





from collections import OrderedDict
from typing import Mapping

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


__UpperCAmelCase						=      logging.get_logger(__name__)

__UpperCAmelCase						=      {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Dict      =		"""ernie"""



       def __init__(     self	:   Dict					,			UpperCAmelCase	:   List[str]=3_05_22					,			UpperCAmelCase	:   Union[str, Any]=7_68					,			UpperCAmelCase	:   Union[str, Any]=12					,			UpperCAmelCase	:   Tuple=12					,			UpperCAmelCase	:   Dict=30_72					,			UpperCAmelCase	:   str="gelu"					,			UpperCAmelCase	:   Tuple=0.1					,			UpperCAmelCase	:   List[Any]=0.1					,			UpperCAmelCase	:   Tuple=5_12					,			UpperCAmelCase	:   Optional[Any]=2					,			UpperCAmelCase	:   Dict=3					,			UpperCAmelCase	:   str=False					,			UpperCAmelCase	:   str=0.02					,			UpperCAmelCase	:   int=1e-1_2					,			UpperCAmelCase	:   Dict=0					,			UpperCAmelCase	:   Optional[int]="absolute"					,			UpperCAmelCase	:   List[Any]=True					,			UpperCAmelCase	:   List[Any]=None					,			**UpperCAmelCase	:   Union[str, Any]					,			):
           super().__init__(pad_token_id=UpperCAmelCase					,			**UpperCAmelCase			)

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






class 		__a							(	__UpperCamelCase      ):



       @property
       def  A							(     self	:   Optional[int]			):
           if self.task == "multiple-choice":
               lowerCAmelCase_					:	str						=   {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
               lowerCAmelCase_					:	Optional[Any]						=   {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
                   ("""task_type_ids""", dynamic_axis),
               ]			)









import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 		__a							(	unittest.TestCase      ):



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""			)



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	List[str]						=   self.tokenizer_ab
           lowerCAmelCase_					:	Any						=   """ I am a small frog."""
           lowerCAmelCase_					:	Union[str, Any]						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Dict						=   self.rust_tokenizer_ab
           lowerCAmelCase_					:	str						=   """ I am a small frog."""
           lowerCAmelCase_					:	Tuple						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   str			):
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]



       def  A							(     self	:   List[str]			):
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]


