



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


a_       :Optional[Any]				  =				logging.get_logger(__name__)

a_       :List[str]				  =				{
    "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json",
    "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json",
    "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json",
    "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json",
    "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json",
    "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json",
}




class  snake_case__						(	A__					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     """opt"""
     _SCREAMING_SNAKE_CASE            =     ["""past_key_values"""]






     def __init__(	self  :  str,			_snake_case  :  List[str]=5_0_2_7_2,			_snake_case  :  List[str]=7_6_8,			_snake_case  :  Optional[int]=1_2,			_snake_case  :  List[Any]=3_0_7_2,			_snake_case  :  List[str]=2_0_4_8,			_snake_case  :  List[Any]=True,			_snake_case  :  Optional[int]=False,			_snake_case  :  Optional[Any]=None,			_snake_case  :  Tuple=0.1,			_snake_case  :  Any=0.0,			_snake_case  :  Any=1_2,			_snake_case  :  Optional[Any]="relu",			_snake_case  :  List[str]=0.0,			_snake_case  :  Optional[Any]=0.0_2,			_snake_case  :  str=True,			_snake_case  :  List[str]=1,			_snake_case  :  Dict=2,			_snake_case  :  Union[str, Any]=2,			_snake_case  :  str=True,			_snake_case  :  Optional[Any]=True,			**_snake_case  :  Optional[Any],			)       ->str:
            super().__init__(
                pad_token_id=snake_case_,			bos_token_id=snake_case_,			eos_token_id=snake_case_,			**snake_case_,			)
            snake_case__							:     List[str]            =     vocab_size
            snake_case__							:     Union[str, Any]            =     max_position_embeddings
            snake_case__							:     Any            =     num_attention_heads
            snake_case__							:     Tuple            =     word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
            snake_case__							:     Dict            =     ffn_dim
            snake_case__							:     Optional[Any]            =     hidden_size
            snake_case__							:     Optional[int]            =     num_hidden_layers
            snake_case__							:     List[str]            =     dropout
            snake_case__							:     List[Any]            =     attention_dropout
            snake_case__							:     Union[str, Any]            =     activation_function
            snake_case__							:     Any            =     init_std
            snake_case__							:     List[Any]            =     layerdrop
            snake_case__							:     Any            =     use_cache
            snake_case__							:     List[str]            =     do_layer_norm_before
            # We keep these variables at `True` for backward compatibility.
            snake_case__							:     int            =     enable_bias
            snake_case__							:     List[str]            =     layer_norm_elementwise_affine

            # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
            # with checkpoints that have been fine-tuned before transformers v4.20.1
            # see https://github.com/facebookresearch/metaseq/pull/164
            snake_case__							:     List[Any]            =     _remove_final_layer_norm











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 )






