



'''simple docstring'''

import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {'''vocab_file''': '''spiece.model'''}

__lowerCAmelCase						      =       {
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

__lowerCAmelCase						      =       {
    '''google/bigbird-roberta-base''': 4_096,
    '''google/bigbird-roberta-large''': 4_096,
    '''google/bigbird-base-trivia-itc''': 4_096,
}

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Tuple           = VOCAB_FILES_NAMES
							lowerCAmelCase	:   Dict           = PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase	:   List[Any]           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase	:   int           = ['input_ids', 'attention_mask']
							lowerCAmelCase	:   List[int]           = []







							def __init__(							self :	int     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	List[str]="<unk>"     ,_UpperCAmelCase :	str="<s>"     ,_UpperCAmelCase :	Any="</s>"     ,_UpperCAmelCase :	Any="<pad>"     ,_UpperCAmelCase :	Tuple="[SEP]"     ,_UpperCAmelCase :	int="[MASK]"     ,_UpperCAmelCase :	List[str]="[CLS]"     ,_UpperCAmelCase :	Optional[Dict[str, Any]] = None     ,**_UpperCAmelCase :	List[str]     ,):
									_a					:				List[Any]				=							AddedToken(_UpperCAmelCase     ,lstrip=_UpperCAmelCase     ,rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) else bos_token
									_a					:				Dict				=							AddedToken(_UpperCAmelCase     ,lstrip=_UpperCAmelCase     ,rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) else eos_token
									_a					:				List[Any]				=							AddedToken(_UpperCAmelCase     ,lstrip=_UpperCAmelCase     ,rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) else unk_token
									_a					:				List[str]				=							AddedToken(_UpperCAmelCase     ,lstrip=_UpperCAmelCase     ,rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) else pad_token
									_a					:				Dict				=							AddedToken(_UpperCAmelCase     ,lstrip=_UpperCAmelCase     ,rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) else cls_token
									_a					:				Optional[int]				=							AddedToken(_UpperCAmelCase     ,lstrip=_UpperCAmelCase     ,rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) else sep_token

									# Mask token behave like a normal word, i.e. include the space before it
									_a					:				Optional[Any]				=							AddedToken(_UpperCAmelCase     ,lstrip=_UpperCAmelCase     ,rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) else mask_token

									_a					:				Optional[int]				=							{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_UpperCAmelCase     ,eos_token=_UpperCAmelCase     ,unk_token=_UpperCAmelCase     ,pad_token=_UpperCAmelCase     ,sep_token=_UpperCAmelCase     ,mask_token=_UpperCAmelCase     ,cls_token=_UpperCAmelCase     ,sp_model_kwargs=self.sp_model_kwargs     ,**_UpperCAmelCase     ,)

									_a					:				int				=							vocab_file

									_a					:				Optional[int]				=							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(_UpperCAmelCase		)







							@property
							def 							__lowercase      (							self :	Union[str, Any]		):
									return self.sp_model.get_piece_size()







							def 							__lowercase      (							self :	str		):
									_a					:				Dict				=							{self.convert_ids_to_tokens(_UpperCAmelCase		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab







							def __getstate__(							self :	List[Any]		):
									_a					:				List[str]				=							self.__dict__.copy()
									_a					:				Union[str, Any]				=							None
									return state







							def __setstate__(							self :	List[Any]     ,_UpperCAmelCase :	Dict		):
									_a					:				List[str]				=							d

									# for backward compatibility
									if not hasattr(self     ,'sp_model_kwargs'		):
											_a					:				Optional[Any]				=							{}

									_a					:				Optional[int]				=							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(self.vocab_file		)







							def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	str		):
									return self.sp_model.encode(_UpperCAmelCase     ,out_type=_UpperCAmelCase		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Any		):
									return self.sp_model.piece_to_id(_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Tuple		):
									_a					:				Tuple				=							self.sp_model.IdToPiece(_UpperCAmelCase		)
									return token







							def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	Optional[int]		):
									_a					:				Union[str, Any]				=							[]
									_a					:				int				=							''
									_a					:				List[Any]				=							False
									for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
													if not prev_is_special:
															out_string += " "
													out_string += self.sp_model.decode(_UpperCAmelCase		) + token
													_a					:				Dict				=							True
													_a					:				Tuple				=							[]
											else:
													current_sub_tokens.append(_UpperCAmelCase		)
													_a					:				List[str]				=							False
									out_string += self.sp_model.decode(_UpperCAmelCase		)
									return out_string.strip()







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	List[int]     ,_UpperCAmelCase :	bool = False     ,_UpperCAmelCase :	bool = None     ,_UpperCAmelCase :	bool = True     ,**_UpperCAmelCase :	List[str]     ,):
									_a					:				Optional[Any]				=							kwargs.pop('use_source_tokenizer'     ,_UpperCAmelCase		)

									_a					:				List[Any]				=							self.convert_ids_to_tokens(_UpperCAmelCase     ,skip_special_tokens=_UpperCAmelCase		)

