




import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__    :							List[str]       		=     get_tests_dir('fixtures/spiece.model')

@require_sentencepiece
@require_tokenizers
class   UpperCAmelCase    (      a_ ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				Optional[Any]      					=       ErnieMTokenizer
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       False
   __SCREAMING_SNAKE_CASE		:				Optional[Any]      					=       True
   __SCREAMING_SNAKE_CASE		:				Any      					=       False
   __SCREAMING_SNAKE_CASE		:				List[Any]      					=       False






   def 			_a    (						self :							int  )->      Optional[Any]:
     super().setUp()

     # We have a SentencePiece fixture for testing
     _lowercase:			Optional[int]      =     ErnieMTokenizer(UpperCAmelCase_    ,       unk_token='<unk>'    ,       pad_token='<pad>'  )
     tokenizer.save_pretrained(self.tmpdirname  )






   def 			_a    (						self :							List[str]    ,       UpperCamelCase_ :							Union[str, Any]  )->      Optional[Any]:
     _lowercase:			Optional[Any]      =     'this is a test'
     _lowercase:			int      =     'this is a test'
     return input_text, output_text






   def 			_a    (						self :							str  )->      str:
     _lowercase:			List[str]      =     '<pad>'
     _lowercase:			List[Any]      =     0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_  )    ,       UpperCAmelCase_  )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_  )    ,       UpperCAmelCase_  )






   def 			_a    (						self :							Any  )->      int:
     _lowercase:			Any      =     list(self.get_tokenizer().get_vocab().keys()  )

     self.assertEqual(vocab_keys[0]    ,       '<pad>'  )
     self.assertEqual(vocab_keys[1]    ,       '<unk>'  )
     self.assertEqual(vocab_keys[-1]    ,       '▁eloquent'  )
     self.assertEqual(len(UpperCAmelCase_  )    ,       3_00_00  )






   def 			_a    (						self :							Tuple  )->      Any:
     self.assertEqual(self.get_tokenizer().vocab_size    ,       3_00_00  )






   def 			_a    (						self :							int  )->      List[str]:







     if not self.test_rust_tokenizer:
       return

     _lowercase:			Union[str, Any]      =     self.get_tokenizer()
     _lowercase:			Any      =     self.get_rust_tokenizer()

     _lowercase:			Optional[Any]      =     'I was born in 92000, and this is falsé.'

     _lowercase:			Union[str, Any]      =     tokenizer.tokenize(UpperCAmelCase_  )
     _lowercase:			Union[str, Any]      =     rust_tokenizer.tokenize(UpperCAmelCase_  )
     self.assertListEqual(UpperCAmelCase_    ,       UpperCAmelCase_  )

     _lowercase:			Optional[int]      =     tokenizer.encode(UpperCAmelCase_    ,       add_special_tokens=UpperCAmelCase_  )
     _lowercase:			List[str]      =     rust_tokenizer.encode(UpperCAmelCase_    ,       add_special_tokens=UpperCAmelCase_  )
     self.assertListEqual(UpperCAmelCase_    ,       UpperCAmelCase_  )

     _lowercase:			str      =     self.get_rust_tokenizer()
     _lowercase:			str      =     tokenizer.encode(UpperCAmelCase_  )
     _lowercase:			Dict      =     rust_tokenizer.encode(UpperCAmelCase_  )
     self.assertListEqual(UpperCAmelCase_    ,       UpperCAmelCase_  )






   def 			_a    (						self :							Optional[Any]  )->      Dict:
     _lowercase:			Tuple      =     ErnieMTokenizer(UpperCAmelCase_    ,       do_lower_case=UpperCAmelCase_    ,       unk_token='<unk>'    ,       pad_token='<pad>'  )

     _lowercase:			Any      =     tokenizer.tokenize('This is a test'  )
     self.assertListEqual(UpperCAmelCase_    ,       ['▁this', '▁is', '▁a', '▁test']  )

     self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_  )    ,       [48, 25, 21, 12_89]  )

     _lowercase:			Union[str, Any]      =     tokenizer.tokenize('I was born in 92000, and this is falsé.'  )
     # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
     self.assertListEqual(
         UpperCAmelCase_    ,       ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']  )
     _lowercase:			List[Any]      =     tokenizer.convert_tokens_to_ids(UpperCAmelCase_  )
     self.assertListEqual(UpperCAmelCase_    ,       [31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]  )

     _lowercase:			List[str]      =     tokenizer.convert_ids_to_tokens(UpperCAmelCase_  )
     self.assertListEqual(
         UpperCAmelCase_    ,       ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']    ,       )






   def 			_a    (						self :							int  )->      Any:
     _lowercase:			str      =     ErnieMTokenizer(UpperCAmelCase_    ,       unk_token='<unk>'    ,       pad_token='<pad>'  )

     _lowercase:			Dict      =     tokenizer.encode('sequence builders'  )
     _lowercase:			Optional[int]      =     tokenizer.encode('multi-sequence build'  )

