



'''simple docstring'''



from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class 	lowerCamelCase_							(					snake_case_			):







						def __init__(  self   :      Tuple				,    lowerCAmelCase__   :      NestedDataStructureLike[PathLike]				,    lowerCAmelCase__   :      Optional[NamedSplit] = None				,    lowerCAmelCase__   :      Optional[Features] = None				,    lowerCAmelCase__   :      str = None				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      Optional[int] = None				,    **lowerCAmelCase__   :      List[str]				,    ):

													"""simple docstring"""
													super().__init__(
													    lowerCAmelCase__				,    split=lowerCAmelCase__				,    features=lowerCAmelCase__				,    cache_dir=lowerCAmelCase__				,    keep_in_memory=lowerCAmelCase__				,    streaming=lowerCAmelCase__				,    num_proc=lowerCAmelCase__				,    **lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      Tuple     					=			path_or_paths if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				) else {self.split: path_or_paths}
													SCREAMING_SNAKE_CASE					:      Tuple     					=			Text(
													    cache_dir=lowerCAmelCase__				,    data_files=lowerCAmelCase__				,    features=lowerCAmelCase__				,    **lowerCAmelCase__				,    )







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													# Build iterable dataset
													if self.streaming:
																				SCREAMING_SNAKE_CASE					:      Any     					=			self.builder.as_streaming_dataset(split=self.split				)
													# Build regular (map-style) dataset
													else:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			None
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			None
																				SCREAMING_SNAKE_CASE					:      str     					=			None
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			None

																				self.builder.download_and_prepare(
																				    download_config=lowerCAmelCase__				,    download_mode=lowerCAmelCase__				,    verification_mode=lowerCAmelCase__				,    base_path=lowerCAmelCase__				,    num_proc=self.num_proc				,    )
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			self.builder.as_dataset(
																				    split=self.split				,    verification_mode=lowerCAmelCase__				,    in_memory=self.keep_in_memory				)
													return dataset









'''simple docstring'''



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    ImageTextPipelineOutput,
							    UniDiffuserPipeline,
							)
else:
							from .modeling_text_decoder import UniDiffuserTextDecoder
							from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
							from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline




