
import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 				_lowerCamelCase      (       lowerCamelCase_:	Any					,						lowerCamelCase_:	str		):



				'''simple docstring'''







				assert isinstance(lowerCamelCase_					,						lowerCamelCase_		)
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory'''					,						[False, True]		)
def 				_lowerCamelCase      (       lowerCamelCase_:	str					,						lowerCamelCase_:	str					,						lowerCamelCase_:	List[Any]		):



				'''simple docstring'''







				A  :	Dict				=					tmp_path / '''cache'''
				A  :	List[Any]				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								A  :	Dict				=					ParquetDatasetReader(lowerCamelCase_					,						cache_dir=lowerCamelCase_					,						keep_in_memory=lowerCamelCase_		).read()
				_check_parquet_dataset(lowerCamelCase_					,						lowerCamelCase_		)





@pytest.mark.parametrize(
    '''features'''					,						[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]					,						)
def 				_lowerCamelCase      (       lowerCamelCase_:	str					,						lowerCamelCase_:	Tuple					,						lowerCamelCase_:	Optional[int]		):



				'''simple docstring'''







				A  :	Tuple				=					tmp_path / '''cache'''
				A  :	int				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				A  :	str				=					features.copy() if features else default_expected_features
				A  :	Any				=					(
				    Features({feature: Value(lowerCamelCase_		) for feature, dtype in features.items()}		) if features is not None else None
				)
				A  :	Union[str, Any]				=					ParquetDatasetReader(lowerCamelCase_					,						features=lowerCamelCase_					,						cache_dir=lowerCamelCase_		).read()
				_check_parquet_dataset(lowerCamelCase_					,						lowerCamelCase_		)





@pytest.mark.parametrize('''split'''					,						[None, NamedSplit('''train'''		), '''train''', '''test''']		)
def 				_lowerCamelCase      (       lowerCamelCase_:	List[Any]					,						lowerCamelCase_:	Optional[Any]					,						lowerCamelCase_:	Tuple		):



				'''simple docstring'''







				A  :	Dict				=					tmp_path / '''cache'''
				A  :	str				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				A  :	Any				=					ParquetDatasetReader(lowerCamelCase_					,						cache_dir=lowerCamelCase_					,						split=lowerCamelCase_		).read()
				_check_parquet_dataset(lowerCamelCase_					,						lowerCamelCase_		)
				assert dataset.split == split if split else "train"





@pytest.mark.parametrize('''path_type'''					,						[str, list]		)
def 				_lowerCamelCase      (       lowerCamelCase_:	int					,						lowerCamelCase_:	int					,						lowerCamelCase_:	Optional[int]		):



				'''simple docstring'''







				if issubclass(lowerCamelCase_					,						lowerCamelCase_		):
								A  :	Union[str, Any]				=					parquet_path
				elif issubclass(lowerCamelCase_					,						lowerCamelCase_		):
								A  :	Optional[int]				=					[parquet_path]
				A  :	Any				=					tmp_path / '''cache'''
				A  :	Optional[Any]				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				A  :	Union[str, Any]				=					ParquetDatasetReader(lowerCamelCase_					,						cache_dir=lowerCamelCase_		).read()
				_check_parquet_dataset(lowerCamelCase_					,						lowerCamelCase_		)





def 				_lowerCamelCase      (       lowerCamelCase_:	Dict					,						lowerCamelCase_:	Tuple					,						lowerCamelCase_:	Any=("train",)		):



				'''simple docstring'''







				assert isinstance(lowerCamelCase_					,						lowerCamelCase_		)
				for split in splits:
								A  :	int				=					dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory'''					,						[False, True]		)
def 				_lowerCamelCase      (       lowerCamelCase_:	List[str]					,						lowerCamelCase_:	Dict					,						lowerCamelCase_:	Union[str, Any]		):



				'''simple docstring'''







				A  :	int				=					tmp_path / '''cache'''
				A  :	Any				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								A  :	Any				=					ParquetDatasetReader(
								    {'''train''': parquet_path}					,						cache_dir=lowerCamelCase_					,						keep_in_memory=lowerCamelCase_		).read()
				_check_parquet_datasetdict(lowerCamelCase_					,						lowerCamelCase_		)





@pytest.mark.parametrize(
    '''features'''					,						[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]					,						)
def 				_lowerCamelCase      (       lowerCamelCase_:	List[Any]					,						lowerCamelCase_:	str					,						lowerCamelCase_:	List[str]		):



				'''simple docstring'''







				A  :	int				=					tmp_path / '''cache'''
				A  :	Dict				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				A  :	Optional[Any]				=					features.copy() if features else default_expected_features
				A  :	Union[str, Any]				=					(
				    Features({feature: Value(lowerCamelCase_		) for feature, dtype in features.items()}		) if features is not None else None
				)
				A  :	int				=					ParquetDatasetReader({'''train''': parquet_path}					,						features=lowerCamelCase_					,						cache_dir=lowerCamelCase_		).read()
				_check_parquet_datasetdict(lowerCamelCase_					,						lowerCamelCase_		)





