

"""simple docstring"""




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class    __lowercase     (      __lowerCamelCase		,     unittest.TestCase   ):
  snake_case_			    =    BartTokenizer
  snake_case_			    =    BartTokenizerFast
  snake_case_			    =    True
  snake_case_			    =    filter_roberta_detectors

  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   super().setUp()
   UpperCAmelCase__   :					Optional[Any]   				=   [
       """l""",
       """o""",
       """w""",
       """e""",
       """r""",
       """s""",
       """t""",
       """i""",
       """d""",
       """n""",
       """\u0120""",
       """\u0120l""",
       """\u0120n""",
       """\u0120lo""",
       """\u0120low""",
       """er""",
       """\u0120lowest""",
       """\u0120newer""",
       """\u0120wider""",
       """<unk>""",
   ]
   UpperCAmelCase__   :					Optional[Any]   				=   dict(zip(A   ,range(len(A      )      )      )      )
   UpperCAmelCase__   :					Union[str, Any]   				=   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
   UpperCAmelCase__   :					Optional[int]   				=   {"""unk_token""": """<unk>"""}

   UpperCAmelCase__   :					Optional[int]   				=   os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES["""vocab_file"""]      )
   UpperCAmelCase__   :					Optional[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 				__lowercase    (					self						:     Dict   ,**A						:     Union[str, Any]      ):






   '''simple docstring'''
   kwargs.update(self.special_tokens_map      )
   return self.tokenizer_class.from_pretrained(self.tmpdirname   ,**A      )

  def 				__lowercase    (					self						:     List[str]   ,**A						:     Any      ):






   '''simple docstring'''
   kwargs.update(self.special_tokens_map      )
   return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,**A      )

  def 				__lowercase    (					self						:     List[str]   ,A						:     Dict      ):






   '''simple docstring'''
   return "lower newer", "lower newer"

  @cached_property
  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   return BartTokenizer.from_pretrained("""facebook/bart-large"""      )

  @cached_property
  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   return BartTokenizerFast.from_pretrained("""facebook/bart-large"""      )

  @require_torch
  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   UpperCAmelCase__   :					Dict   				=   [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    UpperCAmelCase__   :					Any   				=   tokenizer(A   ,max_length=len(A      )   ,padding=A   ,return_tensors="""pt"""      )
    self.assertIsInstance(A   ,A      )

    self.assertEqual((2, 9)   ,batch.input_ids.shape      )
    self.assertEqual((2, 9)   ,batch.attention_mask.shape      )
    UpperCAmelCase__   :					List[str]   				=   batch.input_ids.tolist()[0]
    self.assertListEqual(A   ,A      )
    # Test that special tokens are reset

  @require_torch
  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    UpperCAmelCase__   :					Tuple   				=   tokenizer(A   ,padding=A   ,return_tensors="""pt"""      )
    # check if input_ids are returned and no labels
    self.assertIn("""input_ids"""   ,A      )
    self.assertIn("""attention_mask"""   ,A      )
    self.assertNotIn("""labels"""   ,A      )
    self.assertNotIn("""decoder_attention_mask"""   ,A      )

  @require_torch
  def 				__lowercase    (					self						:     Any      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   [
       """Summary of the text.""",
       """Another summary.""",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    UpperCAmelCase__   :					List[str]   				=   tokenizer(text_target=A   ,max_length=32   ,padding="""max_length"""   ,return_tensors="""pt"""      )
    self.assertEqual(32   ,targets["""input_ids"""].shape[1]      )

  @require_torch
  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    UpperCAmelCase__   :					Union[str, Any]   				=   tokenizer(
        ["""I am a small frog""" * 1_024, """I am a small frog"""]   ,padding=A   ,truncation=A   ,return_tensors="""pt"""      )
    self.assertIsInstance(A   ,A      )
    self.assertEqual(batch.input_ids.shape   ,(2, 1_024)      )

  @require_torch
  def 				__lowercase    (					self						:     List[str]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   ["""A long paragraph for summarization."""]
   UpperCAmelCase__   :					Tuple   				=   [
       """Summary of the text.""",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    UpperCAmelCase__   :					Optional[Any]   				=   tokenizer(A   ,return_tensors="""pt"""      )
    UpperCAmelCase__   :					Optional[int]   				=   tokenizer(text_target=A   ,return_tensors="""pt"""      )
    UpperCAmelCase__   :					int   				=   inputs["""input_ids"""]
    UpperCAmelCase__   :					int   				=   targets["""input_ids"""]
    self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
    self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
    self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
    self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )

  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   pass




  def 				__lowercase    (					self						:     Optional[int]      ):






