





'''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


_A:					List[Any]							=				logging.get_logger(__name__)


_A:					str							=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
_A:					List[str]							=				{
    """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""",
    },
}

_A:					int							=				{
    """allenai/led-base-16384""": 16_384,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def _lowerCAmelCase     (      )->     int:
 __UpperCAmelCase					=   (
     list(range(ord('!'    )       ,    ord('~'    ) + 1    )    ) + list(range(ord('¡'    )       ,    ord('¬'    ) + 1    )    ) + list(range(ord('®'    )       ,    ord('ÿ'    ) + 1    )    )
 )
 __UpperCAmelCase					=   bs[:]
 __UpperCAmelCase					=   0
 for b in range(2**8    ):
  if b not in bs:
   bs.append(_lowerCAmelCase    )
   cs.append(2**8 + n    )
   n += 1
 __UpperCAmelCase					=   [chr(_lowerCAmelCase    ) for n in cs]
 return dict(zip(_lowerCAmelCase       ,    _lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase    )->     List[Any]:
 __UpperCAmelCase					=   set()
 __UpperCAmelCase					=   word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)    )
  __UpperCAmelCase					=   char
 return pairs






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Tuple						   =       VOCAB_FILES_NAMES
    _A :			List[Any]						   =       PRETRAINED_VOCAB_FILES_MAP
    _A :			Optional[int]						   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Optional[Any]						   =       ["""input_ids""", """attention_mask"""]



    def __init__(  self     ,			__A     ,			__A     ,			__A="replace"     ,			__A="<s>"     ,			__A="</s>"     ,			__A="</s>"     ,			__A="<s>"     ,			__A="<unk>"     ,			__A="<pad>"     ,			__A="<mask>"     ,			__A=False     ,			**__A     ,			):
     __UpperCAmelCase					=   AddedToken(__A     ,			lstrip=__A     ,			rstrip=__A					) if isinstance(__A     ,			__A					) else bos_token
     __UpperCAmelCase					=   AddedToken(__A     ,			lstrip=__A     ,			rstrip=__A					) if isinstance(__A     ,			__A					) else eos_token
     __UpperCAmelCase					=   AddedToken(__A     ,			lstrip=__A     ,			rstrip=__A					) if isinstance(__A     ,			__A					) else sep_token
     __UpperCAmelCase					=   AddedToken(__A     ,			lstrip=__A     ,			rstrip=__A					) if isinstance(__A     ,			__A					) else cls_token
     __UpperCAmelCase					=   AddedToken(__A     ,			lstrip=__A     ,			rstrip=__A					) if isinstance(__A     ,			__A					) else unk_token
     __UpperCAmelCase					=   AddedToken(__A     ,			lstrip=__A     ,			rstrip=__A					) if isinstance(__A     ,			__A					) else pad_token

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

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

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

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     __UpperCAmelCase					=   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 					__lowerCamelCase						(  self					):
     return len(self.encoder					)



    def 					__lowerCamelCase						(  self					):
     return dict(self.encoder     ,			**self.added_tokens_encoder					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     if token in self.cache:
      return self.cache[token]
     __UpperCAmelCase					=   tuple(__A					)
     __UpperCAmelCase					=   get_pairs(__A					)

     if not pairs:
      return token

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

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



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   []
     for token in re.findall(self.pat     ,			__A					):
      __UpperCAmelCase					=   ''.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(__A					).split(' '					)					)
     return bpe_tokens



    def 					__lowerCamelCase						(  self     ,			__A					):
     return self.encoder.get(__A     ,			self.encoder.get(self.unk_token					)					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     return self.decoder.get(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   ''.join(__A					)
     __UpperCAmelCase					=   bytearray([self.byte_decoder[c] for c in text]					).decode('utf-8'     ,			errors=self.errors					)
     return text



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):
     if not os.path.isdir(__A					):
      logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
      return
     __UpperCAmelCase					=   os.path.join(
         __A     ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
     __UpperCAmelCase					=   os.path.join(
         __A     ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']					)

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

     __UpperCAmelCase					=   0
     with open(__A     ,			'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 __A					: 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!'					)
        __UpperCAmelCase					=   token_index
       writer.write(' '.join(__A					) + '\n'					)
       index += 1

     return vocab_file, merge_file



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):
     if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     __UpperCAmelCase					=   [self.cls_token_id]
     __UpperCAmelCase					=   [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None     ,			__A = False					):
     if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=__A     ,			token_ids_a=__A     ,			already_has_special_tokens=__A					)

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



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):
     __UpperCAmelCase					=   [self.sep_token_id]
     __UpperCAmelCase					=   [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 					__lowerCamelCase						(  self     ,			__A     ,			__A=False     ,			**__A					):
     __UpperCAmelCase					=   kwargs.pop('add_prefix_space'     ,			self.add_prefix_space					)
     if (is_split_into_words or add_prefix_space) and (len(__A					) > 0 and not text[0].isspace()):
      __UpperCAmelCase					=   ' ' + text
     return (text, kwargs)




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None     ,			__A = PaddingStrategy.DO_NOT_PAD     ,			__A = None     ,			__A = None     ,			):
     __UpperCAmelCase					=   super()._pad(
         encoded_inputs=__A     ,			max_length=__A     ,			padding_strategy=__A     ,			pad_to_multiple_of=__A     ,			return_attention_mask=__A     ,			)

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

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

      if needs_to_be_padded:
       __UpperCAmelCase					=   len(__A					) - 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`
        __UpperCAmelCase					=   (
            encoded_inputs['global_attention_mask'] + [-1] * difference
        )
       elif self.padding_side == "left":
        __UpperCAmelCase					=   [-1] * difference + encoded_inputs[
            'global_attention_mask'
        ]
       else:
        raise ValueError('Invalid padding strategy:' + str(self.padding_side					)					)

     return encoded_inputs












'''simple docstring'''




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_A:					Union[str, Any]							=				get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
    _A:					Optional[Any]							=				json.load(f)






@require_torch
class UpperCAmelCase							(		unittest.TestCase							):



    def 					__lowerCamelCase						(  self     ,			__A					):
     return FSMTTokenizer.from_pretrained(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   FSMTForConditionalGeneration.from_pretrained(__A					).to(__A					)
     if torch_device == "cuda":
      model.half()
     return model




    @parameterized.expand(
        [
            ['en-ru', 2_6.0],
            ['ru-en', 2_2.0],
            ['en-de', 2_2.0],
            ['de-en', 2_9.0],
        ]					)
    @slow
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     # note: this test is not testing the best performance since it only evals a small batch
     # but it should be enough to detect a regression in the output quality
     __UpperCAmelCase					=   f'facebook/wmt19-{pair}'
     __UpperCAmelCase					=   self.get_tokenizer(__A					)
     __UpperCAmelCase					=   self.get_model(__A					)

     __UpperCAmelCase					=   bleu_data[pair]['src']
     __UpperCAmelCase					=   bleu_data[pair]['tgt']

     __UpperCAmelCase					=   tokenizer(__A     ,			return_tensors='pt'     ,			truncation=__A     ,			padding='longest'					).to(__A					)
     __UpperCAmelCase					=   model.generate(
         input_ids=batch.input_ids     ,			num_beams=8     ,			)
     __UpperCAmelCase					=   tokenizer.batch_decode(
         __A     ,			skip_special_tokens=__A     ,			clean_up_tokenization_spaces=__A					)
     __UpperCAmelCase					=   calculate_bleu(__A     ,			__A					)
     print(__A					)
     self.assertGreaterEqual(scores['bleu']     ,			__A					)





