
"""simple docstring"""




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


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







@dataclass
class 	_lowercase    (							datasets.BuilderConfig					):


     '''simple docstring'''





     _A		    =				None
     _A		    =				"utf-8"
     _A		    =				None
     _A		    =				10 << 20  # 10MB
     _A		    =				False
     _A		    =				"line"







class 	_lowercase    (							datasets.ArrowBasedBuilder					):


     '''simple docstring'''





     _A		    =				TextConfig



     def 			lowerCAmelCase__							(   self	)->       str:
          return datasets.DatasetInfo(features=self.config.features	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       int:
          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__				:			int							=    dl_manager.download_and_extract(self.config.data_files	)
          if isinstance(UpperCamelCase__			,      (str, list, tuple)	):
               UpperCAmelCase__				:			Union[str, Any]							=    data_files
               if isinstance(UpperCamelCase__			,      UpperCamelCase__	):
                    UpperCAmelCase__				:			Dict							=    [files]
               UpperCAmelCase__				:			Union[str, Any]							=    [dl_manager.iter_files(UpperCamelCase__	) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,      gen_kwargs={"files": files}	)]
          UpperCAmelCase__				:			Optional[int]							=    []
          for split_name, files in data_files.items():
               if isinstance(UpperCamelCase__			,      UpperCamelCase__	):
                    UpperCAmelCase__				:			List[str]							=    [files]
               UpperCAmelCase__				:			List[Any]							=    [dl_manager.iter_files(UpperCamelCase__	) for file in files]
               splits.append(datasets.SplitGenerator(name=UpperCamelCase__			,      gen_kwargs={"files": files}	)	)
          return splits



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       Dict:
          if self.config.features is not None:
               UpperCAmelCase__				:			int							=    self.config.features.arrow_schema
               if all(not require_storage_cast(UpperCamelCase__	) for feature in self.config.features.values()	):
                    # cheaper cast
                    UpperCAmelCase__				:			Union[str, Any]							=    pa_table.cast(UpperCamelCase__	)
               else:
                    # more expensive cast; allows str <-> int/float or str to Audio for example
                    UpperCAmelCase__				:			Tuple							=    table_cast(UpperCamelCase__			,      UpperCamelCase__	)
               return pa_table
          else:
               return pa_table.cast(pa.schema({"text": pa.string()}	)	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       Optional[Any]:
          UpperCAmelCase__				:			List[Any]							=    list(self.config.features	) if self.config.features is not None else ['text']
          for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__	)	):
               # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
               with open(UpperCamelCase__			,      encoding=self.config.encoding			,      errors=self.config.errors	) as f:
                    if self.config.sample_by == "line":
                         UpperCAmelCase__				:			List[Any]							=    0
                         while True:
                              UpperCAmelCase__				:			Any							=    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__				:			Optional[int]							=    StringIO(UpperCamelCase__	).readlines()
                              if not self.config.keep_linebreaks:
                                   UpperCAmelCase__				:			Union[str, Any]							=    [line.rstrip("\n"	) for line in batch]
                              UpperCAmelCase__				:			Optional[int]							=    pa.Table.from_arrays([pa.array(UpperCamelCase__	)]			,      names=UpperCamelCase__	)
                              # 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(UpperCamelCase__	)
                              batch_idx += 1
                    elif self.config.sample_by == "paragraph":
                         UpperCAmelCase__				:			Any							=    0
                         UpperCAmelCase__				:			Union[str, Any]							=    ''
                         while True:
                              UpperCAmelCase__				:			str							=    f.read(self.config.chunksize	)
                              if not new_batch:
                                   break
                              batch += new_batch
                              batch += f.readline()  # finish current line
                              UpperCAmelCase__				:			str							=    batch.split("\n\n"	)
                              UpperCAmelCase__				:			Optional[Any]							=    pa.Table.from_arrays(
                                  [pa.array([example for example in batch[:-1] if example]	)]			,      names=UpperCamelCase__	)
                              # 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(UpperCamelCase__	)
                              batch_idx += 1
                              UpperCAmelCase__				:			str							=    batch[-1]
                         if batch:
                              UpperCAmelCase__				:			Optional[Any]							=    pa.Table.from_arrays([pa.array([batch]	)]			,      names=UpperCamelCase__	)
                              yield (file_idx, batch_idx), self._cast_table(UpperCamelCase__	)
                    elif self.config.sample_by == "document":
                         UpperCAmelCase__				:			List[Any]							=    f.read()
                         UpperCAmelCase__				:			Tuple							=    pa.Table.from_arrays([pa.array([text]	)]			,      names=UpperCamelCase__	)
                         yield file_idx, self._cast_table(UpperCamelCase__	)











from typing import TYPE_CHECKING

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


A										=      {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      ['DetrFeatureExtractor']
				A										=      ['DetrImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      [
				    'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DetrForObjectDetection',
				    'DetrForSegmentation',
				    'DetrModel',
				    'DetrPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_detr import DetrFeatureExtractor
								from .image_processing_detr import DetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_detr import (
								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetrForObjectDetection,
								    DetrForSegmentation,
								    DetrModel,
								    DetrPreTrainedModel,
								)

else:
				import sys

				A										=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)