






'''simple docstring'''



import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__UpperCamelCase   :							Tuple         =   logging.get_logger(__name__)


__UpperCamelCase   :							Optional[Any]         =   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
__UpperCamelCase   :							Optional[int]         =   {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__UpperCamelCase   :							Dict         =   {
    """allenai/led-base-16384""": 16384,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def  __UpperCAmelCase						(					)	->      List[Any]:

   """simple docstring"""
   __a					  =     (
       list(range(ord('!'     ),       ord('~'     ) + 1     )     ) + list(range(ord('¡'     ),       ord('¬'     ) + 1     )     ) + list(range(ord('®'     ),       ord('ÿ'     ) + 1     )     )
   )
   __a					  =     bs[:]
   __a					  =     0
   for b in range(2**8     ):
      if b not in bs:
         bs.append(SCREAMING_SNAKE_CASE__     )
         cs.append(2**8 + n     )
         n += 1
   __a					  =     [chr(SCREAMING_SNAKE_CASE__     ) for n in cs]
   return dict(zip(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any]     )	->      Optional[int]:

   """simple docstring"""
   __a					  =     set()
   __a					  =     word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)     )
      __a					  =     char
   return pairs



class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =VOCAB_FILES_NAMES
      __a	       =PRETRAINED_VOCAB_FILES_MAP
      __a	       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a	       =["input_ids", "attention_mask"]




      def __init__(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase="replace"			, lowerCamelCase="<s>"			, lowerCamelCase="</s>"			, lowerCamelCase="</s>"			, lowerCamelCase="<s>"			, lowerCamelCase="<unk>"			, lowerCamelCase="<pad>"			, lowerCamelCase="<mask>"			, lowerCamelCase=False			, **lowerCamelCase			, )				->str:



         '''simple docstring'''
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else bos_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else eos_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else sep_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else cls_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else unk_token
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         __a					  =     AddedToken(lowerCamelCase			, lstrip=lowerCamelCase			, rstrip=lowerCamelCase   ) if isinstance(lowerCamelCase			, lowerCamelCase   ) else mask_token

         super().__init__(
             errors=lowerCamelCase			, bos_token=lowerCamelCase			, eos_token=lowerCamelCase			, unk_token=lowerCamelCase			, sep_token=lowerCamelCase			, cls_token=lowerCamelCase			, pad_token=lowerCamelCase			, mask_token=lowerCamelCase			, add_prefix_space=lowerCamelCase			, **lowerCamelCase			, )

         with open(lowerCamelCase			, encoding='utf-8'   ) as vocab_handle:
            __a					  =     json.load(lowerCamelCase   )
         __a					  =     {v: k for k, v in self.encoder.items()}
         __a					  =     errors  # how to handle errors in decoding
         __a					  =     bytes_to_unicode()
         __a					  =     {v: k for k, v in self.byte_encoder.items()}
         with open(lowerCamelCase			, encoding='utf-8'   ) as merges_handle:
            __a					  =     merges_handle.read().split('\n'   )[1:-1]
         __a					  =     [tuple(merge.split()   ) for merge in bpe_merges]
         __a					  =     dict(zip(lowerCamelCase			, range(len(lowerCamelCase   )   )   )   )
         __a					  =     {}
         __a					  =     add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         __a					  =     re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'   )




      @property
      # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         return len(self.encoder   )




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         return dict(self.encoder			, **self.added_tokens_encoder   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         if token in self.cache:
            return self.cache[token]
         __a					  =     tuple(lowerCamelCase   )
         __a					  =     get_pairs(lowerCamelCase   )

         if not pairs:
            return token

         while True:
            __a					  =     min(lowerCamelCase			, key=lambda lowerCamelCase   : self.bpe_ranks.get(lowerCamelCase			, float('inf'   )   )   )
            if bigram not in self.bpe_ranks:
               break
            __a  ,      __a					  =     bigram
            __a					  =     []
            __a					  =     0
            while i < len(lowerCamelCase   ):
               try:
                  __a					  =     word.index(lowerCamelCase			, lowerCamelCase   )
               except ValueError:
                  new_word.extend(word[i:]   )
                  break
               else:
                  new_word.extend(word[i:j]   )
                  __a					  =     j

               if word[i] == first and i < len(lowerCamelCase   ) - 1 and word[i + 1] == second:
                  new_word.append(first + second   )
                  i += 2
               else:
                  new_word.append(word[i]   )
                  i += 1
            __a					  =     tuple(lowerCamelCase   )
            __a					  =     new_word
            if len(lowerCamelCase   ) == 1:
               break
            else:
               __a					  =     get_pairs(lowerCamelCase   )
         __a					  =     ' '.join(lowerCamelCase   )
         __a					  =     word
         return word




