
'''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
__lowercase							:   Optional[int]					=     pytest.mark.integration


__lowercase							:   Optional[Any]					=     {'''comet'''}
__lowercase							:   Any					=     importlib.util.find_spec('''fairseq''') is not None

__lowercase							:   List[str]					=     {'''code_eval'''}
__lowercase							:   Optional[Any]					=     os.name == '''nt'''

__lowercase							:   List[str]					=     {'''bertscore''', '''frugalscore''', '''perplexity'''}
__lowercase							:   Optional[Any]					=     importlib.util.find_spec('''transformers''') is not None




def lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

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

					return wrapper




def lowercase_	(   _lowercase				)     ->							List[Any]:
					'''simple docstring'''

					@wraps(_lowercase				)
					def wrapper(self			,       _lowercase				):
										if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
															self.skipTest('''"test requires transformers"'''				)
										else:
															test_case(self			,       _lowercase				)

					return wrapper




def lowercase_	(   _lowercase				)     ->							List[str]:
					'''simple docstring'''

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

					return wrapper




def lowercase_	(   )     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		[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(
    _lowercase      ,     _lowercase      ,     _lowercase    )
@local
class 	__lowercase				(  parameterized.TestCase    ):
			lowerCamelCase			:		List[Any]	      =			{}
			lowerCamelCase			:		int	      =			None

			@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''      )
			@pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning'''      )
			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Any     =		'''[...]'''
								lowerCamelCase_					:      int     =		importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join('''metrics'''			,   A      )      ).module_path      )
								lowerCamelCase_					:      Any     =		datasets.load.import_main_class(metric_module.__name__			,   dataset=A      )
								# check parameters
								lowerCamelCase_					:      List[str]     =		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(A			,   metric_module.__name__      ):
													with self.use_local_metrics():
																		try:
																							lowerCamelCase_					:      Tuple     =		doctest.testmod(A			,   verbose=A			,   raise_on_error=A      )
																		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        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Union[str, Any]     =		'''[...]'''
								lowerCamelCase_					:      Union[str, Any]     =		importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join('''metrics'''			,   A      )      ).module_path      )
								# run doctest
								with self.use_local_metrics():
													lowerCamelCase_					:      Dict     =		doctest.testmod(A			,   verbose=A			,   raise_on_error=A      )
								self.assertEqual(results.failed			,   0      )
								self.assertGreater(results.attempted			,   1      )

			@contextmanager
			def        UpperCAmelCase__ (self			,   A			,   A      ):
								if metric_name in self.INTENSIVE_CALLS_PATCHER:
													with self.INTENSIVE_CALLS_PATCHER[metric_name](A      ):
																		yield
								else:
													yield

			@contextmanager
			def        UpperCAmelCase__ (self      ):
								def load_local_metric(A			,   *A			,   **A      ):
													return load_metric(os.path.join('''metrics'''			,   A      )			,   *A			,   **A      )

								with patch('''datasets.load_metric'''      ) as mock_load_metric:
													lowerCamelCase_					:      Optional[Any]     =		load_local_metric
													yield






			@classmethod
			def        UpperCAmelCase__ (cls			,   A      ):
								def wrapper(A      ):
													lowerCamelCase_					:      Tuple     =		contextmanager(A      )
													lowerCamelCase_					:      Tuple     =		patcher
													return patcher

								return wrapper




@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''				)
def lowercase_	(   _lowercase				)     ->							Any:
					'''simple docstring'''

					import tensorflow.compat.va as tf
					from bleurt.score import Predictor

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

					class 	__lowercase				(  _lowercase    ):

								def        UpperCAmelCase__ (self			,   A      ):
													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:
										lowerCamelCase_					:      List[str]     =		MockedPredictor()
										yield




@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''				)
def lowercase_	(   _lowercase				)     ->							List[Any]:
					'''simple docstring'''

					import torch

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

					# 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:
										lowerCamelCase_					:      int     =		bert_cos_score_idf
										yield




