


import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
						import sqlitea

						import sqlalchemy
class        _lowercase      (			A__       ):






				'''simple docstring'''



				def __init__(       self			:int			,			lowerCAmelCase__			:Union[str, "sqlalchemy.sql.Selectable"]			,			lowerCAmelCase__			:Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]			,			lowerCAmelCase__			:Optional[Features] = None			,			lowerCAmelCase__			:str = None			,			lowerCAmelCase__			:bool = False			,			**lowerCAmelCase__			:Union[str, Any]			,			)		->			Optional[int]:
										super().__init__(features=lowerCAmelCase__			,			cache_dir=lowerCAmelCase__			,			keep_in_memory=lowerCAmelCase__			,			**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Any					=		Sql(
										    cache_dir=lowerCAmelCase__			,			features=lowerCAmelCase__			,			sql=lowerCAmelCase__			,			con=lowerCAmelCase__			,			**lowerCAmelCase__			,			)



				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		None
										__SCREAMING_SNAKE_CASE						:     int					=		None
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
										__SCREAMING_SNAKE_CASE						:     Dict					=		None

										self.builder.download_and_prepare(
										    download_config=lowerCAmelCase__			,			download_mode=lowerCAmelCase__			,			verification_mode=lowerCAmelCase__			,			base_path=lowerCAmelCase__			,			)

										# Build dataset for splits
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.builder.as_dataset(
										    split='''train'''			,			verification_mode=lowerCAmelCase__			,			in_memory=self.keep_in_memory	)
										return dataset




class        _lowercase      :






				'''simple docstring'''



				def __init__(       self			:int			,			lowerCAmelCase__			:Dataset			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]			,			lowerCAmelCase__			:Optional[int] = None			,			lowerCAmelCase__			:Optional[int] = None			,			**lowerCAmelCase__			:Union[str, Any]			,			)		->			int:
										if num_proc is not None and num_proc <= 0:
																raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		dataset
										__SCREAMING_SNAKE_CASE						:     Tuple					=		name
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		con
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		num_proc
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		to_sql_kwargs


				def 		__magic_name__(       self			:Tuple	)		->			int:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		self.to_sql_kwargs.pop('''sql'''			,			lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		self.to_sql_kwargs.pop('''con'''			,			lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     int					=		self.to_sql_kwargs.pop('''index'''			,			lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     int					=		self._write(index=lowerCAmelCase__			,			**self.to_sql_kwargs	)
										return written


				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:List[Any]	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		args
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
										__SCREAMING_SNAKE_CASE						:     List[str]					=		query_table(
										    table=self.dataset.data			,			key=slice(lowerCAmelCase__			,			offset + self.batch_size	)			,			indices=self.dataset._indices			,			)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		batch.to_pandas()
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		df.to_sql(self.name			,			self.con			,			index=lowerCAmelCase__			,			**lowerCAmelCase__	)
										return num_rows or len(lowerCAmelCase__	)



				def 		__magic_name__(       self			:str			,			lowerCAmelCase__			:str			,			**lowerCAmelCase__			:Tuple	)		->			int:
										__SCREAMING_SNAKE_CASE						:     str					=		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 SQL from Arrow format'''			,			):
																						written += self._batch_sql((offset, index, to_sql_kwargs)	)
										else:
																__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[int]					=		len(self.dataset	), self.batch_size
																with multiprocessing.Pool(self.num_proc	) as pool:
																						for num_rows in logging.tqdm(
																						    pool.imap(
																						        self._batch_sql			,			[(offset, index, to_sql_kwargs) for offset in range(0			,			lowerCAmelCase__			,			lowerCAmelCase__	)]			,			)			,			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 SQL from Arrow format'''			,			):
																												written += num_rows

										return written




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Union[str, Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     str					=		'''ylacombe/bark-small'''
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tempfile.mkdtemp()
										__SCREAMING_SNAKE_CASE						:     str					=		'''en_speaker_1'''
										__SCREAMING_SNAKE_CASE						:     Any					=		'''This is a test string'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings_path.json'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings'''


				def 		__magic_name__(       self			:List[str]			,			**lowerCAmelCase__			:Union[str, Any]	)		->			Any:
										return AutoTokenizer.from_pretrained(self.checkpoint			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			int:
										shutil.rmtree(self.tmpdirname	)


				def 		__magic_name__(       self			:Dict	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Tuple					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										processor.save_pretrained(self.tmpdirname	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		BarkProcessor.from_pretrained(self.tmpdirname	)

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


				@slow
				def 		__magic_name__(       self			:Tuple	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)
										processor.save_pretrained(
										    self.tmpdirname			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			speaker_embeddings_directory=self.speaker_embeddings_directory			,			)

										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''	)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		BarkProcessor.from_pretrained(
										    self.tmpdirname			,			self.speaker_embeddings_dict_path			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			)

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


				def 		__magic_name__(       self			:List[str]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)

										__SCREAMING_SNAKE_CASE						:     str					=		35
										__SCREAMING_SNAKE_CASE						:     str					=		2
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		8

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{
										    '''semantic_prompt''': np.ones(lowerCAmelCase__	),
										    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)	),
										    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)	),
										}

										# test providing already loaded voice_preset
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[str]					=		inputs['''history_prompt''']
										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()			,			processed_voice_preset.get(lowerCAmelCase__			,			np.array([]	)	).tolist()	)

										# test loading voice preset from npz file
										__SCREAMING_SNAKE_CASE						:     str					=		os.path.join(self.tmpdirname			,			'''file.npz'''	)
										np.savez(lowerCAmelCase__			,			**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		inputs['''history_prompt''']

										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()			,			processed_voice_preset.get(lowerCAmelCase__			,			np.array([]	)	).tolist()	)

										# test loading voice preset from the hub
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=self.voice_preset	)



				def 		__magic_name__(       self			:Tuple	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Any					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenizer(
										    self.input_string			,			padding='''max_length'''			,			max_length=256			,			add_special_tokens=lowerCAmelCase__			,			return_attention_mask=lowerCAmelCase__			,			return_token_type_ids=lowerCAmelCase__			,			)

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