




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_					(				UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   BlenderbotSmallTokenizer
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->int:





           '''simple docstring'''

           super().setUp()

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

           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(UpperCAmelCase__) + '''\n''')
           with open(self.merges_file      ,				'''w'''      ,				encoding='''utf-8''') as fp:
                  fp.write('''\n'''.join(UpperCAmelCase__))


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				**UpperCAmelCase__   :			Optional[Any])		->Union[str, Any]:





           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname      ,				**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			str)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					'''adapt act apte'''
           A__   				=					'''adapt act apte'''
           return input_text, output_text


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Dict:





           '''simple docstring'''

           A__   				=					BlenderbotSmallTokenizer(self.vocab_file      ,				self.merges_file      ,				**self.special_tokens_map)
           A__   				=					'''adapt act apte'''
           A__   				=					['''adapt''', '''act''', '''ap@@''', '''te''']
           A__   				=					tokenizer.tokenize(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)

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

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->int:





           '''simple docstring'''

           A__   				=					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')
           assert tok('''sam''').input_ids == [1_384]
           A__   				=					'''I am a small frog.'''
           A__   				=					tok([src_text]      ,				padding=UpperCAmelCase__      ,				truncation=UpperCAmelCase__)['''input_ids''']
           A__   				=					tok.batch_decode(UpperCAmelCase__      ,				skip_special_tokens=UpperCAmelCase__      ,				clean_up_tokenization_spaces=UpperCAmelCase__)[0]
           assert src_text != decoded  # I wish it did!
           assert decoded == "i am a small frog ."





    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')
           A__   				=					'''I am a small frog .'''
           A__   				=					'''.'''
           A__   				=					tok(UpperCAmelCase__)['''input_ids''']
           A__   				=					tok(UpperCAmelCase__)['''input_ids''']

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








from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       return DownloadCommand(args.model				,       args.cache_dir				,       args.force				,       args.trust_remote_code		)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    @staticmethod
    def  SCREAMING_SNAKE_CASE							(    UpperCAmelCase__   :			ArgumentParser)		->Any:





           '''simple docstring'''

           A__   				=					parser.add_parser('''download''')
           download_parser.add_argument(
               '''--cache-dir'''      ,				type=UpperCAmelCase__      ,				default=UpperCAmelCase__      ,				help='''Path to location to store the models''')
           download_parser.add_argument(
               '''--force'''      ,				action='''store_true'''      ,				help='''Force the model to be download even if already in cache-dir''')
           download_parser.add_argument(
               '''--trust-remote-code'''      ,				action='''store_true'''      ,				help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''      ,				)
           download_parser.add_argument('''model'''      ,				type=UpperCAmelCase__      ,				help='''Name of the model to download''')
           download_parser.set_defaults(func=UpperCAmelCase__)


    def __init__(    self   :			Dict      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			bool      ,				UpperCAmelCase__   :			bool)		->int:





           '''simple docstring'''

           A__   				=					model
           A__   				=					cache
           A__   				=					force
           A__   				=					trust_remote_code





    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Union[str, Any]:





           '''simple docstring'''

           from ..models.auto import AutoModel, AutoTokenizer

           AutoModel.from_pretrained(
               self._model      ,				cache_dir=self._cache      ,				force_download=self._force      ,				trust_remote_code=self._trust_remote_code)
           AutoTokenizer.from_pretrained(
               self._model      ,				cache_dir=self._cache      ,				force_download=self._force      ,				trust_remote_code=self._trust_remote_code)


