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__											=					{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : int      =			'roberta-prelayernorm'


   def __init__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[int]=5_0_2_6_5       ,  __UpperCAmelCase     :						List[str]=7_6_8       ,  __UpperCAmelCase     :						List[Any]=1_2       ,  __UpperCAmelCase     :						Any=1_2       ,  __UpperCAmelCase     :						Optional[int]=3_0_7_2       ,  __UpperCAmelCase     :						int="gelu"       ,  __UpperCAmelCase     :						int=0.1       ,  __UpperCAmelCase     :						Optional[Any]=0.1       ,  __UpperCAmelCase     :						Union[str, Any]=5_1_2       ,  __UpperCAmelCase     :						List[Any]=2       ,  __UpperCAmelCase     :						Tuple=0.02       ,  __UpperCAmelCase     :						List[str]=1E-12       ,  __UpperCAmelCase     :						Any=1       ,  __UpperCAmelCase     :						Optional[int]=0       ,  __UpperCAmelCase     :						List[str]=2       ,  __UpperCAmelCase     :						Dict="absolute"       ,  __UpperCAmelCase     :						List[Any]=True       ,  __UpperCAmelCase     :						str=None       ,  **__UpperCAmelCase     :						Dict       ,  )						->				Optional[Any]:
         """simple docstring"""



         super().__init__(pad_token_id=__UpperCAmelCase       ,  bos_token_id=__UpperCAmelCase       ,  eos_token_id=__UpperCAmelCase       ,  **__UpperCAmelCase		)

         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  num_hidden_layers
         UpperCAmelCase__								=  num_attention_heads
         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  intermediate_size
         UpperCAmelCase__								=  hidden_dropout_prob
         UpperCAmelCase__								=  attention_probs_dropout_prob
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  type_vocab_size
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  layer_norm_eps
         UpperCAmelCase__								=  position_embedding_type
         UpperCAmelCase__								=  use_cache
         UpperCAmelCase__								=  classifier_dropout





class      A							(				UpperCAmelCase_     ):


   @property
   def 			lowercase_      (self     :						int		)						->				Mapping[str, Mapping[int, str]]:
         """simple docstring"""



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




'''simple docstring'''

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    BlenderbotSmallTokenizer
       SCREAMING_SNAKE_CASE							=    False

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

        lowercase__				:    List[Any]  =					['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
        lowercase__				:    Any  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )

        lowercase__				:    Optional[Any]  =					['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
        lowercase__				:    Dict  =					{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

        lowercase__				:    List[str]  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Any  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  Tuple:
        kwargs.update(self.special_tokens_map )
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  int:
        lowercase__				:    str  =					'''adapt act apte'''
        lowercase__				:    Any  =					'''adapt act apte'''
        return input_text, output_text

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Optional[int]  =					BlenderbotSmallTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Tuple  =					'''adapt act apte'''
        lowercase__				:    Dict  =					['''adapt''', '''act''', '''ap@@''', '''te''']
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    Any  =					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

        lowercase__				:    int  =					[0, 1, 2, 3, 4, 5]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    int  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        assert tok('''sam''' ).input_ids == [1384]
        lowercase__				:    str  =					'''I am a small frog.'''
        lowercase__				:    Union[str, Any]  =					tok([src_text]	,   padding=__lowerCAmelCase	,   truncation=__lowerCAmelCase )['''input_ids''']
        lowercase__				:    List[str]  =					tok.batch_decode(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase )[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Optional[Any]  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        lowercase__				:    Optional[Any]  =					'''I am a small frog .'''
        lowercase__				:    Any  =					'''.'''
        lowercase__				:    List[Any]  =					tok(__lowerCAmelCase )['''input_ids''']
        lowercase__				:    Optional[Any]  =					tok(__lowerCAmelCase )['''input_ids''']

        assert encoded[-1] == encoded_dot[0]

