



from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_       :Union[str, Any]				  =				logging.get_logger(__name__)

a_       :Tuple				  =				{
    "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json",
    # See all LED models at https://huggingface.co/models?filter=led
}




class  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     """led"""
     _SCREAMING_SNAKE_CASE            =     {
         """num_attention_heads""": """encoder_attention_heads""",
         """hidden_size""": """d_model""",
         """attention_probs_dropout_prob""": """attention_dropout""",
         """initializer_range""": """init_std""",
     }






     def __init__(	self  :  int,			_snake_case  :  Any=5_0_2_6_5,			_snake_case  :  Union[str, Any]=1_6_3_8_4,			_snake_case  :  int=1_0_2_4,			_snake_case  :  Tuple=1_2,			_snake_case  :  Union[str, Any]=4_0_9_6,			_snake_case  :  str=1_6,			_snake_case  :  Optional[Any]=1_2,			_snake_case  :  List[Any]=4_0_9_6,			_snake_case  :  Dict=1_6,			_snake_case  :  List[str]=0.0,			_snake_case  :  Tuple=0.0,			_snake_case  :  Any=True,			_snake_case  :  str=True,			_snake_case  :  str="gelu",			_snake_case  :  Any=1_0_2_4,			_snake_case  :  Optional[Any]=0.1,			_snake_case  :  List[str]=0.0,			_snake_case  :  int=0.0,			_snake_case  :  Any=0.0_2,			_snake_case  :  int=2,			_snake_case  :  Tuple=0.0,			_snake_case  :  Dict=1,			_snake_case  :  List[Any]=0,			_snake_case  :  Union[str, Any]=2,			_snake_case  :  Union[List[int], int] = 5_1_2,			**_snake_case  :  List[Any],			)       ->Any:
            snake_case__							:     Optional[Any]            =     vocab_size
            snake_case__							:     Tuple            =     max_encoder_position_embeddings
            snake_case__							:     List[str]            =     max_decoder_position_embeddings
            snake_case__							:     Tuple            =     d_model
            snake_case__							:     Optional[int]            =     encoder_ffn_dim
            snake_case__							:     Optional[int]            =     encoder_layers
            snake_case__							:     List[str]            =     encoder_attention_heads
            snake_case__							:     Dict            =     decoder_ffn_dim
            snake_case__							:     Optional[int]            =     decoder_layers
            snake_case__							:     List[Any]            =     decoder_attention_heads
            snake_case__							:     Dict            =     dropout
            snake_case__							:     Tuple            =     attention_dropout
            snake_case__							:     str            =     activation_dropout
            snake_case__							:     Dict            =     activation_function
            snake_case__							:     int            =     init_std
            snake_case__							:     int            =     encoder_layerdrop
            snake_case__							:     Optional[int]            =     decoder_layerdrop
            snake_case__							:     str            =     classifier_dropout
            snake_case__							:     Union[str, Any]            =     use_cache
            snake_case__							:     Union[str, Any]            =     encoder_layers
            snake_case__							:     Any            =     attention_window

            super().__init__(
                pad_token_id=_snake_case,			bos_token_id=_snake_case,			eos_token_id=_snake_case,			is_encoder_decoder=_snake_case,			decoder_start_token_id=_snake_case,			**_snake_case,			)











import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class  snake_case__						(	lowerCAmelCase_    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     BertweetTokenizer
     _SCREAMING_SNAKE_CASE            =     False






     def        lowercase_       (	self  :  List[str] )       ->Union[str, Any]:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            snake_case__							:     List[Any]            =     ['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
            snake_case__							:     Tuple            =     dict(zip(_snake_case,			range(len(_snake_case ) ) ) )
            snake_case__							:     int            =     ['#version: 0.2', 'a m</w>']
            snake_case__							:     Dict            =     {'unk_token': '<unk>'}

            snake_case__							:     Tuple            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['vocab_file'] )
            snake_case__							:     Tuple            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['merges_file'] )
            with open(self.vocab_file,			'w',			encoding='utf-8' ) as fp:
                   for token in vocab_tokens:
                          fp.write(F'''{token} {vocab_tokens[token]}\n''' )
            with open(self.merges_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write('\n'.join(_snake_case ) )






     def        lowercase_       (	self  :  List[Any],			**_snake_case  :  List[Any] )       ->Optional[Any]:
            kwargs.update(self.special_tokens_map )
            return BertweetTokenizer.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Tuple )       ->Union[str, Any]:
            snake_case__							:     List[str]            =     'I am VinAI Research'
            snake_case__							:     str            =     'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
            return input_text, output_text






     def        lowercase_       (	self  :  str )       ->int:
            snake_case__							:     Dict            =     BertweetTokenizer(self.vocab_file,			self.merges_file,			**self.special_tokens_map )
            snake_case__							:     List[str]            =     'I am VinAI Research'
            snake_case__							:     str            =     'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
            snake_case__							:     Optional[int]            =     tokenizer.tokenize(_snake_case )
            self.assertListEqual(_snake_case,			_snake_case )

            snake_case__							:     Tuple            =     tokens + [tokenizer.unk_token]

            snake_case__							:     Dict            =     [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case ),			_snake_case )






