


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


lowerCAmelCase__    :Union[str, Any]       =				datasets.utils.logging.get_logger(__name__)

@dataclass
class 						__a (							datasets.BuilderConfig     ):
		_a      :    int           =      1_00_00
		_a      :    Optional[List[str]]           =      None
		_a      :    Optional[datasets.Features]           =      None



class 						__a (							datasets.ArrowBasedBuilder     ):
		_a      :    int           =      ParquetConfig
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									return datasets.DatasetInfo(features=self.config.features		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Optional[Any]:




									"""simple docstring"""





									if not self.config.data_files:
																raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''		)
									_UpperCAmelCase											=     dl_manager.download_and_extract(self.config.data_files		)
									if isinstance(_SCREAMING_SNAKE_CASE     ,       (str, list, tuple)		):
																_UpperCAmelCase											=     data_files
																if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																							_UpperCAmelCase											=     [files]
																# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																_UpperCAmelCase											=     [dl_manager.iter_files(_SCREAMING_SNAKE_CASE		) for file in files]
																return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,       gen_kwargs={'files': files}		)]
									_UpperCAmelCase											=     []
									for split_name, files in data_files.items():
																if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																							_UpperCAmelCase											=     [files]
																# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																_UpperCAmelCase											=     [dl_manager.iter_files(_SCREAMING_SNAKE_CASE		) for file in files]
																# Infer features is they are stoed in the arrow schema
																if self.info.features is None:
																							for file in itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE		):
																														with open(_SCREAMING_SNAKE_CASE     ,       'rb'		) as f:
																																					_UpperCAmelCase											=     datasets.Features.from_arrow_schema(pq.read_schema(_SCREAMING_SNAKE_CASE		)		)
																														break
																splits.append(datasets.SplitGenerator(name=_SCREAMING_SNAKE_CASE     ,       gen_kwargs={'files': files}		)		)
									return splits
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			pa.Table:




									"""simple docstring"""





									if self.info.features is not None:
																# more expensive cast to support nested features with keys in a different order
																# allows str <-> int/float or str to Audio for example
																_UpperCAmelCase											=     table_cast(_SCREAMING_SNAKE_CASE     ,       self.info.features.arrow_schema		)
									return pa_table






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     self.info.features.arrow_schema if self.info.features is not None else None
									if self.info.features is not None and self.config.columns is not None:
																if sorted(field.name for field in schema		) != sorted(self.config.columns		):
																							raise ValueError(
																							    f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''		)
									for file_idx, file in enumerate(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE		)		):
																with open(_SCREAMING_SNAKE_CASE     ,       'rb'		) as f:
																							_UpperCAmelCase											=     pq.ParquetFile(_SCREAMING_SNAKE_CASE		)
																							try:
																														for batch_idx, record_batch in enumerate(
																														    parquet_file.iter_batches(batch_size=self.config.batch_size     ,       columns=self.config.columns		)		):
																																					_UpperCAmelCase											=     pa.Table.from_batches([record_batch]		)
																																					# Uncomment for debugging (will print the Arrow table size and elements)
																																					# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																					# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																					yield f'''{file_idx}_{batch_idx}''', self._cast_table(_SCREAMING_SNAKE_CASE		)
																							except ValueError as e:
																														logger.error(f'''Failed to read file \'{file}\' with error {type(_SCREAMING_SNAKE_CASE		)}: {e}'''		)
																														raise




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__    :Tuple       =				{
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Dict       =				['''SpeechT5Tokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[str]       =				[
		    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SpeechT5ForSpeechToText''',
		    '''SpeechT5ForSpeechToSpeech''',
		    '''SpeechT5ForTextToSpeech''',
		    '''SpeechT5Model''',
		    '''SpeechT5PreTrainedModel''',
		    '''SpeechT5HifiGan''',
		]

if TYPE_CHECKING:
		from .configuration_speechta import (
		    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
		    SpeechTaConfig,
		    SpeechTaHifiGanConfig,
		)
		from .feature_extraction_speechta import SpeechTaFeatureExtractor
		from .processing_speechta import SpeechTaProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speechta import SpeechTaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speechta import (
				    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechTaForSpeechToSpeech,
				    SpeechTaForSpeechToText,
				    SpeechTaForTextToSpeech,
				    SpeechTaHifiGan,
				    SpeechTaModel,
				    SpeechTaPreTrainedModel,
				)

else:
		import sys

		lowerCAmelCase__    :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
