



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


A__	      =       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

A__	      =       {
    """vocab_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""",
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-generator""": (
            """https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"""
        ),
    },
}

A__	      =       {
    """google/electra-small-generator""": 5_12,
    """google/electra-base-generator""": 5_12,
    """google/electra-large-generator""": 5_12,
    """google/electra-small-discriminator""": 5_12,
    """google/electra-base-discriminator""": 5_12,
    """google/electra-large-discriminator""": 5_12,
}

A__	      =       {
    """google/electra-small-generator""": {"""do_lower_case""": True},
    """google/electra-base-generator""": {"""do_lower_case""": True},
    """google/electra-large-generator""": {"""do_lower_case""": True},
    """google/electra-small-discriminator""": {"""do_lower_case""": True},
    """google/electra-base-discriminator""": {"""do_lower_case""": True},
    """google/electra-large-discriminator""": {"""do_lower_case""": True},
}





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				VOCAB_FILES_NAMES
				__lowerCamelCase =				PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase =				PRETRAINED_INIT_CONFIGURATION
				__lowerCamelCase =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase =				ElectraTokenizer




				def __init__(							self						,				_snake_case=None						,				_snake_case=None						,				_snake_case=True						,				_snake_case="[UNK]"						,				_snake_case="[SEP]"						,				_snake_case="[PAD]"						,				_snake_case="[CLS]"						,				_snake_case="[MASK]"						,				_snake_case=True						,				_snake_case=None						,				**_snake_case						,				):



									"""simple docstring"""





									super().__init__(
									    _snake_case						,				tokenizer_file=_snake_case						,				do_lower_case=_snake_case						,				unk_token=_snake_case						,				sep_token=_snake_case						,				pad_token=_snake_case						,				cls_token=_snake_case						,				mask_token=_snake_case						,				tokenize_chinese_chars=_snake_case						,				strip_accents=_snake_case						,				**_snake_case						,				)

									_lowerCAmelCase						=    json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
									if (
									    normalizer_state.get("""lowercase"""						,				_snake_case		) != do_lower_case
									    or normalizer_state.get("""strip_accents"""						,				_snake_case		) != strip_accents
									    or normalizer_state.get("""handle_chinese_chars"""						,				_snake_case		) != tokenize_chinese_chars
									):
														_lowerCAmelCase						=    getattr(_snake_case						,				normalizer_state.pop("""type"""		)		)
														_lowerCAmelCase						=    do_lower_case
														_lowerCAmelCase						=    strip_accents
														_lowerCAmelCase						=    tokenize_chinese_chars
														_lowerCAmelCase						=    normalizer_class(**_snake_case		)

									_lowerCAmelCase						=    do_lower_case




				def 							snake_case   (							self						,				_snake_case						,				_snake_case=None		):



									"""simple docstring"""





									_lowerCAmelCase						=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
														output += token_ids_a + [self.sep_token_id]

									return output




				def 							snake_case   (							self						,				_snake_case						,				_snake_case = None		):



									"""simple docstring"""





									_lowerCAmelCase						=    [self.sep_token_id]
									_lowerCAmelCase						=    [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 							snake_case   (							self						,				_snake_case						,				_snake_case = None		):



									"""simple docstring"""





									_lowerCAmelCase						=    self._tokenizer.model.save(_snake_case						,				name=_snake_case		)
									return tuple(_snake_case		)







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 )



