'''simple docstring'''






import pytest

import datasets


# Import fixture modules as plugins
_UpperCAmelCase    :     Union[str, Any]         =       ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Tuple   ,		__snake_case							:			Union[str, Any]				):
     # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
     for item in items:
          if any(marker in item.keywords for marker in ['integration', 'unit']				):
               continue
          item.add_marker(pytest.mark.unit				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[Any]				):
     config.addinivalue_line('markers'   ,		'torchaudio_latest: mark test to run with torchaudio>=0.12'				)





@pytest.fixture(autouse=__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any   ,		__snake_case							:			List[Any]				):
     # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
     _A    		=  tmp_path_factory.getbasetemp() / 'cache'
     _A    		=  test_hf_cache_home / 'datasets'
     _A    		=  test_hf_cache_home / 'metrics'
     _A    		=  test_hf_cache_home / 'modules'
     monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'   ,		str(__snake_case				)				)
     monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'   ,		str(__snake_case				)				)
     monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'   ,		str(__snake_case				)				)
     _A    		=  test_hf_datasets_cache / 'downloads'
     monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'   ,		str(__snake_case				)				)
     _A    		=  test_hf_datasets_cache / 'downloads' / 'extracted'
     monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'   ,		str(__snake_case				)				)





@pytest.fixture(autouse=__snake_case   ,		scope='session'				)
def  _SCREAMING_SNAKE_CASE		(      ):
     datasets.disable_progress_bar()





@pytest.fixture(autouse=__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[Any]				):
     # don't take tests into account when counting downloads
     monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'   ,		__snake_case				)





@pytest.fixture
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[Any]				):
     # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
     # To be removed once SQLAlchemy 2.0 supported
     monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'   ,		__snake_case				)





"""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 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     BlenderbotSmallTokenizer
						_lowerCamelCase:     List[Any]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							super().setUp()

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

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

							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							A     = 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(A_   ) + '\n'   )
							with open(self.merges_file	,'w'	,encoding='utf-8'   ) as fp:
								fp.write('\n'.join(A_   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,**A_  :   Union[str, Any]   )  -> Optional[int]:
							kwargs.update(self.special_tokens_map   )
							return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   Tuple   )  -> List[Any]:
							A     = 'adapt act apte'
							A     = 'adapt act apte'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[str]:
							A     = BlenderbotSmallTokenizer(self.vocab_file	,self.merges_file	,**self.special_tokens_map   )
							A     = 'adapt act apte'
							A     = ['adapt', 'act', 'ap@@', 'te']
							A     = tokenizer.tokenize(A_   )
							self.assertListEqual(A_	,A_   )

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

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							assert tok('sam'   ).input_ids == [1384]
							A     = 'I am a small frog.'
							A     = tok([src_text]	,padding=A_	,truncation=A_   )['input_ids']
							A     = tok.batch_decode(A_	,skip_special_tokens=A_	,clean_up_tokenization_spaces=A_   )[0]
							assert src_text != decoded  # I wish it did!
							assert decoded == "i am a small frog ."




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> int:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							A     = 'I am a small frog .'
							A     = '.'
							A     = tok(A_   )['input_ids']
							A     = tok(A_   )['input_ids']

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