   '''simple docstring'''
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
     UpperCAmelCase__   :					int   				=   self.rust_tokenizer_class.from_pretrained(A   ,**A      )
     UpperCAmelCase__   :					Optional[Any]   				=   self.tokenizer_class.from_pretrained(A   ,**A      )
     UpperCAmelCase__   :					int   				=   """A, <mask> AllenNLP sentence."""
     UpperCAmelCase__   :					List[str]   				=   tokenizer_r.encode_plus(A   ,add_special_tokens=A   ,return_token_type_ids=A      )
     UpperCAmelCase__   :					str   				=   tokenizer_p.encode_plus(A   ,add_special_tokens=A   ,return_token_type_ids=A      )

     # token_type_ids should put 0 everywhere
     self.assertEqual(sum(tokens_r["""token_type_ids"""]      )   ,sum(tokens_p["""token_type_ids"""]      )      )

     # attention_mask should put 1 everywhere, so sum over length should be 1
     self.assertEqual(
         sum(tokens_r["""attention_mask"""]      ) / len(tokens_r["""attention_mask"""]      )   ,sum(tokens_p["""attention_mask"""]      ) / len(tokens_p["""attention_mask"""]      )   ,)

     UpperCAmelCase__   :					str   				=   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]      )
     UpperCAmelCase__   :					Any   				=   tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]      )

     # Rust correctly handles the space before the mask while python doesnt
     self.assertSequenceEqual(tokens_p["""input_ids"""]   ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]      )
     self.assertSequenceEqual(tokens_r["""input_ids"""]   ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]      )

     self.assertSequenceEqual(
         A   ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]      )
     self.assertSequenceEqual(
         A   ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]      )








"""simple docstring"""




import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

__UpperCAmelCase	=			pathlib.Path.cwd().joinpath('project_euler')
__UpperCAmelCase	=			pathlib.Path.cwd().joinpath(
    'scripts', 'project_euler_answers.json'
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
      __UpperCAmelCase	=			json.load(file_handle)
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Dict   				=   importlib.util.spec_from_file_location(file_path.name							,      str(__UpperCamelCase ) )
 UpperCAmelCase__   :					Optional[Any]   				=   importlib.util.module_from_spec(__UpperCamelCase )  # type: ignore
 spec.loader.exec_module(__UpperCamelCase )  # type: ignore
 return module
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Dict   				=   []
 for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
  if problem_dir_path.is_file() or problem_dir_path.name.startswith("""_""" ):
   continue
  for file_path in problem_dir_path.iterdir():
   if file_path.suffix != ".py" or file_path.name.startswith(("""_""", """test""") ):
    continue
   solution_file_paths.append(__UpperCamelCase )
 return solution_file_paths
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 with open(os.environ["""GITHUB_EVENT_PATH"""] ) as file:
  UpperCAmelCase__   :					List[Any]   				=   json.load(__UpperCamelCase )
 return event["pull_request"]["url"] + "/files"
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Tuple   				=   []
 UpperCAmelCase__   :					List[str]   				=   {
     """Accept""": """application/vnd.github.v3+json""",
     """Authorization""": """token """ + os.environ["""GITHUB_TOKEN"""],
 }
 UpperCAmelCase__   :					Tuple   				=   requests.get(get_files_url()							,      headers=__UpperCamelCase ).json()
 for file in files:
  UpperCAmelCase__   :					List[str]   				=   pathlib.Path.cwd().joinpath(file["""filename"""] )
  if (
      filepath.suffix != ".py"
      or filepath.name.startswith(("""_""", """test""") )
      or not filepath.name.startswith("""sol""" )
  ):
   continue
  solution_file_paths.append(__UpperCamelCase )
 return solution_file_paths
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 if os.environ.get("""CI""" ) and os.environ.get("""GITHUB_EVENT_NAME""" ) == "pull_request":
  # Return only if there are any, otherwise default to all solutions
  if filepaths := added_solution_file_path():
   return filepaths
 return all_solution_file_paths()




@pytest.mark.parametrize(
    """solution_path"""							,      collect_solution_file_paths()							,      ids=lambda __UpperCamelCase : F"{path.parent.name}/{path.name}"							,      )
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					str   				=   solution_path.parent.name[8:].zfill(3 )
 UpperCAmelCase__   :					str   				=   PROBLEM_ANSWERS[problem_number]
 UpperCAmelCase__   :					int   				=   convert_path_to_module(__UpperCamelCase )
 UpperCAmelCase__   :					str   				=   str(solution_module.solution() )  # type: ignore
 UpperCAmelCase__   :					Optional[Any]   				=   hashlib.shaaaa(answer.encode() ).hexdigest()
 assert (
     answer == expected
 ), F"Expected solution to {problem_number} to have hash {expected}, got {answer}"