									# To avoid mixing byte-level and unicode for byte-level BPT
									# we need to build string separately for added tokens and byte-level tokens
									# cf. https://github.com/huggingface/transformers/issues/1133
									_a					:				str				=							[]
									_a					:				int				=							[]
									for token in filtered_tokens:
											if skip_special_tokens and token in self.all_special_ids:
													continue
											if token in self.added_tokens_encoder:
													if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(_UpperCAmelCase		)		)
															_a					:				Dict				=							[]
													sub_texts.append(_UpperCAmelCase		)
											else:
													current_sub_text.append(_UpperCAmelCase		)
									if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(_UpperCAmelCase		)		)

									# Mimic the behavior of the Rust tokenizer:
									# No space before [MASK] and [SEP]
									if spaces_between_special_tokens:
											_a					:				List[str]				=							re.sub(R' (\[(MASK|SEP)\])'     ,R'\1'     ,' '.join(_UpperCAmelCase		)		)
									else:
											_a					:				str				=							''.join(_UpperCAmelCase		)

									_a					:				str				=							(
									    clean_up_tokenization_spaces
									    if clean_up_tokenization_spaces is not None
									    else self.clean_up_tokenization_spaces
									)
									if clean_up_tokenization_spaces:
											_a					:				List[str]				=							self.clean_up_tokenization(_UpperCAmelCase		)
											return clean_text
									else:
											return text







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[str] = None		):
									if not os.path.isdir(_UpperCAmelCase		):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
											return
									_a					:				str				=							os.path.join(
									    _UpperCAmelCase     ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(_UpperCAmelCase		) and os.path.isfile(self.vocab_file		):
											copyfile(self.vocab_file     ,_UpperCAmelCase		)
									elif not os.path.isfile(self.vocab_file		):
											with open(_UpperCAmelCase     ,'wb'		) as fi:
													_a					:				Optional[int]				=							self.sp_model.serialized_model_proto()
													fi.write(_UpperCAmelCase		)

									return (out_vocab_file,)







							def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	List[int]     ,_UpperCAmelCase :	Optional[List[int]] = None		):
									if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_a					:				Any				=							[self.cls_token_id]
									_a					:				str				=							[self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	List[int]     ,_UpperCAmelCase :	Optional[List[int]] = None     ,_UpperCAmelCase :	bool = False		):
									if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=_UpperCAmelCase     ,token_ids_a=_UpperCAmelCase     ,already_has_special_tokens=_UpperCAmelCase		)

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







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	List[int]     ,_UpperCAmelCase :	Optional[List[int]] = None		):
									_a					:				Any				=							[self.sep_token_id]
									_a					:				Optional[int]				=							[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		) * [0] + len(token_ids_a + sep		) * [1]









'''simple docstring'''

import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
__lowerCAmelCase						      =       pytest.mark.integration


__lowerCAmelCase						      =       {'''comet'''}
__lowerCAmelCase						      =       importlib.util.find_spec('''fairseq''') is not None

__lowerCAmelCase						      =       {'''code_eval'''}
__lowerCAmelCase						      =       os.name == '''nt'''

__lowerCAmelCase						      =       {'''bertscore''', '''frugalscore''', '''perplexity'''}
__lowerCAmelCase						      =       importlib.util.find_spec('''transformers''') is not None



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Any:
		@wraps(lowerCAmelCase_  )
		def wrapper(self						,      lowerCAmelCase_  ):
				if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
						self.skipTest('"test requires Fairseq"'  )
				else:
						test_case(self						,      lowerCAmelCase_  )

		return wrapper



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:
		@wraps(lowerCAmelCase_  )
		def wrapper(self						,      lowerCAmelCase_  ):
				if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
						self.skipTest('"test requires transformers"'  )
				else:
						test_case(self						,      lowerCAmelCase_  )

		return wrapper



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				int:
		@wraps(lowerCAmelCase_  )
		def wrapper(self						,      lowerCAmelCase_  ):
				if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
						self.skipTest('"test not supported on Windows"'  )
				else:
						test_case(self						,      lowerCAmelCase_  )

		return wrapper



def 					__lowerCamelCase					(				)      ->				Tuple:
		_a					:				Optional[int]				=							[metric_dir.split(os.sep  )[-2] for metric_dir in glob.glob('./metrics/*/'  )]
		return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished

@parameterized.named_parameters(get_local_metric_names()       )
@for_all_test_methods(
    _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase       )
@local
class      __magic_name__						(   parameterized.TestCase       ):
							lowerCAmelCase	:   List[str]           = {}
							lowerCAmelCase	:   Optional[int]           = None







