






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										=      datasets.utils.logging.get_logger(__name__)


@dataclass
class 				__a				(	datasets.BuilderConfig    ):





		'''simple docstring'''



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






class 				__a				(	datasets.ArrowBasedBuilder    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Union[str, Any]					     =    TextConfig


		def 	__snake_case      (    self							):
						return datasets.DatasetInfo(features=self.config.features							)


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


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






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						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":
																		SCREAMING_SNAKE_CASE_ :						List[Any]    				= 0
																		while True:
																						SCREAMING_SNAKE_CASE_ :						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)
																						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= StringIO(UpperCamelCase__							).readlines()
																						if not self.config.keep_linebreaks:
																										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= [line.rstrip('\n'							) for line in batch]
																						SCREAMING_SNAKE_CASE_ :						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":
																		SCREAMING_SNAKE_CASE_ :						Any    				= 0
																		SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= ''
																		while True:
																						SCREAMING_SNAKE_CASE_ :						str    				= f.read(self.config.chunksize							)
																						if not new_batch:
																										break
																						batch += new_batch
																						batch += f.readline()  # finish current line
																						SCREAMING_SNAKE_CASE_ :						str    				= batch.split('\n\n'							)
																						SCREAMING_SNAKE_CASE_ :						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
																						SCREAMING_SNAKE_CASE_ :						str    				= batch[-1]
																		if batch:
																						SCREAMING_SNAKE_CASE_ :						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":
																		SCREAMING_SNAKE_CASE_ :						List[Any]    				= f.read()
																		SCREAMING_SNAKE_CASE_ :						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__)