

import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class 			lowerCamelCase_    (  lowercase	,     unittest.TestCase		):
							__lowercase				:	int        =				CpmAntTokenizer
							__lowercase				:	Dict        =				False


							def  lowercase   ( self						)							->   Optional[int]:



								"""simple docstring"""

								super().setUp()

								_UpperCamelCase			=   [
								    """<d>""",
								    """</d>""",
								    """<s>""",
								    """</s>""",
								    """</_>""",
								    """<unk>""",
								    """<pad>""",
								    """</n>""",
								    """我""",
								    """是""",
								    """C""",
								    """P""",
								    """M""",
								    """A""",
								    """n""",
								    """t""",
								]
								_UpperCamelCase			=   os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES["vocab_file"]						)
								with open(self.vocab_file      ,				"w"      ,				encoding="utf-8"						) as vocab_writer:
									vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)







							@tooslow
							def  lowercase   ( self						)							->   List[str]:



								"""simple docstring"""

								_UpperCamelCase			=   CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"						)
								_UpperCamelCase			=   """今天天气真好！"""
								_UpperCamelCase			=   ["""今天""", """天气""", """真""", """好""", """！"""]
								_UpperCamelCase			=   tokenizer.tokenize(A_						)
								self.assertListEqual(A_      ,				A_						)
								_UpperCamelCase			=   """今天天气真好！"""
								_UpperCamelCase			=   [tokenizer.bos_token] + tokens

								_UpperCamelCase			=   [6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(A_						)      ,				A_						)

								_UpperCamelCase			=   tokenizer.decode(A_						)
								self.assertEqual(A_      ,				A_						)








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     List[str]       =     {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''camembert'''


     def __init__(  self							,					A_=3_05_22							,					A_=7_68							,					A_=12							,					A_=12							,					A_=30_72							,					A_="gelu"							,					A_=0.1							,					A_=0.1							,					A_=5_12							,					A_=2							,					A_=0.0_2							,					A_=1e-12							,					A_=1							,					A_=0							,					A_=2							,					A_="absolute"							,					A_=True							,					A_=None							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					bos_token_id=A_							,					eos_token_id=A_							,					**A_    )

       _UpperCAmelCase		:							Optional[Any]							=     vocab_size
       _UpperCAmelCase		:							int							=     hidden_size
       _UpperCAmelCase		:							Any							=     num_hidden_layers
       _UpperCAmelCase		:							Optional[Any]							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     hidden_act
       _UpperCAmelCase		:							str							=     intermediate_size
       _UpperCAmelCase		:							str							=     hidden_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     max_position_embeddings
       _UpperCAmelCase		:							Union[str, Any]							=     type_vocab_size
       _UpperCAmelCase		:							List[Any]							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							int							=     position_embedding_type
       _UpperCAmelCase		:							Tuple							=     use_cache
       _UpperCAmelCase		:							Any							=     classifier_dropout


class 		_SCREAMING_SNAKE_CASE     (						A     ):


     @property
     def        __snake_case(  self    ):
       if self.task == "multiple-choice":
         _UpperCAmelCase		:							Any							=     {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         _UpperCAmelCase		:							List[Any]							=     {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]    )





