



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__  =			datasets.utils.logging.get_logger(__name__)







@dataclass
class      __lowerCAmelCase       (     datasets.BuilderConfig		):
			UpperCamelCase     =	None
			UpperCamelCase     =	"utf-8"
			UpperCamelCase     =	None
			UpperCamelCase     =	1_0 << 2_0  # 10MB
			UpperCamelCase     =	False
			UpperCamelCase     =	"line"







class      __lowerCAmelCase       (     datasets.ArrowBasedBuilder		):
			UpperCamelCase     =	TextConfig



			def        _lowerCamelCase			(		self  :    Dict)  ->       List[Any]:



						"""simple docstring"""






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



			def        _lowerCamelCase			(		self  :    str					,						A  :    Dict)  ->       Tuple:



						"""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(A					,						(str, list, tuple)):
									_UpperCAmelCase									=  data_files
									if isinstance(A					,						A):
												_UpperCAmelCase									=  [files]
									_UpperCAmelCase									=  [dl_manager.iter_files(A) 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(A					,						A):
												_UpperCAmelCase									=  [files]
									_UpperCAmelCase									=  [dl_manager.iter_files(A) for file in files]
									splits.append(datasets.SplitGenerator(name=A					,						gen_kwargs={'files': files}))
						return splits



			def        _lowerCamelCase			(		self  :    Optional[int]					,						A  :    pa.Table)  ->       pa.Table:



						"""simple docstring"""






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



			def        _lowerCamelCase			(		self  :    int					,						A  :    List[str])  ->       Tuple:



						"""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(A)):
									# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
									with open(A					,						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(A).readlines()
																		if not self.config.keep_linebreaks:
																					_UpperCAmelCase									=  [line.rstrip('\n') for line in batch]
																		_UpperCAmelCase									=  pa.Table.from_arrays([pa.array(A)]					,						names=A)
																		# 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(A)
																		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=A)
																		# 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(A)
																		batch_idx += 1
																		_UpperCAmelCase									=  batch[-1]
															if batch:
																		_UpperCAmelCase									=  pa.Table.from_arrays([pa.array([batch])]					,						names=A)
																		yield (file_idx, batch_idx), self._cast_table(A)
												elif self.config.sample_by == "document":
															_UpperCAmelCase									=  f.read()
															_UpperCAmelCase									=  pa.Table.from_arrays([pa.array([text])]					,						names=A)
															yield file_idx, self._cast_table(A)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__  =			logging.get_logger(__name__)

UpperCAmelCase__  =			{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}







class      __lowerCAmelCase       (     A		):
			UpperCamelCase     =	'''vilt'''



			def __init__(		self  :    List[Any]					,						A  :    Dict=3_05_22					,						A  :    Union[str, Any]=2					,						A  :    Tuple=2					,						A  :    List[Any]=40					,						A  :    Optional[int]=7_68					,						A  :    Any=12					,						A  :    Optional[int]=12					,						A  :    Optional[Any]=30_72					,						A  :    Optional[Any]="gelu"					,						A  :    str=0.0					,						A  :    Any=0.0					,						A  :    Union[str, Any]=0.0_2					,						A  :    Union[str, Any]=1E-12					,						A  :    Optional[Any]=3_84					,						A  :    Dict=32					,						A  :    int=3					,						A  :    int=True					,						A  :    Union[str, Any]=-1					,						A  :    int=False					,						A  :    int=-1					,						**A  :    Tuple					,						)  ->       Dict:



						"""simple docstring"""






						super().__init__(tie_word_embeddings=A					,						**A)

						_UpperCAmelCase									=  vocab_size
						_UpperCAmelCase									=  type_vocab_size
						_UpperCAmelCase									=  modality_type_vocab_size
						_UpperCAmelCase									=  max_position_embeddings

						_UpperCAmelCase									=  hidden_size
						_UpperCAmelCase									=  num_hidden_layers
						_UpperCAmelCase									=  num_attention_heads
						_UpperCAmelCase									=  intermediate_size
						_UpperCAmelCase									=  hidden_act
						_UpperCAmelCase									=  hidden_dropout_prob
						_UpperCAmelCase									=  attention_probs_dropout_prob
						_UpperCAmelCase									=  initializer_range
						_UpperCAmelCase									=  layer_norm_eps

						_UpperCAmelCase									=  image_size
						_UpperCAmelCase									=  patch_size
						_UpperCAmelCase									=  num_channels
						_UpperCAmelCase									=  qkv_bias
						_UpperCAmelCase									=  max_image_length
						_UpperCAmelCase									=  num_images