							@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'		)
							@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'		)
							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				Tuple				=							'[...]'
									_a					:				Dict				=							importlib.import_module(
									    datasets.load.metric_module_factory(os.path.join('metrics'     ,_UpperCAmelCase		)		).module_path		)
									_a					:				Optional[int]				=							datasets.load.import_main_class(metric_module.__name__     ,dataset=_UpperCAmelCase		)
									# check parameters
									_a					:				Optional[int]				=							inspect.signature(metric._compute		).parameters
									self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()		)		)  # no **kwargs
									# run doctest
									with self.patch_intensive_calls(_UpperCAmelCase     ,metric_module.__name__		):
											with self.use_local_metrics():
													try:
															_a					:				Optional[int]				=							doctest.testmod(_UpperCAmelCase     ,verbose=_UpperCAmelCase     ,raise_on_error=_UpperCAmelCase		)
													except doctest.UnexpectedException as e:
															raise e.exc_info[1]  # raise the exception that doctest caught
									self.assertEqual(results.failed     ,0		)
									self.assertGreater(results.attempted     ,1		)







							@slow
							def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	Dict		):
									_a					:				Tuple				=							'[...]'
									_a					:				Optional[Any]				=							importlib.import_module(
									    datasets.load.metric_module_factory(os.path.join('metrics'     ,_UpperCAmelCase		)		).module_path		)
									# run doctest
									with self.use_local_metrics():
											_a					:				int				=							doctest.testmod(_UpperCAmelCase     ,verbose=_UpperCAmelCase     ,raise_on_error=_UpperCAmelCase		)
									self.assertEqual(results.failed     ,0		)
									self.assertGreater(results.attempted     ,1		)







							@contextmanager
							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	List[str]		):
									if metric_name in self.INTENSIVE_CALLS_PATCHER:
											with self.INTENSIVE_CALLS_PATCHER[metric_name](_UpperCAmelCase		):
													yield
									else:
											yield







							@contextmanager
							def 							__lowercase      (							self :	Optional[int]		):
									def load_local_metric(_UpperCAmelCase :	Tuple     ,*_UpperCAmelCase :	Dict     ,**_UpperCAmelCase :	Tuple		):
											return load_metric(os.path.join('metrics'     ,_UpperCAmelCase		)     ,*_UpperCAmelCase     ,**_UpperCAmelCase		)

									with patch('datasets.load_metric'		) as mock_load_metric:
											_a					:				Any				=							load_local_metric
											yield







							@classmethod
							def 							__lowercase      (							cls :	str     ,_UpperCAmelCase :	List[str]		):
									def wrapper(_UpperCAmelCase :	int		):
											_a					:				Optional[Any]				=							contextmanager(_UpperCAmelCase		)
											_a					:				Optional[int]				=							patcher
											return patcher

									return wrapper



@LocalMetricTest.register_intensive_calls_patcher('bleurt'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				List[str]:
		import tensorflow.compat.va as tf
		from bleurt.score import Predictor

		tf.flags.DEFINE_string('sv'						,      ''						,      ''  )  # handle pytest cli flags

		class      __magic_name__						(   _UpperCamelCase       ):







									def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Union[str, Any]		):
											assert len(input_dict['input_ids']		) == 2
											return np.array([1.03, 1.04]		)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
		with patch('bleurt.score._create_predictor'  ) as mock_create_predictor:
				_a					:				int				=							MockedPredictor()
				yield



@LocalMetricTest.register_intensive_calls_patcher('bertscore'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:
		import torch

		def bert_cos_score_idf(lowerCAmelCase_						,      lowerCAmelCase_						,      *lowerCAmelCase_						,      **lowerCAmelCase_  ):
				return torch.tensor([[1.0, 1.0, 1.0]] * len(lowerCAmelCase_  )  )

		# mock get_model which is supposed to do download a bert model
		# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
		with patch('bert_score.scorer.get_model'  ), patch(
		    'bert_score.scorer.bert_cos_score_idf'  ) as mock_bert_cos_score_idf:
				_a					:				Any				=							bert_cos_score_idf
				yield



@LocalMetricTest.register_intensive_calls_patcher('comet'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Dict:
		def load_from_checkpoint(lowerCAmelCase_  ):

				class      __magic_name__						:







											def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Dict     ,*_UpperCAmelCase :	int     ,**_UpperCAmelCase :	str		):
													assert len(_UpperCAmelCase		) == 2
													_a					:				Dict				=							[0.19, 0.92]
													return scores, sum(_UpperCAmelCase		) / len(_UpperCAmelCase		)
				return Model()

		# mock load_from_checkpoint which is supposed to do download a bert model
		# mock load_from_checkpoint which is supposed to do download a bert model
		with patch('comet.download_model'  ) as mock_download_model:
				_a					:				Any				=							None
				with patch('comet.load_from_checkpoint'  ) as mock_load_from_checkpoint:
						_a					:				Optional[Any]				=							load_from_checkpoint
						yield



def 					__lowerCamelCase					(				)      ->				Tuple:
		_a					:				Dict				=							load_metric(os.path.join('metrics'						,      'seqeval'  )  )
		_a					:				Optional[int]				=							'ERROR'
		_a					:				Optional[Any]				=							f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
		with pytest.raises(lowerCAmelCase_						,      match=re.escape(lowerCAmelCase_  )  ):
				metric.compute(predictions=[]						,      references=[]						,      scheme=lowerCAmelCase_  )




