

import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	Optional[int]														=					None
    __lowerCamelCase						:	List[str]														=					BloomTokenizerFast
    __lowerCamelCase						:	str														=					BloomTokenizerFast
    __lowerCamelCase						:	List[Any]														=					True
    __lowerCamelCase						:	Optional[Any]														=					False
    __lowerCamelCase						:	int														=					"""tokenizer_file"""
    __lowerCamelCase						:	Tuple														=					{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}





    def 				snake_case_    (				self):
         super().setUp()
         lowercase__   :       Tuple				=	BloomTokenizerFast.from_pretrained('bigscience/tokenizer')
         tokenizer.save_pretrained(self.tmpdirname)





    def 				snake_case_    (				self						,       **a):
         kwargs.update(self.special_tokens_map)
         return BloomTokenizerFast.from_pretrained(self.tmpdirname						,       **a)





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	self.get_rust_tokenizer()

         lowercase__   :       Any				=	['The quick brown fox</s>', 'jumps over the lazy dog</s>']
         lowercase__   :       Optional[int]				=	[[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

         lowercase__   :       Tuple				=	tokenizer.batch_encode_plus(a)['input_ids']
         self.assertListEqual(a						,       a)

         lowercase__   :       List[str]				=	tokenizer.batch_decode(a)
         self.assertListEqual(a						,       a)





    def 				snake_case_    (				self						,       a=6):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                   lowercase__   :       List[Any]				=	self.rust_tokenizer_class.from_pretrained(a						,       **a)
                   # tokenizer_r.pad_token = None # Hotfixing padding = None
                   # Simple input
                   lowercase__   :       Optional[Any]				=	'This is a simple input'
                   lowercase__   :       Union[str, Any]				=	['This is a simple input 1', 'This is a simple input 2']
                   lowercase__   :       Dict				=	('This is a simple input', 'This is a pair')
                   lowercase__   :       Dict				=	[
                       ('This is a simple input 1', 'This is a simple input 2'),
                       ('This is a simple pair 1', 'This is a simple pair 2'),
                   ]

                   # Simple input tests
                   try:
                        tokenizer_r.encode(a						,       max_length=a)
                        tokenizer_r.encode_plus(a						,       max_length=a)

                        tokenizer_r.batch_encode_plus(a						,       max_length=a)
                        tokenizer_r.encode(a						,       max_length=a)
                        tokenizer_r.batch_encode_plus(a						,       max_length=a)
                   except ValueError:
                        self.fail('Bloom Tokenizer should be able to deal with padding')

                   lowercase__   :       Optional[int]				=	None  # Hotfixing padding = None
                   self.assertRaises(a						,       tokenizer_r.encode						,       a						,       max_length=a						,       padding='max_length')

                   # Simple input
                   self.assertRaises(a						,       tokenizer_r.encode_plus						,       a						,       max_length=a						,       padding='max_length')

                   # Simple input
                   self.assertRaises(
                       a						,       tokenizer_r.batch_encode_plus						,       a						,       max_length=a						,       padding='max_length'						,       )

                   # Pair input
                   self.assertRaises(a						,       tokenizer_r.encode						,       a						,       max_length=a						,       padding='max_length')

                   # Pair input
                   self.assertRaises(a						,       tokenizer_r.encode_plus						,       a						,       max_length=a						,       padding='max_length')

                   # Pair input
                   self.assertRaises(
                       a						,       tokenizer_r.batch_encode_plus						,       a						,       max_length=a						,       padding='max_length'						,       )





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	self.get_rust_tokenizer()
         lowercase__   :       Optional[int]				=	load_dataset('xnli'						,       'all_languages'						,       split='test'						,       streaming=a)

         lowercase__   :       Any				=	next(iter(a))['premise']  # pick up one data
         lowercase__   :       Dict				=	list(sample_data.values())

         lowercase__   :       Tuple				=	list(map(tokenizer.encode						,       a))
         lowercase__   :       Any				=	[tokenizer.decode(a						,       clean_up_tokenization_spaces=a) for x in output_tokens]
         self.assertListEqual(a						,       a)





    def 				snake_case_    (				self):
         # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
         # any sequence length constraints. This test of the parent class will fail since it relies on the
         # maximum sequence length of the positoonal embeddings.
         self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)						,       1)
         self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])						,       1)




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

snake_case_        =			{
    '''junnyu/roformer_chinese_small''': 1_536,
    '''junnyu/roformer_chinese_base''': 1_536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


snake_case_        =			{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Union[str, Any]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	int														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	Any														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase						:	Tuple														=					RoFormerTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=True						,       a="[UNK]"						,       a="[SEP]"						,       a="[PAD]"						,       a="[CLS]"						,       a="[MASK]"						,       a=True						,       a=None						,       **a						,       ):
         super().__init__(
             a						,       tokenizer_file=a						,       do_lower_case=a						,       unk_token=a						,       sep_token=a						,       pad_token=a						,       cls_token=a						,       mask_token=a						,       tokenize_chinese_chars=a						,       strip_accents=a						,       **a						,       )

         lowercase__   :       Optional[Any]				=	json.loads(self.backend_tokenizer.normalizer.__getstate__())
         if (
             pre_tok_state.get('lowercase'						,       a) != do_lower_case
             or pre_tok_state.get('strip_accents'						,       a) != strip_accents
         ):
              lowercase__   :       Optional[int]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       str				=	do_lower_case
              lowercase__   :       Union[str, Any]				=	strip_accents
              lowercase__   :       int				=	pre_tok_class(**a)

         lowercase__   :       Optional[int]				=	do_lower_case





    def __getstate__(				self):
         lowercase__   :       str				=	self.__dict__.copy()
         lowercase__   :       Any				=	BertPreTokenizer()
         return state





    def __setstate__(				self						,       a):
         lowercase__   :       Union[str, Any]				=	d
         lowercase__   :       int				=	self.__dict__['_tokenizer'].get_vocab()
         lowercase__   :       List[str]				=	PreTokenizer.custom(JiebaPreTokenizer(a))





    def 				snake_case_    (				self						,       a						,       a=None):
         lowercase__   :       Union[str, Any]				=	[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						,       a						,       a = None):
         lowercase__   :       Tuple				=	[self.sep_token_id]
         lowercase__   :       Optional[int]				=	[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						,       a						,       a = None):
         lowercase__   :       Optional[Any]				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a						,       a=None						,       a=None						,       a=False						,       **a						,       ):
         lowercase__   :       List[str]				=	BertPreTokenizer()
         return super().save_pretrained(a						,       a						,       a						,       a						,       **a)

