



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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_big_bird import BigBirdTokenizer
else:
							__snake_case       :	Any            =None

__snake_case       :	int            =logging.get_logger(__name__)
__snake_case       :	Dict            ={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__snake_case       :	Union[str, Any]            ={
    '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'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

__snake_case       :	Any            ={
    'google/bigbird-roberta-base': 4_0_9_6,
    'google/bigbird-roberta-large': 4_0_9_6,
    'google/bigbird-base-trivia-itc': 4_0_9_6,
}


__snake_case       :	Union[str, Any]            ='▁'



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=VOCAB_FILES_NAMES
		snake_case_			=PRETRAINED_VOCAB_FILES_MAP
		snake_case_			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case_			=BigBirdTokenizer
		snake_case_			=["""input_ids""", """attention_mask"""]
		snake_case_			=[]



		def __init__(self	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase="<unk>"	,__lowerCamelCase="<s>"	,__lowerCamelCase="</s>"	,__lowerCamelCase="<pad>"	,__lowerCamelCase="[SEP]"	,__lowerCamelCase="[MASK]"	,__lowerCamelCase="[CLS]"	,**__lowerCamelCase	,)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  AddedToken(__lowerCamelCase	,lstrip=__lowerCamelCase	,rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase	,__lowerCamelCase				) else bos_token
						lowerCAmelCase__  :       Any				=  AddedToken(__lowerCamelCase	,lstrip=__lowerCamelCase	,rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase	,__lowerCamelCase				) else eos_token
						lowerCAmelCase__  :       Optional[int]				=  AddedToken(__lowerCamelCase	,lstrip=__lowerCamelCase	,rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase	,__lowerCamelCase				) else unk_token
						lowerCAmelCase__  :       Union[str, Any]				=  AddedToken(__lowerCamelCase	,lstrip=__lowerCamelCase	,rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase	,__lowerCamelCase				) else pad_token
						lowerCAmelCase__  :       Optional[int]				=  AddedToken(__lowerCamelCase	,lstrip=__lowerCamelCase	,rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase	,__lowerCamelCase				) else cls_token
						lowerCAmelCase__  :       str				=  AddedToken(__lowerCamelCase	,lstrip=__lowerCamelCase	,rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase	,__lowerCamelCase				) else sep_token

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

						super().__init__(
						    __lowerCamelCase	,tokenizer_file=__lowerCamelCase	,bos_token=__lowerCamelCase	,eos_token=__lowerCamelCase	,unk_token=__lowerCamelCase	,sep_token=__lowerCamelCase	,pad_token=__lowerCamelCase	,cls_token=__lowerCamelCase	,mask_token=__lowerCamelCase	,**__lowerCamelCase	,)

						lowerCAmelCase__  :       List[Any]				=  vocab_file
						lowerCAmelCase__  :       List[Any]				=  False if not self.vocab_file else True



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    List[int]:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = False				)  ->    List[int]:

						"""simple docstring"""






						if already_has_special_tokens:
										if token_ids_a is not None:
														raise ValueError(
														    '''You should not supply a second sequence if the provided sequence of '''
														    '''ids is already formatted with special tokens for the model.'''				)
										return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    List[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  [self.sep_token_id]
						lowerCAmelCase__  :       Optional[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				) * [0] + len(token_ids_a + sep				) * [1]



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    Tuple[str]:

						"""simple docstring"""






						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
										    '''tokenizer.'''				)

						if not os.path.isdir(__lowerCamelCase				):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
										return
						lowerCAmelCase__  :       Dict				=  os.path.join(
						    __lowerCamelCase	,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

						if os.path.abspath(self.vocab_file				) != os.path.abspath(__lowerCamelCase				):
										copyfile(self.vocab_file	,__lowerCamelCase				)

						return (out_vocab_file,)






import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__snake_case       :	Optional[int]            =get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
							__snake_case       :	Optional[Any]            =json.load(f)



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						return FSMTTokenizer.from_pretrained(__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  FSMTForConditionalGeneration.from_pretrained(__lowerCamelCase				).to(__lowerCamelCase				)
						if torch_device == "cuda":
										model.half()
						return model



		@parameterized.expand(
		    [
		        ['''en-ru''', 26.0],
		        ['''ru-en''', 22.0],
		        ['''en-de''', 22.0],
		        ['''de-en''', 29.0],
		    ]				)
		@slow
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  f"""facebook/wmt19-{pair}"""
						lowerCAmelCase__  :       int				=  self.get_tokenizer(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  self.get_model(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''src''']
						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''tgt''']

						lowerCAmelCase__  :       List[str]				=  tokenizer(__lowerCamelCase	,return_tensors='''pt'''	,truncation=__lowerCamelCase	,padding='''longest'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  model.generate(
						    input_ids=batch.input_ids	,num_beams=8	,)
						lowerCAmelCase__  :       Tuple				=  tokenizer.batch_decode(
						    __lowerCamelCase	,skip_special_tokens=__lowerCamelCase	,clean_up_tokenization_spaces=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  calculate_bleu(__lowerCamelCase	,__lowerCamelCase				)
						print(__lowerCamelCase				)
						self.assertGreaterEqual(scores['''bleu''']	,__lowerCamelCase				)

