


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 					__lowerCamelCase	(_a		):







       def __init__(	self:				int,A_:				str,A_:				Optional[int] = None,A_:				Union[str, Any] = None,A_:				Optional[int] = None,A_:				Dict = False,A_:				List[str] = False,A_:				Union[str, Any] = None,**A_:				str,):






            '''simple docstring'''




            super().__init__(
                __A,split=__A,features=__A,cache_dir=__A,keep_in_memory=__A,streaming=__A,num_proc=__A,**__A,)
            __UpperCamelCase				=	path_or_paths if isinstance(__A,__A			) else {self.split: path_or_paths}
            __UpperCamelCase				=	Csv(
                cache_dir=__A,data_files=__A,features=__A,**__A,)







       def 						snake_case_					(	self:				Any			):






            '''simple docstring'''




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

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



class 					__lowerCamelCase	:







       def __init__(	self:				Optional[int],A_:				Tuple,A_:				Optional[Any],A_:				Dict = None,A_:				Union[str, Any] = None,**A_:				Optional[int],):






            '''simple docstring'''




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

            __UpperCamelCase				=	dataset
            __UpperCamelCase				=	path_or_buf
            __UpperCamelCase				=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
            __UpperCamelCase				=	num_proc
            __UpperCamelCase				=	"utf-8"
            __UpperCamelCase				=	to_csv_kwargs







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	self.to_csv_kwargs.pop('path_or_buf',__A			)
            __UpperCamelCase				=	self.to_csv_kwargs.pop('header',__A			)
            __UpperCamelCase				=	self.to_csv_kwargs.pop('index',__A			)

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







       def 						snake_case_					(	self:				str,A_:				List[Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	args

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







       def 						snake_case_					(	self:				Any,A_:				Any,A_:				List[str],A_:				List[Any],**A_:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	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',):
                      __UpperCamelCase				=	self._batch_csv((offset, header, index, to_csv_kwargs)			)
                      written += file_obj.write(__A			)

            else:
                 __UpperCamelCase				=	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,__A,__A			)],),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(__A			)

            return written






import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin



class 					__lowerCamelCase	(_a							,     unittest.TestCase		):
       _lowercase    							=      SpeechaTextaTokenizer
       _lowercase    							=      False







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            super().setUp()

            __UpperCamelCase				=	'<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '			)
            __UpperCamelCase				=	['he re</w> 123', 'here a 1456']
            __UpperCamelCase				=	dict(zip(A_,range(len(A_			)			)			)			)

            __UpperCamelCase				=	{'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

            __UpperCamelCase				=	tempfile.mkdtemp()
            __UpperCamelCase				=	os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['vocab_file']			)
            __UpperCamelCase				=	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'			) as fp:
                 fp.write('\n'.join(A_			)			)







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	list(self.get_tokenizer().get_vocab().keys()			)

            self.assertEqual(vocab_keys[0],'<s>'			)
            self.assertEqual(vocab_keys[1],'<pad>'			)
            self.assertEqual(vocab_keys[-1],'vocab'			)
            self.assertEqual(len(A_			),14			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size,14			)







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

            # make sure @@ is correctly concatenated
            __UpperCamelCase				=	[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
            __UpperCamelCase				=	tokenizer.decode(A_			)

            self.assertTrue(output_string == 'herecouple words ofthe'			)







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 tokenizer.save_pretrained(A_			)
                 os.remove(os.path.join(A_,'merges.txt'			)			)

                 # load tokenizer without merges file should not throw an error
                 __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(A_			)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 # save tokenizer and load again
                 tokenizer.save_pretrained(A_			)
                 __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(A_			)

            self.assertIsNotNone(A_			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            if not self.test_slow_tokenizer:
                 return

            __UpperCamelCase				=	inspect.signature(self.tokenizer_class.__init__			)
            __UpperCamelCase				=	self.get_tokenizer()

            for parameter_name, parameter in signature.parameters.items():
                 if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                      self.assertIn(A_,tokenizer.init_kwargs			)


