



import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


UpperCAmelCase				:							int								=get_logger(__name__)



def 				_lowerCAmelCase      (_lowerCAmelCase):
     return TestCommand(
         args.dataset				,		args.name				,		args.cache_dir				,		args.data_dir				,		args.all_configs				,		args.save_info or args.save_infos				,		args.ignore_verifications				,		args.force_redownload				,		args.clear_cache				,		)






class  _lowercase				(A__ ):
    '''simple docstring'''







    lowercase__				  =  False  # to tell pytest it's not a test class

    @staticmethod
    def  _lowerCamelCase   (	snake_case__      ):

         '''simple docstring'''


         UpperCamelCase_											=				parser.add_parser("test"       ,	help="Test dataset implementation."      )
         test_parser.add_argument("--name"       ,	type=_A       ,	default=_A       ,	help="Dataset processing name"      )
         test_parser.add_argument(
             "--cache_dir"       ,	type=_A       ,	default=_A       ,	help="Cache directory where the datasets are stored."       ,	)
         test_parser.add_argument(
             "--data_dir"       ,	type=_A       ,	default=_A       ,	help="Can be used to specify a manual directory to get the files from."       ,	)
         test_parser.add_argument("--all_configs"       ,	action="store_true"       ,	help="Test all dataset configurations"      )
         test_parser.add_argument(
             "--save_info"       ,	action="store_true"       ,	help="Save the dataset infos in the dataset card (README.md)"      )
         test_parser.add_argument(
             "--ignore_verifications"       ,	action="store_true"       ,	help="Run the test without checksums and splits checks."       ,	)
         test_parser.add_argument("--force_redownload"       ,	action="store_true"       ,	help="Force dataset redownload"      )
         test_parser.add_argument(
             "--clear_cache"       ,	action="store_true"       ,	help="Remove downloaded files and cached datasets after each config test"       ,	)
         # aliases
         test_parser.add_argument("--save_infos"       ,	action="store_true"       ,	help="alias to save_info"      )
         test_parser.add_argument("dataset"       ,	type=_A       ,	help="Name of the dataset to download"      )
         test_parser.set_defaults(func=_test_command_factory      )

    def __init__(	self       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	):

         '''simple docstring'''


         UpperCamelCase_											=				dataset
         UpperCamelCase_											=				name
         UpperCamelCase_											=				cache_dir
         UpperCamelCase_											=				data_dir
         UpperCamelCase_											=				all_configs
         UpperCamelCase_											=				save_infos
         UpperCamelCase_											=				ignore_verifications
         UpperCamelCase_											=				force_redownload
         UpperCamelCase_											=				clear_cache
         if clear_cache and not cache_dir:
              print(
                  "When --clear_cache is used, specifying a cache directory is mandatory.\n"
                  "The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"
                  "Please provide a --cache_dir that will be used to test the dataset script."      )
              exit(1      )
         if save_infos:
              UpperCamelCase_											=				True

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         fl_logger().setLevel(_A      )
         if self._name is not None and self._all_configs:
              print("Both parameters `config` and `all_configs` can't be used at once."      )
              exit(1      )
         UpperCamelCase_											=				self._dataset, self._name
         UpperCamelCase_											=				dataset_module_factory(_A      )
         UpperCamelCase_											=				import_main_class(module.module_path      )
         UpperCamelCase_											=				len(builder_cls.BUILDER_CONFIGS      ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

         def get_builders() -> Generator[DatasetBuilder, None, None]:
              if self._all_configs and builder_cls.BUILDER_CONFIGS:
                   for i, config in enumerate(builder_cls.BUILDER_CONFIGS      ):
                        if "config_name" in module.builder_kwargs:
                             yield builder_cls(
                                 cache_dir=self._cache_dir       ,	data_dir=self._data_dir       ,	**module.builder_kwargs       ,	)
                        else:
                             yield builder_cls(
                                 config_name=config.name       ,	cache_dir=self._cache_dir       ,	data_dir=self._data_dir       ,	**module.builder_kwargs       ,	)
              else:
                   if "config_name" in module.builder_kwargs:
                        yield builder_cls(cache_dir=self._cache_dir       ,	data_dir=self._data_dir       ,	**module.builder_kwargs      )
                   else:
                        yield builder_cls(
                            config_name=_A       ,	cache_dir=self._cache_dir       ,	data_dir=self._data_dir       ,	**module.builder_kwargs       ,	)

         for j, builder in enumerate(get_builders()      ):
              print(F"""Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})"""      )
              UpperCamelCase_											=				os.path.exists(
                  os.path.join(builder.get_imported_module_dir()       ,	datasets.config.DATASETDICT_INFOS_FILENAME      )      )  # record checksums only if we need to update a (deprecated) dataset_infos.json
              builder.download_and_prepare(
                  download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                  if not self._force_redownload
                  else DownloadMode.FORCE_REDOWNLOAD       ,	verification_mode=VerificationMode.NO_CHECKS
                  if self._ignore_verifications
                  else VerificationMode.ALL_CHECKS       ,	try_from_hf_gcs=_A       ,	)
              builder.as_dataset()
              if self._save_infos:
                   builder._save_infos()

              # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
              # The dataset_infos are saved in the YAML part of the README.md

              # Let's move it to the original directory of the dataset script, to allow the user to
              # upload them on S3 at the same time afterwards.
              if self._save_infos:
                   UpperCamelCase_											=				os.path.join(builder_cls.get_imported_module_dir()       ,	"README.md"      )
                   UpperCamelCase_											=				Path(_A      ).name + ".py"
                   UpperCamelCase_											=				os.path.join(_A       ,	_A      )
                   if os.path.isfile(_A      ):
                        UpperCamelCase_											=				os.path.dirname(_A      )
                   elif os.path.isfile(_A      ):
                        UpperCamelCase_											=				path
                   elif os.path.isdir(_A      ):  # for local directories containing only data files
                        UpperCamelCase_											=				path
                   else:  # in case of a remote dataset
                        UpperCamelCase_											=				None
                        print(F"""Dataset card saved at {dataset_readme_path}"""      )

                   # Move dataset_info back to the user
                   if dataset_dir is not None:
                        UpperCamelCase_											=				os.path.join(_A       ,	"README.md"      )
                        copyfile(_A       ,	_A      )
                        print(F"""Dataset card saved at {user_dataset_readme_path}"""      )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
              if self._clear_cache:
                   if os.path.isdir(builder._cache_dir      ):
                        logger.warning(F"""Clearing cache at {builder._cache_dir}"""      )
                        rmtree(builder._cache_dir      )
                   UpperCamelCase_											=				os.path.join(self._cache_dir       ,	datasets.config.DOWNLOADED_DATASETS_DIR      )
                   if os.path.isdir(_A      ):
                        logger.warning(F"""Clearing cache at {download_dir}"""      )
                        rmtree(_A      )

         print("Test successful."      )





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  _lowercase				(unittest.TestCase ):
				'''simple docstring'''







				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				"<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(" "      )
									UpperCamelCase_											=				dict(zip(snake_case__       ,	range(len(snake_case__      )      )      )      )

									UpperCamelCase_											=				{
									    "pad_token": "<pad>",
									    "unk_token": "<unk>",
									    "bos_token": "<s>",
									    "eos_token": "</s>",
									}
									UpperCamelCase_											=				{
									    "feature_size": 1,
									    "padding_value": 0.0,
									    "sampling_rate": 1_6000,
									    "return_attention_mask": False,
									    "do_normalize": True,
									}

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

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

				def  _lowerCamelCase   (	self       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.add_kwargs_tokens_map.copy()
									kwargs.update(snake_case__      )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,	**snake_case__      )

				def  _lowerCamelCase   (	self       ,	**snake_case__      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									shutil.rmtree(self.tmpdirname      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_tokenizer()
									UpperCamelCase_											=				self.get_feature_extractor()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

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

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

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

									UpperCamelCase_											=				self.get_tokenizer(bos_token="(BOS)"       ,	eos_token="(EOS)"      )
									UpperCamelCase_											=				self.get_feature_extractor(do_normalize=snake_case__       ,	padding_value=1.0      )

									UpperCamelCase_											=				WavaVecaProcessor.from_pretrained(
									    self.tmpdirname       ,	bos_token="(BOS)"       ,	eos_token="(EOS)"       ,	do_normalize=snake_case__       ,	padding_value=1.0      )

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

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

									UpperCamelCase_											=				floats_list((3, 1000)      )

									UpperCamelCase_											=				feature_extractor(snake_case__       ,	return_tensors="np"      )
									UpperCamelCase_											=				processor(snake_case__       ,	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  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

									UpperCamelCase_											=				"This is a test string"

									UpperCamelCase_											=				processor(text=snake_case__      )

									UpperCamelCase_											=				tokenizer(snake_case__      )

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

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

									UpperCamelCase_											=				processor.batch_decode(snake_case__      )
									UpperCamelCase_											=				tokenizer.batch_decode(snake_case__      )

									self.assertListEqual(snake_case__       ,	snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

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