





import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


UpperCamelCase__ :			Any       	=			datasets.utils.logging.get_logger(__name__)







@dataclass
class 	_UpperCamelCase	(							datasets.BuilderConfig	):







					'''simple docstring'''

					lowerCamelCase  :  Optional[datasets.Features]           =							None
					lowerCamelCase  :  str           =							"utf-8"
					lowerCamelCase  :  Optional[str]           =							None
					lowerCamelCase  :  int           =							10 << 20  # 10MB
					lowerCamelCase  :  bool           =							False
					lowerCamelCase  :  str           =							"line"







class 	_UpperCamelCase	(							datasets.ArrowBasedBuilder	):







					'''simple docstring'''

					lowerCamelCase  :  int           =							TextConfig





					def 							SCREAMING_SNAKE_CASE				(    self     :	int		):






						'''simple docstring'''







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





					def 							SCREAMING_SNAKE_CASE				(    self     :	str			,       __lowercase     :	List[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(__lowercase			,       (str, list, tuple)		):
							UpperCAmelCase_		    =  data_files
							if isinstance(__lowercase			,       __lowercase		):
								UpperCAmelCase_		    =  [files]
							UpperCAmelCase_		    =  [dl_manager.iter_files(__lowercase		) 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(__lowercase			,       __lowercase		):
								UpperCAmelCase_		    =  [files]
							UpperCAmelCase_		    =  [dl_manager.iter_files(__lowercase		) for file in files]
							splits.append(datasets.SplitGenerator(name=__lowercase			,       gen_kwargs={"""files""": files}		)		)
						return splits





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]			,       __lowercase     :	pa.Table		):






						'''simple docstring'''







						if self.config.features is not None:
							UpperCAmelCase_		    =  self.config.features.arrow_schema
							if all(not require_storage_cast(__lowercase		) for feature in self.config.features.values()		):
								# cheaper cast
								UpperCAmelCase_		    =  pa_table.cast(__lowercase		)
							else:
								# more expensive cast; allows str <-> int/float or str to Audio for example
								UpperCAmelCase_		    =  table_cast(__lowercase			,       __lowercase		)
							return pa_table
						else:
							return pa_table.cast(pa.schema({"""text""": pa.string()}		)		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Any			,       __lowercase     :	Any		):






						'''simple docstring'''







						UpperCAmelCase_		    =  list(self.config.features		) if self.config.features is not None else ["""text"""]
						for file_idx, file in enumerate(itertools.chain.from_iterable(__lowercase		)		):
							# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
							with open(__lowercase			,       encoding=self.config.encoding			,       errors=self.config.errors		) as f:
								if self.config.sample_by == "line":
									UpperCAmelCase_		    =  0
									while True:
										UpperCAmelCase_		    =  f.read(self.config.chunksize		)
										if not batch:
											break
										batch += f.readline()  # finish current line
										# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
										UpperCAmelCase_		    =  StringIO(__lowercase		).readlines()
										if not self.config.keep_linebreaks:
											UpperCAmelCase_		    =  [line.rstrip("""\n"""		) for line in batch]
										UpperCAmelCase_		    =  pa.Table.from_arrays([pa.array(__lowercase		)]			,       names=__lowercase		)
										# 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 (file_idx, batch_idx), self._cast_table(__lowercase		)
										batch_idx += 1
								elif self.config.sample_by == "paragraph":
									UpperCAmelCase_		    =  0
									UpperCAmelCase_		    =  """"""
									while True:
										UpperCAmelCase_		    =  f.read(self.config.chunksize		)
										if not new_batch:
											break
										batch += new_batch
										batch += f.readline()  # finish current line
										UpperCAmelCase_		    =  batch.split("""\n\n"""		)
										UpperCAmelCase_		    =  pa.Table.from_arrays(
										    [pa.array([example for example in batch[:-1] if example]		)]			,       names=__lowercase		)
										# 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 (file_idx, batch_idx), self._cast_table(__lowercase		)
										batch_idx += 1
										UpperCAmelCase_		    =  batch[-1]
									if batch:
										UpperCAmelCase_		    =  pa.Table.from_arrays([pa.array([batch]		)]			,       names=__lowercase		)
										yield (file_idx, batch_idx), self._cast_table(__lowercase		)
								elif self.config.sample_by == "document":
									UpperCAmelCase_		    =  f.read()
									UpperCAmelCase_		    =  pa.Table.from_arrays([pa.array([text]		)]			,       names=__lowercase		)
									yield file_idx, self._cast_table(__lowercase		)









from typing import TYPE_CHECKING

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


UpperCamelCase__ :			Union[str, Any]       	=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			int       	=			[
				    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """MobileViTV2ForImageClassification""",
				    """MobileViTV2ForSemanticSegmentation""",
				    """MobileViTV2Model""",
				    """MobileViTV2PreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				UpperCamelCase__ :			List[Any]       	=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


