





import json
import os
from typing import Optional, Tuple

import regex as re

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


lowerCAmelCase_    =				logging.get_logger(__name__)

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

lowerCAmelCase_    =				{
    '''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
    '''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}

lowerCAmelCase_    =				{
    '''ctrl''': 2_5_6,
}

lowerCAmelCase_    =				{
    '''Pregnancy''': 1_6_8_6_2_9,
    '''Christianity''': 7_6_7_5,
    '''Explain''': 1_0_6_4_2_3,
    '''Fitness''': 6_3_4_4_0,
    '''Saving''': 6_3_1_6_3,
    '''Ask''': 2_7_1_7_1,
    '''Ass''': 9_5_9_8_5,
    '''Joke''': 1_6_3_5_0_9,
    '''Questions''': 4_5_6_2_2,
    '''Thoughts''': 4_9_6_0_5,
    '''Retail''': 5_2_3_4_2,
    '''Feminism''': 1_6_4_3_3_8,
    '''Writing''': 1_1_9_9_2,
    '''Atheism''': 1_9_2_2_6_3,
    '''Netflix''': 4_8_6_1_6,
    '''Computing''': 3_9_6_3_9,
    '''Opinion''': 4_3_2_1_3,
    '''Alone''': 4_4_9_6_7,
    '''Funny''': 5_8_9_1_7,
    '''Gaming''': 4_0_3_5_8,
    '''Human''': 4_0_8_8,
    '''India''': 1_3_3_1,
    '''Joker''': 7_7_1_3_8,
    '''Diet''': 3_6_2_0_6,
    '''Legal''': 1_1_8_5_9,
    '''Norman''': 4_9_3_9,
    '''Tip''': 7_2_6_8_9,
    '''Weight''': 5_2_3_4_3,
    '''Movies''': 4_6_2_7_3,
    '''Running''': 2_3_4_2_5,
    '''Science''': 2_0_9_0,
    '''Horror''': 3_7_7_9_3,
    '''Confession''': 6_0_5_7_2,
    '''Finance''': 1_2_2_5_0,
    '''Politics''': 1_6_3_6_0,
    '''Scary''': 1_9_1_9_8_5,
    '''Support''': 1_2_6_5_4,
    '''Technologies''': 3_2_5_1_6,
    '''Teenage''': 6_6_1_6_0,
    '''Event''': 3_2_7_6_9,
    '''Learned''': 6_7_4_6_0,
    '''Notion''': 1_8_2_7_7_0,
    '''Wikipedia''': 3_7_5_8_3,
    '''Books''': 6_6_6_5,
    '''Extract''': 7_6_0_5_0,
    '''Confessions''': 1_0_2_7_0_1,
    '''Conspiracy''': 7_5_9_3_2,
    '''Links''': 6_3_6_7_4,
    '''Narcissus''': 1_5_0_4_2_5,
    '''Relationship''': 5_4_7_6_6,
    '''Relationships''': 1_3_4_7_9_6,
    '''Reviews''': 4_1_6_7_1,
    '''News''': 4_2_5_6,
    '''Translation''': 2_6_8_2_0,
    '''multilingual''': 1_2_8_4_0_6,
}






def      lowerCamelCase_						(				_UpperCamelCase )		->						List[str]:






				"""simple docstring"""


				snake_case_						:    int				=  set()
				snake_case_						:    str				=  word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								snake_case_						:    Union[str, Any]				=  char

				snake_case_						:    str				=  set(_UpperCamelCase )
				return pairs





class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      VOCAB_FILES_NAMES
			lowerCamelCase_     :     int       			=      PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_     :     List[Any]       			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_     :     int       			=      CONTROL_CODES






			def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__="<unk>"					,							**__magic_name__    )     ->   Tuple:


							'''simple docstring'''



							super().__init__(unk_token=__magic_name__					,							**__magic_name__    )

							with open(__magic_name__					,							encoding='''utf-8'''    ) as vocab_handle:
											snake_case_						:    Tuple				=  json.load(__magic_name__    )
							snake_case_						:    Any				=  {v: k for k, v in self.encoder.items()}
							with open(__magic_name__					,							encoding='''utf-8'''    ) as merges_handle:
											snake_case_						:    Optional[Any]				=  merges_handle.read().split('''\n'''    )[1:-1]
							snake_case_						:    Union[str, Any]				=  [tuple(merge.split()    ) for merge in merges]
							snake_case_						:    Tuple				=  dict(zip(__magic_name__					,							range(len(__magic_name__    )    )    )    )
							snake_case_						:    List[str]				=  {}






