


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


snake_case_  :	int							  =							datasets.utils.logging.get_logger(__name__)

@dataclass
class   A__							(						datasets.BuilderConfig     ):
						UpperCAmelCase									=  None
						UpperCAmelCase									=  "utf-8"
						UpperCAmelCase									=  None
						UpperCAmelCase									=  10 << 20  # 10MB
						UpperCAmelCase									=  False
						UpperCAmelCase									=  "line"

class   A__							(						datasets.ArrowBasedBuilder     ):
						UpperCAmelCase									=  TextConfig




						def 							__UpperCamelCase (				self    :					Dict     )							->   int:


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					List[str]     )							->   str:


										"""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	     =dl_manager.download_and_extract(self.config.data_files     )
										if isinstance(_UpperCAmelCase ,			(str, list, tuple)     ):
														_SCREAMING_SNAKE_CASE	     =data_files
														if isinstance(_UpperCAmelCase ,			_UpperCAmelCase     ):
																		_SCREAMING_SNAKE_CASE	     =[files]
														_SCREAMING_SNAKE_CASE	     =[dl_manager.iter_files(_UpperCAmelCase     ) for file in files]
														return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,			gen_kwargs={'''files''': files}     )]
										_SCREAMING_SNAKE_CASE	     =[]
										for split_name, files in data_files.items():
														if isinstance(_UpperCAmelCase ,			_UpperCAmelCase     ):
																		_SCREAMING_SNAKE_CASE	     =[files]
														_SCREAMING_SNAKE_CASE	     =[dl_manager.iter_files(_UpperCAmelCase     ) for file in files]
														splits.append(datasets.SplitGenerator(name=_UpperCAmelCase ,			gen_kwargs={'''files''': files}     )     )
										return splits




						def 							__UpperCamelCase (				self    :					List[str] ,			_a    :					pa.Table     )							->   pa.Table:


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					str ,			_a    :					Optional[Any]     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =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	     =0
																						while True:
																										_SCREAMING_SNAKE_CASE	     =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	     =StringIO(_UpperCAmelCase     ).readlines()
																										if not self.config.keep_linebreaks:
																														_SCREAMING_SNAKE_CASE	     =[line.rstrip('''\n'''     ) for line in batch]
																										_SCREAMING_SNAKE_CASE	     =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	     =0
																						_SCREAMING_SNAKE_CASE	     =""""""
																						while True:
																										_SCREAMING_SNAKE_CASE	     =f.read(self.config.chunksize     )
																										if not new_batch:
																														break
																										batch += new_batch
																										batch += f.readline()  # finish current line
																										_SCREAMING_SNAKE_CASE	     =batch.split('''\n\n'''     )
																										_SCREAMING_SNAKE_CASE	     =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	     =batch[-1]
																						if batch:
																										_SCREAMING_SNAKE_CASE	     =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	     =f.read()
																						_SCREAMING_SNAKE_CASE	     =pa.Table.from_arrays([pa.array([text]     )] ,			names=_UpperCAmelCase     )
																						yield file_idx, self._cast_table(_UpperCAmelCase     )





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


_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     List[str]					  =  {
    "ZinengTang/tvlt-base": "https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json",
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   "tvlt"



				def __init__(					self					:						Dict						,		_UpperCAmelCase					:						Any=224						,		_UpperCAmelCase					:						Any=2048						,		_UpperCAmelCase					:						Optional[int]=128						,		_UpperCAmelCase					:						Tuple=[16, 16]						,		_UpperCAmelCase					:						Optional[int]=[16, 16]						,		_UpperCAmelCase					:						Tuple=3						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						Dict=8						,		_UpperCAmelCase					:						Any=768						,		_UpperCAmelCase					:						Dict=12						,		_UpperCAmelCase					:						Any=12						,		_UpperCAmelCase					:						Optional[Any]=3072						,		_UpperCAmelCase					:						Tuple="gelu"						,		_UpperCAmelCase					:						List[str]=0.0						,		_UpperCAmelCase					:						Any=0.0						,		_UpperCAmelCase					:						Optional[int]=0.02						,		_UpperCAmelCase					:						int=1E-6						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						Dict=False						,		_UpperCAmelCase					:						str=16						,		_UpperCAmelCase					:						List[Any]=512						,		_UpperCAmelCase					:						Optional[Any]=8						,		_UpperCAmelCase					:						Optional[Any]=2048						,		_UpperCAmelCase					:						Optional[int]=0.75						,		_UpperCAmelCase					:						List[Any]=0.15						,		_UpperCAmelCase					:						int="frame-level"						,		_UpperCAmelCase					:						Union[str, Any]=True						,		_UpperCAmelCase					:						Optional[int]=True						,		_UpperCAmelCase					:						Any="classification"						,		**_UpperCAmelCase					:						Tuple						,		)     ->			List[str]:





								'''simple docstring'''




								super().__init__(**_UpperCAmelCase					)

								if audio_mask_type not in ("frame-level", "patch_level"):
												raise ValueError(
												    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
												    f"got {audio_mask_type}"					)

								_lowerCAmelCase			:			Tuple        =					image_size
								_lowerCAmelCase			:			List[str]        =					spectrogram_length
								_lowerCAmelCase			:			str        =					frequency_length
								_lowerCAmelCase			:			Dict        =					image_patch_size
								_lowerCAmelCase			:			Tuple        =					audio_patch_size
								_lowerCAmelCase			:			str        =					num_image_channels
								_lowerCAmelCase			:			Dict        =					num_audio_channels
								_lowerCAmelCase			:			str        =					num_frames

								_lowerCAmelCase			:			Any        =					hidden_size
								_lowerCAmelCase			:			List[str]        =					num_hidden_layers
								_lowerCAmelCase			:			Union[str, Any]        =					num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					intermediate_size
								_lowerCAmelCase			:			Any        =					hidden_act
								_lowerCAmelCase			:			Any        =					hidden_dropout_prob
								_lowerCAmelCase			:			Union[str, Any]        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			str        =					initializer_range
								_lowerCAmelCase			:			Tuple        =					layer_norm_eps
								_lowerCAmelCase			:			str        =					qkv_bias
								_lowerCAmelCase			:			Tuple        =					use_mean_pooling

								_lowerCAmelCase			:			Union[str, Any]        =					decoder_num_attention_heads
								_lowerCAmelCase			:			int        =					decoder_hidden_size
								_lowerCAmelCase			:			List[str]        =					decoder_num_hidden_layers
								_lowerCAmelCase			:			Optional[int]        =					decoder_intermediate_size
								_lowerCAmelCase			:			Any        =					pixel_mask_ratio
								_lowerCAmelCase			:			int        =					audio_mask_ratio
								_lowerCAmelCase			:			Optional[int]        =					audio_mask_type

								_lowerCAmelCase			:			Dict        =					task_matching
								_lowerCAmelCase			:			Any        =					task_mae
								_lowerCAmelCase			:			int        =					loss_type

