
"""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__   :					Optional[Any]							=   logging.get_logger(__name__)


A__   :					List[Any]							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
A__   :					Optional[Any]							=   {
    '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__   :					Any							=   {
    'allenai/led-base-16384': 1_6_3_8_4,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Any       =     (
      list(range(ord('''!'''						) ,				ord('''~'''						) + 1						)						) + list(range(ord('''¡'''						) ,				ord('''¬'''						) + 1						)						) + list(range(ord('''®'''						) ,				ord('''ÿ'''						) + 1						)						)
  )
  _lowercase:			Optional[Any]       =     bs[:]
  _lowercase:			List[Any]       =     0
  for b in range(2**8						):
    if b not in bs:
      bs.append(_UpperCamelCase						)
      cs.append(2**8 + n						)
      n += 1
  _lowercase:			Optional[Any]       =     [chr(_UpperCamelCase						) for n in cs]
  return dict(zip(_UpperCamelCase ,				_UpperCamelCase						)						)




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[Any]       =     set()
  _lowercase:			Dict       =     word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)						)
    _lowercase:			Optional[Any]       =     char
  return pairs







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		VOCAB_FILES_NAMES
       UpperCamelCase_   =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_   =		['''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_ ,				)     ->	List[Any]:





         """simple docstring"""




         _lowercase:			int       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else bos_token
         _lowercase:			List[Any]       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else eos_token
         _lowercase:			int       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else sep_token
         _lowercase:			Any       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else cls_token
         _lowercase:			Union[str, Any]       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else unk_token
         _lowercase:			Dict       =     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
         _lowercase:			List[Any]       =     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:
           _lowercase:			List[Any]       =     json.load(A_       )
         _lowercase:			Optional[Any]       =     {v: k for k, v in self.encoder.items()}
         _lowercase:			Any       =     errors  # how to handle errors in decoding
         _lowercase:			Union[str, Any]       =     bytes_to_unicode()
         _lowercase:			Tuple       =     {v: k for k, v in self.byte_encoder.items()}
         with open(A_ ,				encoding='''utf-8'''       ) as merges_handle:
           _lowercase:			Union[str, Any]       =     merges_handle.read().split('''\n'''       )[1:-1]
         _lowercase:			str       =     [tuple(merge.split()       ) for merge in bpe_merges]
         _lowercase:			Optional[Any]       =     dict(zip(A_ ,				range(len(A_       )       )       )       )
         _lowercase:			Union[str, Any]       =     {}
         _lowercase:			List[str]       =     add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         _lowercase:			Any       =     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 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         return len(self.encoder       )
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         return dict(self.encoder ,				**self.added_tokens_encoder       )
       def 							lowercase_							(     self ,				A_       )     ->	List[Any]:





         """simple docstring"""




         if token in self.cache:
           return self.cache[token]
         _lowercase:			Dict       =     tuple(A_       )
         _lowercase:			List[Any]       =     get_pairs(A_       )

         if not pairs:
           return token

         while True:
           _lowercase:			Tuple       =     min(A_ ,				key=lambda A_       : self.bpe_ranks.get(A_ ,				float('''inf'''       )       )       )
           if bigram not in self.bpe_ranks:
             break
           _lowercase ,				_lowercase:			Dict       =     bigram
           _lowercase:			Union[str, Any]       =     []
           _lowercase:			List[Any]       =     0
           while i < len(A_       ):
             try:
               _lowercase:			List[str]       =     word.index(A_ ,				A_       )
             except ValueError:
               new_word.extend(word[i:]       )
               break
             else:
               new_word.extend(word[i:j]       )
               _lowercase:			Tuple       =     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
           _lowercase:			str       =     tuple(A_       )
           _lowercase:			Tuple       =     new_word
           if len(A_       ) == 1:
             break
           else:
             _lowercase:			Dict       =     get_pairs(A_       )
         _lowercase:			str       =     ''' '''.join(A_       )
         _lowercase:			Tuple       =     word
         return word
       def 							lowercase_							(     self ,				A_       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			List[str]       =     []
         for token in re.findall(self.pat ,				A_       ):
           _lowercase:			List[str]       =     ''''''.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 							lowercase_							(     self ,				A_       )     ->	List[str]:





         """simple docstring"""




         return self.encoder.get(A_ ,				self.encoder.get(self.unk_token       )       )
       def 							lowercase_							(     self ,				A_       )     ->	Any:





         """simple docstring"""




         return self.decoder.get(A_       )
       def 							lowercase_							(     self ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     ''''''.join(A_       )
         _lowercase:			Tuple       =     bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8''' ,				errors=self.errors       )
         return text
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	Tuple[str]:





         """simple docstring"""




         if not os.path.isdir(A_       ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
         _lowercase:			str       =     os.path.join(
             A_ ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
         _lowercase:			Optional[Any]       =     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'''       )

         _lowercase:			Any       =     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!'''       )
               _lowercase:			Tuple       =     token_index
             writer.write(''' '''.join(A_       ) + '''\n'''       )
             index += 1

         return vocab_file, merge_file
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _lowercase:			Optional[Any]       =     [self.cls_token_id]
         _lowercase:			Tuple       =     [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
       def 							lowercase_							(     self ,				A_ ,				A_ = None ,				A_ = False       )     ->	List[int]:





         """simple docstring"""




         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 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         _lowercase:			Dict       =     [self.sep_token_id]
         _lowercase:			str       =     [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 							lowercase_							(     self ,				A_ ,				A_=False ,				**A_       )     ->	Any:





         """simple docstring"""




         _lowercase:			Any       =     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()):
           _lowercase:			Dict       =     ''' ''' + text
         return (text, kwargs)



       def 							lowercase_							(     self ,				A_ ,				A_ = None ,				A_ = PaddingStrategy.DO_NOT_PAD ,				A_ = None ,				A_ = None ,				)     ->	dict:





         """simple docstring"""




         _lowercase:			str       =     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:
           _lowercase:			List[str]       =     '''attention_mask''' in self.model_input_names

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

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

         return encoded_inputs


"""simple docstring"""




import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
A__   :					Any							=   logging.getLogger()




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     '''\n'''.join(_UpperCamelCase						)
  Path(_UpperCamelCase						).open('''w'''						).writelines(_UpperCamelCase						)


A__   :					int							=   'patrickvonplaten/t5-tiny-random'
A__   :					Tuple							=   'sshleifer/bart-tiny-random'
A__   :					Optional[Any]							=   'sshleifer/tiny-mbart'

A__   :					Optional[int]							=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       def 							lowercase_							(     self ,				A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			str       =     Path(self.get_auto_remove_tmp_dir()       ) / '''utest_input.source'''
         _lowercase:			Tuple       =     input_file_name.parent / '''utest_output.txt'''
         assert not output_file_name.exists()
         _lowercase:			List[str]       =     [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
         _dump_articles(A_ ,				A_       )

         _lowercase:			int       =     str(Path(self.get_auto_remove_tmp_dir()       ) / '''scores.json'''       )
         _lowercase:			Optional[Any]       =     '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
         _lowercase:			str       =     f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

         with patch.object(A_ ,				'''argv''' ,				A_       ):
           run_generate()
           assert Path(A_       ).exists()
           # os.remove(Path(output_file_name))
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         self.run_eval_tester(A_       )
       @parameterized.expand([BART_TINY, MBART_TINY]       )
       @slow
       def 							lowercase_							(     self ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




         self.run_eval_tester(A_       )



       @parameterized.expand([T5_TINY, MBART_TINY]       )
       @slow
       def 							lowercase_							(     self ,				A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     Path(self.get_auto_remove_tmp_dir()       ) / '''utest_input.source'''
         _lowercase:			Dict       =     input_file_name.parent / '''utest_output.txt'''
         assert not output_file_name.exists()

         _lowercase:			int       =     {
             '''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
             '''de''': [
                 '''Maschinelles Lernen ist großartig, oder?''',
                 '''Ich esse gerne Bananen''',
                 '''Morgen ist wieder ein toller Tag!''',
             ],
         }

         _lowercase:			Tuple       =     Path(self.get_auto_remove_tmp_dir()       )
         _lowercase:			Tuple       =     str(tmp_dir / '''scores.json'''       )
         _lowercase:			List[str]       =     str(tmp_dir / '''val.target'''       )
         _dump_articles(A_ ,				text['''en''']       )
         _dump_articles(A_ ,				text['''de''']       )
         _lowercase:			Tuple       =     '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
         _lowercase:			Tuple       =     f'''
            run_eval_search.py
            {model}
            {str(A_       )}
            {str(A_       )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
         testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0''']       )

         with patch.object(A_ ,				'''argv''' ,				A_       ):
           with CaptureStdout() as cs:
             run_search()
           _lowercase:			List[str]       =     [''' num_beams | length_penalty''', model, '''Best score args''']
           _lowercase:			Union[str, Any]       =     ['''Info''']
           if "translation" in task:
             expected_strings.append('''bleu'''       )
           else:
             expected_strings.extend(A_       )
           for w in expected_strings:
             assert w in cs.out
           for w in un_expected_strings:
             assert w not in cs.out
           assert Path(A_       ).exists()
           os.remove(Path(A_       )       )