@pytest.mark.parametrize('''split'''					,						[None, NamedSplit('''train'''		), '''train''', '''test''']		)
def 				_lowerCamelCase      (       lowerCamelCase_:	Optional[int]					,						lowerCamelCase_:	Union[str, Any]					,						lowerCamelCase_:	Dict		):



				'''simple docstring'''







				if split:
								A  :	List[Any]				=					{split: parquet_path}
				else:
								A  :	Any				=					'''train'''
								A  :	str				=					{'''train''': parquet_path, '''test''': parquet_path}
				A  :	List[Any]				=					tmp_path / '''cache'''
				A  :	Optional[Any]				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				A  :	str				=					ParquetDatasetReader(lowerCamelCase_					,						cache_dir=lowerCamelCase_		).read()
				_check_parquet_datasetdict(lowerCamelCase_					,						lowerCamelCase_					,						splits=list(path.keys()		)		)
				assert all(dataset[split].split == split for split in path.keys()		)





def 				_lowerCamelCase      (       lowerCamelCase_:	int					,						lowerCamelCase_:	Dict		):



				'''simple docstring'''







				A  :	Tuple				=					ParquetDatasetWriter(lowerCamelCase_					,						tmp_path / '''foo.parquet'''		)
				assert writer.write() > 0
				A  :	Optional[Any]				=					pq.ParquetFile(tmp_path / '''foo.parquet'''		)
				A  :	Dict				=					pf.read()
				assert dataset.data.table == output_table





def 				_lowerCamelCase      (       lowerCamelCase_:	Tuple					,						lowerCamelCase_:	str		):



				'''simple docstring'''







				A  :	Tuple				=					str(shared_datadir / '''test_image_rgb.jpg'''		)
				A  :	Optional[int]				=					{'''image''': [image_path]}
				A  :	Tuple				=					Features({'''image''': Image()}		)
				A  :	List[str]				=					Dataset.from_dict(lowerCamelCase_					,						features=lowerCamelCase_		)
				A  :	Dict				=					ParquetDatasetWriter(lowerCamelCase_					,						tmp_path / '''foo.parquet'''		)
				assert writer.write() > 0

				A  :	Tuple				=					Dataset.from_parquet(str(tmp_path / '''foo.parquet'''		)		)
				assert dataset.features == reloaded_dataset.features

				A  :	Union[str, Any]				=					ParquetDatasetReader(str(tmp_path / '''foo.parquet'''		)					,						streaming=lowerCamelCase_		).read()
				assert dataset.features == reloaded_iterable_dataset.features






@pytest.mark.parametrize(
    '''feature, expected'''					,						[
        (Features({'''foo''': Value('''int32'''		)}		), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''		)}		), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()		)}		), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]					,						)
def 				_lowerCamelCase      (       lowerCamelCase_:	int					,						lowerCamelCase_:	Any		):



				'''simple docstring'''







				assert get_writer_batch_size(lowerCamelCase_		) == expected





'''simple docstring'''


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 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   'ylacombe/bark-small'
							__snake_case						 =   tempfile.mkdtemp()
							__snake_case						 =   'en_speaker_1'
							__snake_case						 =   'This is a test string'
							__snake_case						 =   'speaker_embeddings_path.json'
							__snake_case						 =   'speaker_embeddings'




	def   lowercase__   (       self     :  int       ,		**__lowerCAmelCase     :  str      ):
							return AutoTokenizer.from_pretrained(self.checkpoint       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]      ):
							shutil.rmtree(self.tmpdirname      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

							processor.save_pretrained(self.tmpdirname      )
							__snake_case						 =   BarkProcessor.from_pretrained(self.tmpdirname      )

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




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   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       ,		)

							__snake_case						 =   self.get_tokenizer(bos_token='(BOS)'       ,		eos_token='(EOS)'      )

							__snake_case						 =   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   lowercase__   (       self     :  str      ):
							__snake_case						 =   BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint       ,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,		)

							__snake_case						 =   3_5
							__snake_case						 =   2
							__snake_case						 =   8

							__snake_case						 =   {
							    '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
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=__lowerCAmelCase      )

							__snake_case						 =   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
							__snake_case						 =   os.path.join(self.tmpdirname       ,		'file.npz'      )
							np.savez(__lowerCAmelCase       ,		**__lowerCAmelCase      )
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=__lowerCAmelCase      )
							__snake_case						 =   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
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=self.voice_preset      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

							__snake_case						 =   processor(text=self.input_string      )

							__snake_case						 =   tokenizer(
							    self.input_string       ,		padding='max_length'       ,		max_length=2_5_6       ,		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()      )

