

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

import sentencepiece as spm

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


lowerCamelCase           =					logging.get_logger(__name__)

lowerCamelCase           =					'▁'

lowerCamelCase           =					{'vocab_file': 'sentencepiece.bpe.model'}

lowerCamelCase           =					{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

lowerCamelCase           =					{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
lowerCamelCase           =					['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']







class 						__magic_name__			(							lowerCAmelCase_							):

   '''simple docstring'''




   lowerCamelCase__ :	Optional[Any]    =   VOCAB_FILES_NAMES
   lowerCamelCase__ :	Optional[int]    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__ :	Optional[int]    =   PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__ :	Optional[int]    =   ['input_ids', 'attention_mask']

   lowerCamelCase__ :	List[Any]    =   []
   lowerCamelCase__ :	Optional[int]    =   []



   def __init__(				self,    lowercase_,    lowercase_="<s>",    lowercase_="</s>",    lowercase_="</s>",    lowercase_="<s>",    lowercase_="<unk>",    lowercase_="<pad>",    lowercase_="<mask>",    lowercase_=None,    lowercase_=None,    lowercase_=None,    lowercase_ = None,    lowercase_=None,    **lowercase_,    )							->    List[str]:






     """simple docstring"""






     a__  =AddedToken(__a,    lstrip=__a,    rstrip=__a       ) if isinstance(__a,    __a       ) else mask_token

     a__  ={} 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,    tokenizer_file=__a,    src_lang=__a,    tgt_lang=__a,    additional_special_tokens=__a,    sp_model_kwargs=self.sp_model_kwargs,    **__a,    )

     a__  =spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(str(__a       )       )
     a__  =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
     a__  ={'<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
     a__  =1

     a__  =len(self.sp_model       )
     a__  ={
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__a       )
     }
     a__  ={v: k for k, v in self.lang_code_to_id.items()}
     a__  =len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset

     self.fairseq_tokens_to_ids.update(self.lang_code_to_id       )
     a__  ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
     a__  =list(self.lang_code_to_id.keys()       )

     if additional_special_tokens is not None:
       # Only add those special tokens if they are not already there.
       self._additional_special_tokens.extend(
           [t for t in additional_special_tokens if t not in self._additional_special_tokens]       )

     a__  =src_lang if src_lang is not None else 'en_XX'
     a__  =self.lang_code_to_id[self._src_lang]
     a__  =tgt_lang
     self.set_src_lang_special_tokens(self._src_lang       )



   def __getstate__(				self       )							->    List[str]:






     """simple docstring"""






     a__  =self.__dict__.copy()
     a__  =None
     a__  =self.sp_model.serialized_model_proto()
     return state



   def __setstate__(				self,    lowercase_       )							->    Any:






     """simple docstring"""






     a__  =d

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

     a__  =spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )



   @property
   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token



   @property
   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     return self._src_lang



   @src_lang.setter
   def  _UpperCAmelCase (				self,    lowercase_       )							->    Dict:






     """simple docstring"""






     a__  =new_src_lang
     self.set_src_lang_special_tokens(self._src_lang       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = None,    lowercase_ = False       )							->    Any:






     """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       )

     a__  =[1] * len(self.prefix_tokens       )
     a__  =[1] * len(self.suffix_tokens       )
     if token_ids_a is None:
       return prefix_ones + ([0] * len(__a       )) + suffix_ones
     return prefix_ones + ([0] * len(__a       )) + ([0] * len(__a       )) + suffix_ones



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = None       )							->    Optional[Any]:






     """simple docstring"""






     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + self.suffix_tokens
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = None       )							->    List[Any]:






     """simple docstring"""






     a__  =[self.sep_token_id]
     a__  =[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  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    **lowercase_       )							->    Optional[int]:






     """simple docstring"""






     if src_lang is None or tgt_lang is None:
       raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''       )
     a__  =src_lang
     a__  =self(__a,    add_special_tokens=__a,    return_tensors=__a,    **__a       )
     a__  =self.convert_tokens_to_ids(__a       )
     a__  =tgt_lang_id
     return inputs



   def  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






     a__  ={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,    lowercase_       )							->    List[str]:






     """simple docstring"""






     return self.sp_model.encode(__a,    out_type=__a       )



   def  _UpperCAmelCase (				self,    lowercase_       )							->    Optional[int]:






     """simple docstring"""






     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     a__  =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,    lowercase_       )							->    Union[str, Any]:






     """simple docstring"""






     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,    lowercase_       )							->    Tuple:






     """simple docstring"""






     a__  =''.join(__a       ).replace(__a,    ''' '''       ).strip()
     return out_string



