

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

import pytest
import requests

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

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
  lowercase_     =       json.load(file_handle)
def 					a				(      A__     :       Tuple					)   ->				ModuleType:





     """simple docstring"""



     _lowercase     	=importlib.util.spec_from_file_location(file_path.name				,       str(snake_case__					)					)
     _lowercase     	=importlib.util.module_from_spec(snake_case__					)  # type: ignore
     spec.loader.exec_module(snake_case__					)  # type: ignore
     return module
def 					a				(      )   ->				list[pathlib.Path]:





     """simple docstring"""



     _lowercase     	=[]
     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(snake_case__					)
     return solution_file_paths
def 					a				(      )   ->				str:





     """simple docstring"""



     with open(os.environ['GITHUB_EVENT_PATH']					) as file:
          _lowercase     	=json.load(snake_case__					)
     return event["pull_request"]["url"] + "/files"
def 					a				(      )   ->				list[pathlib.Path]:





     """simple docstring"""



     _lowercase     	=[]
     _lowercase     	={
         'Accept': 'application/vnd.github.v3+json',
         'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
     }
     _lowercase     	=requests.get(get_files_url()				,       headers=snake_case__					).json()
     for file in files:
          _lowercase     	=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(snake_case__					)
     return solution_file_paths
def 					a				(      )   ->				list[pathlib.Path]:





     """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 A__					: F'''{path.parent.name}/{path.name}'''				,       )
def 					a				(      A__     :       Optional[int]					)   ->				None:





     """simple docstring"""



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





import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list





class 			__lowerCAmelCase					( unittest.TestCase							):




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	='<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' ' )
        _lowercase     	=dict(zip(lowerCAmelCase							,       range(len(lowerCAmelCase ) ) ) )

        _lowercase     	={
            'pad_token': '<pad>',
            'unk_token': '<unk>',
            'bos_token': '<s>',
            'eos_token': '</s>',
        }
        _lowercase     	={
            'feature_size': 1,
            'padding_value': 0.0,
            'sampling_rate': 16_000,
            'return_attention_mask': False,
            'do_normalize': True,
        }

        _lowercase     	=tempfile.mkdtemp()
        _lowercase     	=os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES['vocab_file'] )
        _lowercase     	=os.path.join(self.tmpdirname							,       lowerCAmelCase )
        with open(self.vocab_file							,       'w'							,       encoding='utf-8' ) as fp:
              fp.write(json.dumps(lowerCAmelCase ) + '\n' )

        with open(self.feature_extraction_file							,       'w'							,       encoding='utf-8' ) as fp:
              fp.write(json.dumps(lowerCAmelCase ) + '\n' )




  def   A__					(    self							,       **lowerCAmelCase )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname							,       **lowerCAmelCase )




  def   A__					(    self							,       **lowerCAmelCase )  ->			List[str]:
        '''simple docstring'''






        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname							,       **lowerCAmelCase )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname )




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=self.get_tokenizer()
        _lowercase     	=self.get_feature_extractor()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        processor.save_pretrained(self.tmpdirname )
        _lowercase     	=WavaVecaProcessor.from_pretrained(self.tmpdirname )

        self.assertEqual(processor.tokenizer.get_vocab()							,       tokenizer.get_vocab() )
        self.assertIsInstance(processor.tokenizer							,       lowerCAmelCase )

        self.assertEqual(processor.feature_extractor.to_json_string()							,       feature_extractor.to_json_string() )
        self.assertIsInstance(processor.feature_extractor							,       lowerCAmelCase )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=WavaVecaProcessor(tokenizer=self.get_tokenizer()							,       feature_extractor=self.get_feature_extractor() )
        processor.save_pretrained(self.tmpdirname )

        _lowercase     	=self.get_tokenizer(bos_token='(BOS)'							,       eos_token='(EOS)' )
        _lowercase     	=self.get_feature_extractor(do_normalize=lowerCAmelCase							,       padding_value=1.0 )

        _lowercase     	=WavaVecaProcessor.from_pretrained(
            self.tmpdirname							,       bos_token='(BOS)'							,       eos_token='(EOS)'							,       do_normalize=lowerCAmelCase							,       padding_value=1.0 )

        self.assertEqual(processor.tokenizer.get_vocab()							,       tokenizer_add_kwargs.get_vocab() )
        self.assertIsInstance(processor.tokenizer							,       lowerCAmelCase )

        self.assertEqual(processor.feature_extractor.to_json_string()							,       feature_extractor_add_kwargs.to_json_string() )
        self.assertIsInstance(processor.feature_extractor							,       lowerCAmelCase )




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        _lowercase     	=floats_list((3, 1_000) )

        _lowercase     	=feature_extractor(lowerCAmelCase							,       return_tensors='np' )
        _lowercase     	=processor(lowerCAmelCase							,       return_tensors='np' )

        for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()							,       input_processor[key].sum()							,       delta=1e-2 )




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        _lowercase     	='This is a test string'

        _lowercase     	=processor(text=lowerCAmelCase )

        _lowercase     	=tokenizer(lowerCAmelCase )

        for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]							,       encoded_processor[key] )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        _lowercase     	=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _lowercase     	=processor.batch_decode(lowerCAmelCase )
        _lowercase     	=tokenizer.batch_decode(lowerCAmelCase )

        self.assertListEqual(lowerCAmelCase							,       lowerCAmelCase )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        self.assertListEqual(
            processor.model_input_names							,       feature_extractor.model_input_names							,       msg='`processor` and `feature_extractor` model input names do not match'							,       )


