






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

import pytest
import requests

snake_case_   :	Dict							      =pathlib.Path.cwd().joinpath('''project_euler''')
snake_case_   :	Any							      =pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
					snake_case_   :	dict[str, str]							      =json.load(file_handle)



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							importlib.util.spec_from_file_location(file_path.name					,     str(__snake_case	)	)
		__A    =							importlib.util.module_from_spec(__snake_case	)  # type: ignore
		spec.loader.exec_module(__snake_case	)  # type: ignore
		return module



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							[]
		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      UpperCAmelCase						( ):


		'''simple docstring'''

		with open(os.environ["GITHUB_EVENT_PATH"]	) as file:
				__A    =							json.load(__snake_case	)
		return event["pull_request"]["url"] + "/files"



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							[]
		__A    =							{
		    "Accept": "application/vnd.github.v3+json",
		    "Authorization": "token " + os.environ["GITHUB_TOKEN"],
		}
		__A    =							requests.get(get_files_url()					,     headers=__snake_case	).json()
		for file in files:
				__A    =							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      UpperCAmelCase						( ):


		'''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 lowerCAmelCase__	: F"""{path.parent.name}/{path.name}"""					,     )
def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

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









"""simple docstring"""



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 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      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': 1_6_0_0_0,
          '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      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      lowercase				       =			self.add_kwargs_tokens_map.copy()
      kwargs.update(_lowerCamelCase			)
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      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      UpperCamelCase_ (     self			):
      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      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			floats_list((3, 1_0_0_0)			)

      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      UpperCamelCase_ (     self			):
      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      UpperCamelCase_ (     self			):
      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      UpperCamelCase_ (     self			):
      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'				,						)