     _lowercase:			Dict      =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_  )
     _lowercase:			List[str]      =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_    ,       UpperCAmelCase_  )

     assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
     assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
         tokenizer.sep_token_id
     ] + text_a + [tokenizer.sep_token_id]






   @slow
   def 			_a    (						self :							Optional[Any]  )->      Union[str, Any]:
     _lowercase:			Dict      =     {'input_ids': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 9, 3_04, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 5, 5, 5, 16, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 64_60, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 35_59, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=UpperCAmelCase_    ,       model_name='susnato/ernie-m-base_pytorch'    ,       sequences=[
             'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
             'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
             'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
             'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
             'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
             'conditioning on both left and right context in all layers.',
             'The quick brown fox jumps over the lazy dog.',
         ]    ,       )

'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase       :     Dict			=    logging.get_logger(__name__)

UpperCamelCase       :     Tuple			=    {
    """flaubert/flaubert_small_cased""": """https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json""",
    """flaubert/flaubert_base_uncased""": """https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json""",
    """flaubert/flaubert_base_cased""": """https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json""",
    """flaubert/flaubert_large_cased""": """https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    int				=			"flaubert"
    A     :    List[str]				=			{
        "hidden_size": "emb_dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
        "n_words": "vocab_size",  # For backward compatibility
    }





    def __init__(					self     :						str							,							UpperCAmelCase_     :						Optional[int]=False							,							UpperCAmelCase_     :						List[Any]=0.0							,							UpperCAmelCase_     :						List[Any]=3_0_1_4_5							,							UpperCAmelCase_     :						Union[str, Any]=2_0_4_8							,							UpperCAmelCase_     :						Tuple=1_2							,							UpperCAmelCase_     :						str=1_6							,							UpperCAmelCase_     :						Tuple=0.1							,							UpperCAmelCase_     :						str=0.1							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						Union[str, Any]=False							,							UpperCAmelCase_     :						Any=False							,							UpperCAmelCase_     :						str=False							,							UpperCAmelCase_     :						Tuple=1							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						Tuple=5_1_2							,							UpperCAmelCase_     :						List[str]=2_0_4_8**-0.5							,							UpperCAmelCase_     :						List[Any]=1e-12							,							UpperCAmelCase_     :						Tuple=0.02							,							UpperCAmelCase_     :						Any=0							,							UpperCAmelCase_     :						List[Any]=1							,							UpperCAmelCase_     :						str=2							,							UpperCAmelCase_     :						Optional[Any]=3							,							UpperCAmelCase_     :						List[str]=5							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						List[Any]="first"							,							UpperCAmelCase_     :						Any=True							,							UpperCAmelCase_     :						Union[str, Any]=None							,							UpperCAmelCase_     :						Union[str, Any]=True							,							UpperCAmelCase_     :						Any=0.1							,							UpperCAmelCase_     :						List[Any]=5							,							UpperCAmelCase_     :						List[Any]=5							,							UpperCAmelCase_     :						Union[str, Any]=0							,							UpperCAmelCase_     :						Optional[Any]=0							,							UpperCAmelCase_     :						List[Any]=2							,							UpperCAmelCase_     :						List[Any]=0							,							**UpperCAmelCase_     :						List[Any]							,							):


         """simple docstring"""







         a					:							Any							= pre_norm
         a					:							str							= layerdrop
         a					:							int							= vocab_size
         a					:							str							= emb_dim
         a					:							Any							= n_layers
         a					:							Dict							= n_heads
         a					:							Union[str, Any]							= dropout
         a					:							List[Any]							= attention_dropout
         a					:							List[str]							= gelu_activation
         a					:							int							= sinusoidal_embeddings
         a					:							Union[str, Any]							= causal
         a					:							str							= asm
         a					:							Tuple							= n_langs
         a					:							Optional[Any]							= use_lang_emb
         a					:							str							= layer_norm_eps
         a					:							Optional[Any]							= bos_index
         a					:							Union[str, Any]							= eos_index
         a					:							str							= pad_index
         a					:							List[str]							= unk_index
         a					:							Dict							= mask_index
         a					:							Optional[Any]							= is_encoder
         a					:							str							= max_position_embeddings
         a					:							Optional[Any]							= embed_init_std
         a					:							str							= init_std
         a					:							Union[str, Any]							= summary_type
         a					:							Tuple							= summary_use_proj
         a					:							Tuple							= summary_activation
         a					:							str							= summary_proj_to_labels
         a					:							List[str]							= summary_first_dropout
         a					:							Any							= start_n_top
         a					:							Optional[Any]							= end_n_top
         a					:							List[Any]							= mask_token_id
         a					:							Optional[Any]							= lang_id

         if "n_words" in kwargs:
              a					:							int							= kwargs['n_words']

         super().__init__(pad_token_id=UpperCAmelCase_							,							bos_token_id=UpperCAmelCase_							,							**UpperCAmelCase_)






class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    @property
    def    SCREAMING_SNAKE_CASE_     (					self     :						str):


         """simple docstring"""







         if self.task == "multiple-choice":
              a					:							int							= {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
              a					:							Tuple							= {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ])