@LocalMetricTest.register_intensive_calls_patcher('''comet'''				)
def lowercase_	(   _lowercase				)     ->							List[Any]:
					'''simple docstring'''

					def load_from_checkpoint(_lowercase				):

										class 	__lowercase				:

													def        UpperCAmelCase__ (self			,   A			,   *A			,   **A      ):
																		assert len(A      ) == 2
																		lowerCamelCase_					:      Optional[int]     =		[0.19, 0.92]
																		return scores, sum(A      ) / len(A      )
										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:
										lowerCamelCase_					:      Any     =		None
										with patch('''comet.load_from_checkpoint'''				) as mock_load_from_checkpoint:
															lowerCamelCase_					:      Tuple     =		load_from_checkpoint
															yield






def lowercase_	(   )     ->							Dict:
					'''simple docstring'''

					lowerCamelCase_					:      int     =		load_metric(os.path.join('''metrics'''			,       '''seqeval'''				)				)
					lowerCamelCase_					:      Optional[Any]     =		'''ERROR'''
					lowerCamelCase_					:      Optional[Any]     =		F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
					with pytest.raises(_lowercase			,       match=re.escape(_lowercase				)				):
										metric.compute(predictions=[]			,       references=[]			,       scheme=_lowercase				)




'''simple docstring'''


import os
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


__lowercase							:   Any					=     logging.get_logger(__name__)

__lowercase							:   List[str]					=     '''▁'''

__lowercase							:   Optional[Any]					=     {'''vocab_file''': '''sentencepiece.bpe.model'''}

__lowercase							:   List[str]					=     {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

__lowercase							:   Union[str, Any]					=     {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}

class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		Any	      =			VOCAB_FILES_NAMES
			lowerCamelCase			:		Optional[int]	      =			PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase			:		str	      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase			:		Union[str, Any]	      =			["input_ids", "attention_mask"]

			def __init__(self			,   A			,   A="<s>"			,   A="</s>"			,   A="</s>"			,   A="<s>"			,   A="<unk>"			,   A="<pad>"			,   A="<mask>"			,   A = None			,   **A			,   ):
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase_					:      Tuple     =		AddedToken(A			,   lstrip=A			,   rstrip=A      ) if isinstance(A			,   A      ) else mask_token

								lowerCamelCase_					:      Dict     =		{} if sp_model_kwargs is None else sp_model_kwargs

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

								lowerCamelCase_					:      Any     =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(str(A      )      )
								lowerCamelCase_					:      Tuple     =		vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# Mimic fairseq token-to-id alignment for the first 4 token
								lowerCamelCase_					:      str     =		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								lowerCamelCase_					:      int     =		1

								lowerCamelCase_					:      Union[str, Any]     =		len(self.sp_model      ) + self.fairseq_offset
								lowerCamelCase_					:      Any     =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}

			def __getstate__(self      ):
								lowerCamelCase_					:      Any     =		self.__dict__.copy()
								lowerCamelCase_					:      Optional[Any]     =		None
								lowerCamelCase_					:      int     =		self.sp_model.serialized_model_proto()
								return state

			def __setstate__(self			,   A      ):
								lowerCamelCase_					:      Any     =		d

								# for backward compatibility
								if not hasattr(self			,   '''sp_model_kwargs'''      ):
													lowerCamelCase_					:      str     =		{}

								lowerCamelCase_					:      List[Any]     =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )

			def        UpperCAmelCase__ (self			,   A			,   A = None      ):

								if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase_					:      Optional[int]     =		[self.cls_token_id]
								lowerCamelCase_					:      Optional[int]     =		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep

			def        UpperCAmelCase__ (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        UpperCAmelCase__ (self			,   A			,   A = None      ):
								lowerCamelCase_					:      List[str]     =		[self.sep_token_id]
								lowerCamelCase_					:      List[Any]     =		[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]

			@property
			def        UpperCAmelCase__ (self      ):
								return len(self.sp_model      ) + self.fairseq_offset + 1  # Add the <mask> token

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      str     =		{self.convert_ids_to_tokens(A      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab

			def        UpperCAmelCase__ (self			,   A      ):
								return self.sp_model.encode(A			,   out_type=A      )

			def        UpperCAmelCase__ (self			,   A      ):
								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								lowerCamelCase_					:      int     =		self.sp_model.PieceToId(A      )

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

			def        UpperCAmelCase__ (self			,   A      ):
								if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset      )

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Optional[int]     =		''''''.join(A      ).replace(A			,   ''' '''      ).strip()
								return out_string






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

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

								return (out_vocab_file,)


