


"""simple docstring"""







from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader




class 			UpperCamelCase_     (			UpperCamelCase__):

	"""simple docstring"""





	def __init__(			self      :    List[Any]      ,      UpperCAmelCase__      :    pyspark.sql.DataFrame      ,      UpperCAmelCase__      :    Optional[NamedSplit] = None      ,      UpperCAmelCase__      :    Optional[Features] = None      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    str = None      ,      UpperCAmelCase__      :    bool = False      ,      UpperCAmelCase__      :    str = None      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    str = "arrow"      ,      **UpperCAmelCase__      :    Dict      ,      )	->     Any:
			super().__init__(
			    split=UpperCamelCase_      ,      features=UpperCamelCase_      ,      cache_dir=UpperCamelCase_      ,      keep_in_memory=UpperCamelCase_      ,      streaming=UpperCamelCase_      ,      **UpperCamelCase_      ,      )
			__SCREAMING_SNAKE_CASE						       =    load_from_cache_file
			__SCREAMING_SNAKE_CASE						       =    file_format
			__SCREAMING_SNAKE_CASE						       =    Spark(
			    df=UpperCamelCase_      ,      features=UpperCamelCase_      ,      cache_dir=UpperCamelCase_      ,      working_dir=UpperCamelCase_      ,      **UpperCamelCase_      ,      )




	def   UpperCAmelCase_				(			self      :    int       )	->     Optional[int]:







			if self.streaming:
					return self.builder.as_streaming_dataset(split=self.split       )
			__SCREAMING_SNAKE_CASE						       =    None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
			self.builder.download_and_prepare(
			    download_mode=UpperCamelCase_      ,      file_format=self._file_format      ,      )
			return self.builder.as_dataset(split=self.split       )




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a__    :							Dict            =					TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a__    :							Any            =					TaTokenizerFast

a__    :							Tuple            =					{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							int            =					[
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							str            =					['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a__    :							str            =					_LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)
