






from typing import TYPE_CHECKING

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


A__	:			Tuple			   = {
    'configuration_blenderbot_small': [
        'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotSmallConfig',
        'BlenderbotSmallOnnxConfig',
    ],
    'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Union[str, Any]			   = ['BlenderbotSmallTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			str			   = [
							    'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BlenderbotSmallForCausalLM',
							    'BlenderbotSmallForConditionalGeneration',
							    'BlenderbotSmallModel',
							    'BlenderbotSmallPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			List[str]			   = [
							    'TFBlenderbotSmallForConditionalGeneration',
							    'TFBlenderbotSmallModel',
							    'TFBlenderbotSmallPreTrainedModel',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Union[str, Any]			   = [
							    'FlaxBlenderbotSmallForConditionalGeneration',
							    'FlaxBlenderbotSmallModel',
							    'FlaxBlenderbotSmallPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_blenderbot_small import (
							    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotSmallConfig,
							    BlenderbotSmallOnnxConfig,
							)
							from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot_small import (
														    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotSmallForCausalLM,
														    BlenderbotSmallForConditionalGeneration,
														    BlenderbotSmallModel,
														    BlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot_small import (
														    TFBlenderbotSmallForConditionalGeneration,
														    TFBlenderbotSmallModel,
														    TFBlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot_small import (
														    FlaxBlenderbotSmallForConditionalGeneration,
														    FlaxBlenderbotSmallModel,
														    FlaxBlenderbotSmallPreTrainedModel,
														)

else:
							import sys

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












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					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Dict,  lowerCamelCase						:    pyspark.sql.DataFrame,  lowerCamelCase						:    Optional[NamedSplit] = None,  lowerCamelCase						:    Optional[Features] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = "arrow",  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(
								    split=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  keep_in_memory=lowerCamelCase,  streaming=lowerCamelCase,  **lowerCamelCase,  )
								lowercase__    =			load_from_cache_file
								lowercase__    =			file_format
								lowercase__    =			Spark(
								    df=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  working_dir=lowerCamelCase,  **lowerCamelCase,  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								if self.streaming:
												return self.builder.as_streaming_dataset(split=self.split  )
								lowercase__    =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
								self.builder.download_and_prepare(
								    download_mode=lowerCamelCase,  file_format=self._file_format,  )
								return self.builder.as_dataset(split=self.split  )