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->int:



         '''simple docstring'''
         __a					  =     []
         for token in re.findall(self.pat			, lowerCamelCase   ):
            __a					  =     ''.join(
                self.byte_encoder[b] for b in token.encode('utf-8'   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase   ).split(' '   )   )
         return bpe_tokens




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         return self.encoder.get(lowerCamelCase			, self.encoder.get(self.unk_token   )   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Any:



         '''simple docstring'''
         return self.decoder.get(lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     ''.join(lowerCamelCase   )
         __a					  =     bytearray([self.byte_decoder[c] for c in text]   ).decode('utf-8'			, errors=self.errors   )
         return text




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->Tuple[str]:



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

         with open(lowerCamelCase			, 'w'			, encoding='utf-8'   ) as f:
            f.write(json.dumps(self.encoder			, indent=2			, sort_keys=lowerCamelCase			, ensure_ascii=lowerCamelCase   ) + '\n'   )

         __a					  =     0
         with open(lowerCamelCase			, 'w'			, encoding='utf-8'   ) as writer:
            writer.write('#version: 0.2\n'   )
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			, key=lambda lowerCamelCase   : kv[1]   ):
               if index != token_index:
                  logger.warning(
                      F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                      ' Please check that the tokenizer is not corrupted!'   )
                  __a					  =     token_index
               writer.write(' '.join(lowerCamelCase   ) + '\n'   )
               index += 1

         return vocab_file, merge_file




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __a					  =     [self.cls_token_id]
         __a					  =     [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = 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   )) + [1]
         return [1] + ([0] * len(lowerCamelCase   )) + [1, 1] + ([0] * len(lowerCamelCase   )) + [1]




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         __a					  =     [self.sep_token_id]
         __a					  =     [self.cls_token_id]

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




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase=False			, **lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     kwargs.pop('add_prefix_space'			, self.add_prefix_space   )
         if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase   ) > 0 and not text[0].isspace()):
            __a					  =     ' ' + text
         return (text, kwargs)






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = PaddingStrategy.DO_NOT_PAD			, lowerCamelCase = None			, lowerCamelCase = None			, )				->dict:



         '''simple docstring'''
         __a					  =     super()._pad(
             encoded_inputs=lowerCamelCase			, max_length=lowerCamelCase			, padding_strategy=lowerCamelCase			, pad_to_multiple_of=lowerCamelCase			, return_attention_mask=lowerCamelCase			, )

         # Load from model defaults
         if return_attention_mask is None:
            __a					  =     'attention_mask' in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
            __a					  =     encoded_inputs[self.model_input_names[0]]
            # `global_attention_mask` need to have the same length as other (sequential) inputs.
            __a					  =     len(encoded_inputs['global_attention_mask']   ) != len(lowerCamelCase   )

            if needs_to_be_padded:
               __a					  =     len(lowerCamelCase   ) - len(encoded_inputs['global_attention_mask']   )

               if self.padding_side == "right":
                  # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                  __a					  =     (
                      encoded_inputs['global_attention_mask'] + [-1] * difference
                  )
               elif self.padding_side == "left":
                  __a					  =     [-1] * difference + encoded_inputs[
                      'global_attention_mask'
                  ]
               else:
                  raise ValueError('Invalid padding strategy:' + str(self.padding_side   )   )

         return encoded_inputs







'''simple docstring'''



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__UpperCamelCase   :							int         =   getLogger(__name__)


__UpperCamelCase   :							int         =   """cuda""" if torch.cuda.is_available() else """cpu"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int = 8,       SCREAMING_SNAKE_CASE__:						str = DEFAULT_DEVICE,       SCREAMING_SNAKE_CASE__:						Any=False,       SCREAMING_SNAKE_CASE__:						Tuple="summarization",       SCREAMING_SNAKE_CASE__:						List[Any]=None,       **SCREAMING_SNAKE_CASE__:						int,       )	->      Dict:

   """simple docstring"""
   __a					  =     Path(SCREAMING_SNAKE_CASE__     ).open('w',       encoding='utf-8'     )
   __a					  =     str(SCREAMING_SNAKE_CASE__     )
   __a					  =     AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )
   if fpaa:
      __a					  =     model.half()

   __a					  =     AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__     )
   logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.

