
import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 						UpperCamelCase__    (			SCREAMING_SNAKE_CASE_       ):
   def  lowerCAmelCase     (self					:   Optional[int]   ,							snake_case_					:   str  ):

    with open(__a   ,							encoding='''utf-8'''  ) as input_file:
     __a					:    int													=    re.compile(r'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''  )
     __a					:    Optional[int]													=    input_file.read()
     __a					:    Tuple													=    regexp.search(__a  )

    return match
   def  lowerCAmelCase     (self					:   Any   ,							snake_case_					:   str  ):

    with open(__a   ,							encoding='''utf-8'''  ) as input_file:
     __a					:    Dict													=    re.compile(r'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'''   ,							re.DOTALL  )
     __a					:    Tuple													=    input_file.read()
     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
     __a					:    List[str]													=    regexp.finditer(__a  )

    __a					:    Optional[Any]													=    [match for match in matches if match is not None and match.group(1  ) is not None]
    return matches[0] if matches else None
   def  lowerCAmelCase     (self					:   Any  ):
    __a					:    Tuple													=    Path('''./datasets'''  )
    __a					:    Dict													=    list(dataset_paths.absolute().glob('''**/*.py'''  )  )

    for dataset in dataset_files:
     if self._no_encoding_on_file_open(str(__a  )  ):
      raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}"  )







   def  lowerCAmelCase     (self					:   Optional[Any]  ):
    __a					:    Optional[int]													=    Path('''./datasets'''  )
    __a					:    List[Any]													=    list(dataset_paths.absolute().glob('''**/*.py'''  )  )

    for dataset in dataset_files:
     if self._no_print_statements(str(__a  )  ):
      raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead."  )





'''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]



