
from typing import TYPE_CHECKING

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


UpperCAmelCase__   :					Any												=				{
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					int												=				[
					    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SwiftFormerForImageClassification',
					    'SwiftFormerModel',
					    'SwiftFormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

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






"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast



@dataclass
class 			lowerCAmelCase_		(datasets.BuilderConfig						):


				"""simple docstring"""



				__UpperCamelCase   :		Optional[datasets.Features]       =     None






class 			lowerCAmelCase_		(datasets.ArrowBasedBuilder						):


				"""simple docstring"""



				__UpperCamelCase   :		Union[str, Any]       =     PandasConfig



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											return datasets.DatasetInfo(features=self.config.features					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""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}'''					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dl_manager.download_and_extract(self.config.data_files					)
											if isinstance(SCREAMING_SNAKE_CASE__     , (str, list, tuple)					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     data_files
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [files]
																		# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																		SCREAMING_SNAKE_CASE__		: str     =     [dl_manager.iter_files(SCREAMING_SNAKE_CASE__					) for file in files]
																		return [datasets.SplitGenerator(name=datasets.Split.TRAIN     , gen_kwargs={"""files""": files}					)]
											SCREAMING_SNAKE_CASE__		: int     =     []
											for split_name, files in data_files.items():
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: List[str]     =     [files]
																		# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																		SCREAMING_SNAKE_CASE__		: Dict     =     [dl_manager.iter_files(SCREAMING_SNAKE_CASE__					) for file in files]
																		splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__     , gen_kwargs={"""files""": files}					)					)
											return splits



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	pa.Table:



											"""simple docstring"""




											if self.config.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
																		SCREAMING_SNAKE_CASE__		: Tuple     =     table_cast(SCREAMING_SNAKE_CASE__     , self.config.features.arrow_schema					)
											return pa_table



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



											"""simple docstring"""




											for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__					)					):
																		with open(SCREAMING_SNAKE_CASE__     , """rb"""					) as f:
																									SCREAMING_SNAKE_CASE__		: List[str]     =     pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE__					)					)
																									yield i, self._cast_table(SCREAMING_SNAKE_CASE__					)




