


"""simple docstring"""







import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class  __A							(			lowerCamelCase__     ):






 """simple docstring"""




 def __init__(						self					,       __A					,       __A = None					,       __A = None					,       __A = None					,       __A = False					,       __A = False					,       __A = None					,       **__A					,       )   ->	Union[str, Any]:
     super().__init__(
         _snake_case					,       split=_snake_case					,       features=_snake_case					,       cache_dir=_snake_case					,       keep_in_memory=_snake_case					,       streaming=_snake_case					,       num_proc=_snake_case					,       **_snake_case					,       )
     a											=path_or_paths if isinstance(_snake_case					,       _snake_case		) else {self.split: path_or_paths}
     a											=Csv(
         cache_dir=_snake_case					,       data_files=_snake_case					,       features=_snake_case					,       **_snake_case					,       )





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:





     if self.streaming:
         a											=self.builder.as_streaming_dataset(split=self.split		)
     # Build regular (map-style) dataset
     else:
         a											=None
         a											=None
         a											=None
         a											=None

         self.builder.download_and_prepare(
             download_config=_snake_case					,       download_mode=_snake_case					,       verification_mode=_snake_case					,       base_path=_snake_case					,       num_proc=self.num_proc					,       )
         a											=self.builder.as_dataset(
             split=self.split					,       verification_mode=_snake_case					,       in_memory=self.keep_in_memory		)
     return dataset






class  __A							:






 """simple docstring"""




 def __init__(						self					,       __A					,       __A					,       __A = None					,       __A = None					,       **__A					,       )   ->	List[Any]:





     if num_proc is not None and num_proc <= 0:
         raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''		)

     a											=dataset
     a											=path_or_buf
     a											=batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     a											=num_proc
     a											='''utf-8'''
     a											=to_csv_kwargs


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=self.to_csv_kwargs.pop('''path_or_buf'''					,       _snake_case		)
     a											=self.to_csv_kwargs.pop('''header'''					,       _snake_case		)
     a											=self.to_csv_kwargs.pop('''index'''					,       _snake_case		)

     if isinstance(self.path_or_buf					,       (str, bytes, os.PathLike)		):
         with open(self.path_or_buf					,       '''wb+'''		) as buffer:
             a											=self._write(file_obj=_snake_case					,       header=_snake_case					,       index=_snake_case					,       **self.to_csv_kwargs		)
     else:
         a											=self._write(file_obj=self.path_or_buf					,       header=_snake_case					,       index=_snake_case					,       **self.to_csv_kwargs		)
     return written


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a						, a						, a						, a											=args

     a											=query_table(
         table=self.dataset.data					,       key=slice(_snake_case					,       offset + self.batch_size		)					,       indices=self.dataset._indices					,       )
     a											=batch.to_pandas().to_csv(
         path_or_buf=_snake_case					,       header=header if (offset == 0) else False					,       index=_snake_case					,       **_snake_case		)
     return csv_str.encode(self.encoding		)





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A					,       __A					,       **__A		)   ->	Optional[Any]:
     a											=0

     if self.num_proc is None or self.num_proc == 1:
         for offset in logging.tqdm(
             range(0					,       len(self.dataset		)					,       self.batch_size		)					,       unit='''ba'''					,       disable=not logging.is_progress_bar_enabled()					,       desc='''Creating CSV from Arrow format'''					,       ):
             a											=self._batch_csv((offset, header, index, to_csv_kwargs)		)
             written += file_obj.write(_snake_case		)

     else:
         a						, a											=len(self.dataset		), self.batch_size
         with multiprocessing.Pool(self.num_proc		) as pool:
             for csv_str in logging.tqdm(
                 pool.imap(
                     self._batch_csv					,       [(offset, header, index, to_csv_kwargs) for offset in range(0					,       _snake_case					,       _snake_case		)]					,       )					,       total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size					,       unit='''ba'''					,       disable=not logging.is_progress_bar_enabled()					,       desc='''Creating CSV from Arrow format'''					,       ):
                 written += file_obj.write(_snake_case		)

     return written




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


A__       =			get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")





@require_torch
class    __lowerCAmelCase					(	unittest.TestCase			):




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       tempfile.mkdtemp()

									_lowerCAmelCase         =       SpeechTaTokenizer(_snake_case						)
									tokenizer.save_pretrained(self.tmpdirname						)

									_lowerCAmelCase         =       {
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 16000,
									    """do_normalize""": False,
									    """num_mel_bins""": 80,
									    """hop_length""": 16,
									    """win_length""": 64,
									    """win_function""": """hann_window""",
									    """fmin""": 80,
									    """fmax""": 7600,
									    """mel_floor""": 1e-10,
									    """reduction_factor""": 2,
									    """return_attention_mask""": True,
									}

									_lowerCAmelCase         =       os.path.join(self.tmpdirname		,							_snake_case						)
									with open(self.feature_extraction_file		,							"""w"""		,							encoding="""utf-8"""						) as fp:
														fp.write(json.dumps(_snake_case						) + """\n"""						)




				def 				snake_case  (				self		,							**_snake_case						):



									"""simple docstring"""





									return SpeechTaTokenizer.from_pretrained(self.tmpdirname		,							**_snake_case						)




				def 				snake_case  (				self		,							**_snake_case						):



									"""simple docstring"""





									return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname		,							**_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									shutil.rmtree(self.tmpdirname						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_tokenizer()
									_lowerCAmelCase         =       self.get_feature_extractor()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									processor.save_pretrained(self.tmpdirname						)
									_lowerCAmelCase         =       SpeechTaProcessor.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 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=self.get_tokenizer()		,							feature_extractor=self.get_feature_extractor()						)
									processor.save_pretrained(self.tmpdirname						)

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

									_lowerCAmelCase         =       SpeechTaProcessor.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 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       floats_list((3, 1000)						)

									_lowerCAmelCase         =       feature_extractor(audio=_snake_case		,							return_tensors="""np"""						)
									_lowerCAmelCase         =       processor(audio=_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 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       floats_list((3, 1000)						)

									_lowerCAmelCase         =       feature_extractor(audio_target=_snake_case		,							return_tensors="""np"""						)
									_lowerCAmelCase         =       processor(audio_target=_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 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       """This is a test string"""

									_lowerCAmelCase         =       processor(text=_snake_case						)
									_lowerCAmelCase         =       tokenizer(_snake_case						)

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       """This is a test string"""

									_lowerCAmelCase         =       processor(text_target=_snake_case						)
									_lowerCAmelCase         =       tokenizer(_snake_case						)

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

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

									_lowerCAmelCase         =       processor.batch_decode(_snake_case						)
									_lowerCAmelCase         =       tokenizer.batch_decode(_snake_case						)

									self.assertListEqual(_snake_case		,							_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(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"""		,							)