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = None       )							->    Optional[int]:






     """simple docstring"""






     if not os.path.isdir(__a       ):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
       return
     a__  =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:
         a__  =self.sp_model.serialized_model_proto()
         fi.write(__a       )

     return (out_vocab_file,)



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = "en_XX",    lowercase_ = None,    lowercase_ = "ro_RO",    **lowercase_,    )							->    List[str]:






     """simple docstring"""






     a__  =src_lang
     a__  =tgt_lang
     return super().prepare_seqaseq_batch(__a,    __a,    **__a       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     return self.set_src_lang_special_tokens(self.src_lang       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     return self.set_tgt_lang_special_tokens(self.tgt_lang       )



   def  _UpperCAmelCase (				self,    lowercase_       )							->    List[str]:






     """simple docstring"""






     a__  =self.lang_code_to_id[src_lang]
     a__  =[]
     a__  =[self.eos_token_id, self.cur_lang_code]



   def  _UpperCAmelCase (				self,    lowercase_       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =self.lang_code_to_id[lang]
     a__  =[]
     a__  =[self.eos_token_id, self.cur_lang_code]




'''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	:						Tuple       =	pytest.mark.integration


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

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

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					@wraps(_SCREAMING_SNAKE_CASE   )
					def wrapper(self      :   int						,      _SCREAMING_SNAKE_CASE      :   List[Any]   ):
										if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
															self.skipTest('"test requires Fairseq"'   )
										else:
															test_case(self						,      _SCREAMING_SNAKE_CASE   )

					return wrapper






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

					return wrapper






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					@wraps(_SCREAMING_SNAKE_CASE   )
					def wrapper(self      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
										if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
															self.skipTest('"test not supported on Windows"'   )
										else:
															test_case(self						,      _SCREAMING_SNAKE_CASE   )

					return wrapper






def   lowerCamelCase							():
					__a			:       List[Any]					    =  [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(
    lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_					)
@local
class 							__UpperCamelCase				(		parameterized.TestCase					):
	A_				=    {}
	A_				=    None




	@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'							)
	@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'							)
	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       int					    =  '[...]'
						__a			:       Tuple					    =  importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join('metrics'						,				__a							)							).module_path							)
						__a			:       Optional[Any]					    =  datasets.load.import_main_class(metric_module.__name__						,				dataset=__a							)
						# check parameters
						__a			:       Dict					    =  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:
																					__a			:       str					    =  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							):

						'''simple docstring'''





						__a			:       Tuple					    =  '[...]'
						__a			:       Optional[Any]					    =  importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join('metrics'						,				__a							)							).module_path							)
						# run doctest
						with self.use_local_metrics():
											__a			:       List[Any]					    =  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							):

						'''simple docstring'''





						if metric_name in self.INTENSIVE_CALLS_PATCHER:
											with self.INTENSIVE_CALLS_PATCHER[metric_name](__a							):
																yield
						else:
											yield




	@contextmanager
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						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:
											__a			:       Dict					    =  load_local_metric
											yield





	@classmethod
	def       __UpperCAmelCase							(   cls						,				__a							):

						'''simple docstring'''





						def wrapper(__a							):
											__a			:       Optional[Any]					    =  contextmanager(__a							)
											__a			:       str					    =  patcher
											return patcher

						return wrapper






@LocalMetricTest.register_intensive_calls_patcher('bleurt'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					import tensorflow.compat.va as tf
					from bleurt.score import Predictor

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





					class 							__UpperCamelCase				(		lowerCAmelCase_					):




						def       __UpperCAmelCase							(   self						,				__a							):

											'''simple docstring'''





											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			:       Dict					    =  MockedPredictor()
										yield






@LocalMetricTest.register_intensive_calls_patcher('bertscore'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					import torch

					def bert_cos_score_idf(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   str						,      *_SCREAMING_SNAKE_CASE      :   int						,      **_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
										return torch.tensor([[1.0, 1.0, 1.0]] * len(_SCREAMING_SNAKE_CASE   )   )

					# 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			:       str					    =  bert_cos_score_idf
										yield






@LocalMetricTest.register_intensive_calls_patcher('comet'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					def load_from_checkpoint(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):





										class 							__UpperCamelCase				:




											def       __UpperCAmelCase							(   self						,				__a						,				*__a						,				**__a							):

																'''simple docstring'''





																assert len(__a							) == 2
																__a			:       Dict					    =  [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:
										__a			:       str					    =  None
										with patch('comet.load_from_checkpoint'   ) as mock_load_from_checkpoint:
															__a			:       int					    =  load_from_checkpoint
															yield






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