   __a					  =     time.time()
   # update config with task specific params
   use_task_specific_params(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   if prefix is None:
      __a					  =     prefix or getattr(model.config,       'prefix',       ''     ) or ''
   for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )     )     ):
      __a					  =     [prefix + text for text in examples_chunk]
      __a					  =     tokenizer(SCREAMING_SNAKE_CASE__,       return_tensors='pt',       truncation=SCREAMING_SNAKE_CASE__,       padding='longest'     ).to(SCREAMING_SNAKE_CASE__     )
      __a					  =     model.generate(
          input_ids=batch.input_ids,       attention_mask=batch.attention_mask,       **SCREAMING_SNAKE_CASE__,       )
      __a					  =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE__,       skip_special_tokens=SCREAMING_SNAKE_CASE__,       clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     )
      for hypothesis in dec:
         fout.write(hypothesis + '\n'     )
         fout.flush()
   fout.close()
   __a					  =     int(time.time() - start_time     )  # seconds
   __a					  =     len(SCREAMING_SNAKE_CASE__     )
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs,       4     )}



def  __UpperCAmelCase						(					)	->      List[str]:

   """simple docstring"""
   return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int]=True     )	->      List[Any]:

   """simple docstring"""
   __a					  =     argparse.ArgumentParser()
   parser.add_argument('model_name',       type=SCREAMING_SNAKE_CASE__,       help='like facebook/bart-large-cnn,t5-base, etc.'     )
   parser.add_argument('input_path',       type=SCREAMING_SNAKE_CASE__,       help='like cnn_dm/test.source'     )
   parser.add_argument('save_path',       type=SCREAMING_SNAKE_CASE__,       help='where to save summaries'     )
   parser.add_argument('--reference_path',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       help='like cnn_dm/test.target'     )
   parser.add_argument('--score_path',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       default='metrics.json',       help='where to save metrics'     )
   parser.add_argument('--device',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       default=SCREAMING_SNAKE_CASE__,       help='cuda, cuda:1, cpu etc.'     )
   parser.add_argument(
       '--prefix',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       default=SCREAMING_SNAKE_CASE__,       help='will be added to the begininng of src examples'     )
   parser.add_argument('--task',       type=SCREAMING_SNAKE_CASE__,       default='summarization',       help='used for task_specific_params + metrics'     )
   parser.add_argument('--bs',       type=SCREAMING_SNAKE_CASE__,       default=8,       required=SCREAMING_SNAKE_CASE__,       help='batch size'     )
   parser.add_argument(
       '--n_obs',       type=SCREAMING_SNAKE_CASE__,       default=-1,       required=SCREAMING_SNAKE_CASE__,       help='How many observations. Defaults to all.'     )
   parser.add_argument('--fp16',       action='store_true'     )
   parser.add_argument('--dump-args',       action='store_true',       help='print the custom hparams with the results'     )
   parser.add_argument(
       '--info',       nargs='?',       type=SCREAMING_SNAKE_CASE__,       const=datetime_now(),       help=(
           'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
           ' lang=en-ru. If no value is passed, the current datetime string will be used.'
       ),       )
   # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
   __a  ,      __a					  =     parser.parse_known_args()
   __a					  =     parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__     )
   if parsed_args and verbose:
      print(f"""parsed the following generate kwargs: {parsed_args}"""     )
   __a					  =     [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path     ).readlines()]
   if args.n_obs > 0:
      __a					  =     examples[: args.n_obs]
   Path(args.save_path     ).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__     )

   if args.reference_path is None and Path(args.score_path     ).exists():
      warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""     )

   if args.device == "cpu" and args.fpaa:
      # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
      raise ValueError('Can\'t mix --fp16 and --device cpu'     )

   __a					  =     generate_summaries_or_translations(
       SCREAMING_SNAKE_CASE__,       args.save_path,       args.model_name,       batch_size=args.bs,       device=args.device,       fpaa=args.fpaa,       task=args.task,       prefix=args.prefix,       **SCREAMING_SNAKE_CASE__,       )

   if args.reference_path is None:
      return {}

   # Compute scores
   __a					  =     calculate_bleu if 'translation' in args.task else calculate_rouge
   __a					  =     [x.rstrip() for x in open(args.save_path     ).readlines()]
   __a					  =     [x.rstrip() for x in open(args.reference_path     ).readlines()][: len(SCREAMING_SNAKE_CASE__     )]
   __a					  =     score_fn(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   scores.update(SCREAMING_SNAKE_CASE__     )

   if args.dump_args:
      scores.update(SCREAMING_SNAKE_CASE__     )
   if args.info:
      __a					  =     args.info

   if verbose:
      print(SCREAMING_SNAKE_CASE__     )

   if args.score_path is not None:
      json.dump(SCREAMING_SNAKE_CASE__,       open(args.score_path,       'w'     )     )

   return scores


if __name__ == "__main__":
 # Usage for MT:
 # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
 run_generate(verbose=True)