

'''simple docstring'''
_UpperCAmelCase  :		Dict  =		{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}





'''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  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= BlenderbotSmallTokenizer
 A__				= False
 def A_   (   self						:							Any  )      ->   str:







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

       __snake_case :   Any													=    ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
       __snake_case :   str													=    dict(zip(__a      ,     range(len(__a  )  )  )  )

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

       __snake_case :   str													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['vocab_file']  )
       __snake_case :   Union[str, 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(__a  ) + '\n'  )
       with open(self.merges_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write('\n'.join(__a  )  )
 def A_   (   self						:							Tuple      ,     **__a						:							Optional[int]  )      ->   List[str]:







       '''simple docstring'''
       kwargs.update(self.special_tokens_map  )
       return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname      ,     **__a  )
 def A_   (   self						:							int      ,     __a						:							int  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[str]													=    'adapt act apte'
       __snake_case :   List[str]													=    'adapt act apte'
       return input_text, output_text
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       __snake_case :   Dict													=    BlenderbotSmallTokenizer(self.vocab_file      ,     self.merges_file      ,     **self.special_tokens_map  )
       __snake_case :   List[str]													=    'adapt act apte'
       __snake_case :   List[Any]													=    ['adapt', 'act', 'ap@@', 'te']
       __snake_case :   Optional[int]													=    tokenizer.tokenize(__a  )
       self.assertListEqual(__a      ,     __a  )

       __snake_case :   List[str]													=    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

       __snake_case :   List[str]													=    [0, 1, 2, 3, 4, 5]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__a  )      ,     __a  )
 def A_   (   self						:							int  )      ->   str:







       '''simple docstring'''
       __snake_case :   int													=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
       assert tok('sam'  ).input_ids == [1384]
       __snake_case :   List[str]													=    'I am a small frog.'
       __snake_case :   List[Any]													=    tok([src_text]      ,     padding=__a      ,     truncation=__a  )['input_ids']
       __snake_case :   int													=    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 A_   (   self						:							List[str]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
       __snake_case :   Optional[Any]													=    'I am a small frog .'
       __snake_case :   List[str]													=    '.'
       __snake_case :   List[str]													=    tok(__a  )['input_ids']
       __snake_case :   str													=    tok(__a  )['input_ids']

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



