



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 			__lowerCAmelCase  (						_snake_case	,						unittest.TestCase ):
 lowerCAmelCase__							       =	BlenderbotSmallTokenizer
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().setUp()

    __lowerCamelCase    				=     ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
    __lowerCamelCase    				=     dict(zip(_lowerCamelCase						,					range(len(_lowerCamelCase					)					)					)					)

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

    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''vocab_file''']					)
    __lowerCamelCase    				=     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						,					**__UpperCAmelCase					):



    '''simple docstring'''
    kwargs.update(self.special_tokens_map					)
    return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname						,					**_lowerCamelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''adapt act apte'''
    __lowerCamelCase    				=     '''adapt act apte'''
    return input_text, output_text




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     BlenderbotSmallTokenizer(self.vocab_file						,					self.merges_file						,					**self.special_tokens_map					)
    __lowerCamelCase    				=     '''adapt act apte'''
    __lowerCamelCase    				=     ['''adapt''', '''act''', '''ap@@''', '''te''']
    __lowerCamelCase    				=     tokenizer.tokenize(_lowerCamelCase					)
    self.assertListEqual(_lowerCamelCase						,					_lowerCamelCase					)

    __lowerCamelCase    				=     [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

    __lowerCamelCase    				=     [0, 1, 2, 3, 4, 5]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase					)						,					_lowerCamelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''					)
    assert tok('''sam'''					).input_ids == [1384]
    __lowerCamelCase    				=     '''I am a small frog.'''
    __lowerCamelCase    				=     tok([src_text]						,					padding=_lowerCamelCase						,					truncation=_lowerCamelCase					)['''input_ids''']
    __lowerCamelCase    				=     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					):



    '''simple docstring'''
    __lowerCamelCase    				=     BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''					)
    __lowerCamelCase    				=     '''I am a small frog .'''
    __lowerCamelCase    				=     '''.'''
    __lowerCamelCase    				=     tok(_lowerCamelCase					)['''input_ids''']
    __lowerCamelCase    				=     tok(_lowerCamelCase					)['''input_ids''']

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










from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None







class 						_snake_case    ( _snake_case				):




 def __init__(						self   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=2   ,    _lowerCamelCase=512   ,    _lowerCamelCase="cls"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    **_lowerCamelCase   ,    ):
       super().__init__(pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase   ,    **_lowerCamelCase       )
       a		:Tuple									=			project_dim
       a		:Optional[int]									=			pooler_fn
       a		:int									=			learn_encoder
       a		:int									=			use_attention_mask







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			[r'pooler', r'logit_scale']
 SCREAMING_SNAKE_CASE__ =			[r'position_ids', r'predictions.decoder.bias']
 SCREAMING_SNAKE_CASE__ =			'roberta'
 SCREAMING_SNAKE_CASE__ =			RobertaSeriesConfig




 def __init__(						self   ,    _lowerCamelCase       ):
       super().__init__(_lowerCamelCase       )
       a		:Tuple									=			XLMRobertaModel(_lowerCamelCase       )
       a		:Optional[Any]									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
       a		:Optional[int]									=			getattr(_lowerCamelCase   ,    '''has_pre_transformation'''   ,    _lowerCamelCase       )
       if self.has_pre_transformation:
             a		:Tuple									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
             a		:Union[str, Any]									=			nn.LayerNorm(config.hidden_size   ,    eps=config.layer_norm_eps       )
       self.post_init()




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    ):
       a		:Optional[int]									=			return_dict if return_dict is not None else self.config.use_return_dict

       a		:int									=			self.base_model(
           input_ids=_lowerCamelCase   ,    attention_mask=_lowerCamelCase   ,    token_type_ids=_lowerCamelCase   ,    position_ids=_lowerCamelCase   ,    head_mask=_lowerCamelCase   ,    inputs_embeds=_lowerCamelCase   ,    encoder_hidden_states=_lowerCamelCase   ,    encoder_attention_mask=_lowerCamelCase   ,    output_attentions=_lowerCamelCase   ,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states   ,    return_dict=_lowerCamelCase   ,    )

       if self.has_pre_transformation:
             a		:Optional[int]									=			outputs['''hidden_states'''][-2]
             a		:List[Any]									=			self.pre_LN(_lowerCamelCase       )
             a		:Optional[Any]									=			self.transformation_pre(_lowerCamelCase       )

             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )
       else:
             a		:List[str]									=			self.transformation(outputs.last_hidden_state       )
             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )





