      getattr(_SCREAMING_SNAKE_CASE						,pre_tok_state.pop('type'		)		)
										A	 =      add_prefix_space
										A	 =      pre_tok_class(**_SCREAMING_SNAKE_CASE		)

						A	 =      add_prefix_space
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None		)    ->   Tuple[str]:


						'''simple docstring'''
						A	 =      self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,name=_SCREAMING_SNAKE_CASE		)
						return tuple(_SCREAMING_SNAKE_CASE		)







		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       "Conversation"		)    ->   List[int]:


						'''simple docstring'''
						A	 =      []
						for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE		) + [self.eos_token_id]		)

						if len(_SCREAMING_SNAKE_CASE		) > self.model_max_length:
										A	 =      input_ids[-self.model_max_length :]
						return input_ids