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


							'''simple docstring'''



							return len(self.encoder    )






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							return dict(self.encoder					,							**self.added_tokens_encoder    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



							if token in self.cache:
											return self.cache[token]
							snake_case_						:    Union[str, Any]				=  tuple(__magic_name__    )
							snake_case_						:    List[str]				=  tuple(list(word[:-1]    ) + [word[-1] + '''</w>''']    )
							snake_case_						:    List[Any]				=  get_pairs(__magic_name__    )

							if not pairs:
											return token

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

															if word[i] == first and i < len(__magic_name__    ) - 1 and word[i + 1] == second:
																			new_word.append(first + second    )
																			i += 2
															else:
																			new_word.append(word[i]    )
																			i += 1
											snake_case_						:    Optional[Any]				=  tuple(__magic_name__    )
											snake_case_						:    Union[str, Any]				=  new_word
											if len(__magic_name__    ) == 1:
															break
											else:
															snake_case_						:    List[Any]				=  get_pairs(__magic_name__    )
							snake_case_						:    Optional[int]				=  '''@@ '''.join(__magic_name__    )
							snake_case_						:    Tuple				=  word[:-4]
							snake_case_						:    int				=  word
							return word






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   str:


							'''simple docstring'''



							snake_case_						:    str				=  []

							snake_case_						:    Tuple				=  re.findall(R'''\S+\n?'''					,							__magic_name__    )

							for token in words:
											split_tokens.extend(list(self.bpe(__magic_name__    ).split(''' '''    )    )    )
							return split_tokens






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   str:


							'''simple docstring'''



							return self.encoder.get(__magic_name__					,							self.encoder.get(self.unk_token    )    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   str:


							'''simple docstring'''



							return self.decoder.get(__magic_name__					,							self.unk_token    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Dict				=  ''' '''.join(__magic_name__    ).replace('''@@ '''					,							''''''    ).strip()
							return out_string






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


							'''simple docstring'''



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

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

							snake_case_						:    List[str]				=  0
							with open(__magic_name__					,							'''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 __magic_name__    : 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!'''    )
																			snake_case_						:    Dict				=  token_index
															writer.write(''' '''.join(__magic_name__    ) + '''\n'''    )
															index += 1

							return vocab_file, merge_file

			# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
			#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
			#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
			#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
			#     return ''.join(tokens_generated_so_far)







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowerCAmelCase_    =				datasets.logging.get_logger(__name__)


lowerCAmelCase_    =				'''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

lowerCAmelCase_    =				'''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

lowerCAmelCase_    =				'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

lowerCAmelCase_    =				{
    '''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
    '''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
    '''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
    '''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
    '''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
    '''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
    '''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
    '''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
    '''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
    '''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,     _KWARGS_DESCRIPTION     )
class 					__lowerCAmelCase       (			datasets.Metric     ):






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							return datasets.MetricInfo(
							    description=_DESCRIPTION					,							citation=_CITATION					,							homepage='''https://github.com/google-research/bleurt'''					,							inputs_description=_KWARGS_DESCRIPTION					,							features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''					,							id='''sequence'''    ),
							            '''references''': datasets.Value('''string'''					,							id='''sequence'''    ),
							        }    )					,							codebase_urls=['''https://github.com/google-research/bleurt''']					,							reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']					,							)






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[Any]:


							'''simple docstring'''



							if self.config_name == "default":
											logger.warning(
											    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
											    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''    )
											snake_case_						:    Dict				=  '''bleurt-base-128'''

							if self.config_name.lower() in CHECKPOINT_URLS:
											snake_case_						:    Optional[int]				=  self.config_name.lower()

							elif self.config_name.upper() in CHECKPOINT_URLS:
											snake_case_						:    Union[str, Any]				=  self.config_name.upper()

							else:
											raise KeyError(
											    F'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''    )

							# download the model checkpoint specified by self.config_name and set up the scorer
							snake_case_						:    Any				=  dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]    )
							snake_case_						:    Dict				=  score.BleurtScorer(os.path.join(__magic_name__					,							__magic_name__    )    )






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


							'''simple docstring'''



							snake_case_						:    Dict				=  self.scorer.score(references=__magic_name__					,							candidates=__magic_name__    )
							return {"scores": scores}
