
'''simple docstring'''



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_fnet import FNetTokenizer
else:
				__A								=			None

__A								=			logging.get_logger(__name__)
__A								=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A								=			{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

__A								=			{
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

__A								=			"▁"





class 	A							(				__UpperCAmelCase ):
			lowerCamelCase							:							List[str]										=     VOCAB_FILES_NAMES
			lowerCamelCase							:							Any										=     PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase							:							Union[str, Any]										=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase							:							Optional[int]										=     ["input_ids", "token_type_ids"]
			lowerCamelCase							:							Tuple										=     FNetTokenizer



			def __init__(    self				,   lowerCamelCase__=None				,   lowerCamelCase__=None				,   lowerCamelCase__=False				,   lowerCamelCase__=True				,   lowerCamelCase__=True				,   lowerCamelCase__="<unk>"				,   lowerCamelCase__="[SEP]"				,   lowerCamelCase__="<pad>"				,   lowerCamelCase__="[CLS]"				,   lowerCamelCase__="[MASK]"				,   **lowerCamelCase__				,   )	->						Any:




									'''simple docstring'''



									lowercase__							       =				(
									    AddedToken(__A				,   lstrip=__A				,   rstrip=__A				,   normalized=__A				)
									    if isinstance(__A				,   __A				)
									    else mask_token
									)

									super().__init__(
									    __A				,   tokenizer_file=__A				,   do_lower_case=__A				,   remove_space=__A				,   keep_accents=__A				,   unk_token=__A				,   sep_token=__A				,   pad_token=__A				,   cls_token=__A				,   mask_token=__A				,   **__A				,   )

									lowercase__							       =				do_lower_case
									lowercase__							       =				remove_space
									lowercase__							       =				keep_accents
									lowercase__							       =				vocab_file
									lowercase__							       =				False if not self.vocab_file else True



			def A__       (    self				,   lowerCamelCase__				,   lowerCamelCase__ = None				)	->						Any:




									'''simple docstring'''



									lowercase__							       =				[self.sep_token_id]
									lowercase__							       =				[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 A__       (    self				,   lowerCamelCase__				,   lowerCamelCase__ = None				)	->						List[Any]:




									'''simple docstring'''



									lowercase__							       =				[self.sep_token_id]
									lowercase__							       =				[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 A__       (    self				,   lowerCamelCase__				,   lowerCamelCase__ = None				)	->						Any:




									'''simple docstring'''



									if not os.path.isdir(__A				):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
															return
									lowercase__							       =				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				):
															copyfile(self.vocab_file				,   __A				)

									return (out_vocab_file,)










import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      List[Any]     		=		BioGptTokenizer
   __A :      Optional[int]     		=		False






   def _snake_case    (		self ):






     """simple docstring"""





     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     lowerCamelCase :					Union[str, Any]    =				[
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "w</w>",
         "r</w>",
         "t</w>",
         "lo",
         "low",
         "er</w>",
         "low</w>",
         "lowest</w>",
         "newer</w>",
         "wider</w>",
         "<unk>",
     ]
     lowerCamelCase :					str    =				dict(zip(__A						,				range(len(__A ) ) ) )
     lowerCamelCase :					Dict    =				["l o 123", "lo w 1456", "e r</w> 1789", ""]

     lowerCamelCase :					Dict    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["vocab_file"] )
     lowerCamelCase :					int    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["merges_file"] )
     with open(self.vocab_file						,				"w" ) as fp:
       fp.write(json.dumps(__A ) )
     with open(self.merges_file						,				"w" ) as fp:
       fp.write("\n".join(__A ) )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				"lower newer"
     lowerCamelCase :					Union[str, Any]    =				"lower newer"
     return input_text, output_text






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer(self.vocab_file						,				self.merges_file )

     lowerCamelCase :					Optional[int]    =				"lower"
     lowerCamelCase :					Any    =				["low", "er</w>"]
     lowerCamelCase :					List[str]    =				tokenizer.tokenize(__A )
     self.assertListEqual(__A						,				__A )

     lowerCamelCase :					Union[str, Any]    =				tokens + ["<unk>"]
     lowerCamelCase :					List[str]    =				[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A )						,				__A )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer.from_pretrained("microsoft/biogpt" )

     lowerCamelCase :					Optional[int]    =				tokenizer.encode("sequence builders"						,				add_special_tokens=__A )
     lowerCamelCase :					Tuple    =				tokenizer.encode("multi-sequence build"						,				add_special_tokens=__A )

     lowerCamelCase :					Tuple    =				tokenizer.build_inputs_with_special_tokens(__A )
     lowerCamelCase :					List[str]    =				tokenizer.build_inputs_with_special_tokens(__A						,				__A )

     self.assertTrue(encoded_sentence == [2] + text )
     self.assertTrue(encoded_pair == [2] + text + [2] + text_a )



