




import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
class 				__snake_case     (_a						):



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						Dict						,		_UpperCAmelCase					:						int=False					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					(
								    filename_skip_take["""filename"""],
								    filename_skip_take["""skip"""] if """skip""" in filename_skip_take else None,
								    filename_skip_take["""take"""] if """take""" in filename_skip_take else None,
								)
								open(os.path.join(_UpperCAmelCase					)						,		"""wb"""					).close()
								_lowerCAmelCase			:			int        =					pa.Table.from_pydict({"""filename""": [Path(_UpperCAmelCase					).name] * 100}					)
								if take == -1:
												_lowerCAmelCase			:			Tuple        =					len(_UpperCAmelCase					) - skip
								if skip is not None and take is not None:
												_lowerCAmelCase			:			int        =					pa_table.slice(_UpperCAmelCase						,		_UpperCAmelCase					)
								return pa_table






class 				__snake_case     (_a						):



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					"""my_name"""
								_lowerCAmelCase			:			Optional[int]        =					SplitInfo(name="""train"""						,		num_examples=100					)
								_lowerCAmelCase			:			Optional[Any]        =					SplitInfo(name="""test"""						,		num_examples=100					)
								_lowerCAmelCase			:			Any        =					[train_info, test_info]
								_lowerCAmelCase			:			Union[str, Any]        =					SplitDict()
								split_dict.add(_UpperCAmelCase					)
								split_dict.add(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					DatasetInfo(splits=_UpperCAmelCase					)

								with tempfile.TemporaryDirectory() as tmp_dir:
												_lowerCAmelCase			:			Optional[Any]        =					ReaderTest(_UpperCAmelCase						,		_UpperCAmelCase					)

												_lowerCAmelCase			:			Union[str, Any]        =					"""test[:33%]"""
												_lowerCAmelCase			:			Dict        =					Dataset(**reader.read(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					)					)
												self.assertEqual(dset["""filename"""][0]						,		f"{name}-test"					)
												self.assertEqual(dset.num_rows						,		33					)
												self.assertEqual(dset.num_columns						,		1					)

												_lowerCAmelCase			:			Any        =					["""train""", """test[:33%]"""]
												_lowerCAmelCase			:			Any        =					[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""					)]
												for instructions in [instructionsa, instructionsa]:
																_lowerCAmelCase			:			Optional[Any]        =					[reader.read(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					) for instr in instructions]
																_lowerCAmelCase    ,			_lowerCAmelCase			:			Optional[int]        =					(Dataset(**_UpperCAmelCase					) for dataset_kwargs in datasets_kwargs)
																self.assertEqual(train_dset["""filename"""][0]						,		f"{name}-train"					)
																self.assertEqual(train_dset.num_rows						,		100					)
																self.assertEqual(train_dset.num_columns						,		1					)
																self.assertIsInstance(train_dset.split						,		_UpperCAmelCase					)
																self.assertEqual(str(train_dset.split					)						,		"""train"""					)
																self.assertEqual(test_dset["""filename"""][0]						,		f"{name}-test"					)
																self.assertEqual(test_dset.num_rows						,		33					)
																self.assertEqual(test_dset.num_columns						,		1					)
																self.assertIsInstance(test_dset.split						,		_UpperCAmelCase					)
																self.assertEqual(str(test_dset.split					)						,		"""test[:33%]"""					)
																del train_dset, test_dset



				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					"""my_name"""
								_lowerCAmelCase			:			Dict        =					SplitInfo(name="""train"""						,		num_examples=1000						,		shard_lengths=[100] * 10					)
								_lowerCAmelCase			:			Tuple        =					[train_info]
								_lowerCAmelCase			:			Optional[Any]        =					SplitDict()
								split_dict.add(_UpperCAmelCase					)
								_lowerCAmelCase			:			str        =					DatasetInfo(splits=_UpperCAmelCase					)

								with tempfile.TemporaryDirectory() as tmp_dir:
												_lowerCAmelCase			:			int        =					ReaderTest(_UpperCAmelCase						,		_UpperCAmelCase					)

												_lowerCAmelCase			:			str        =					"""train[:33%]"""
												_lowerCAmelCase			:			str        =					Dataset(**reader.read(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					)					)
												self.assertEqual(dset["""filename"""][0]						,		f"{name}-train-00000-of-00010"					)
												self.assertEqual(dset["""filename"""][-1]						,		f"{name}-train-00003-of-00010"					)
												self.assertEqual(dset.num_rows						,		330					)
												self.assertEqual(dset.num_columns						,		1					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					SplitInfo(name="""train"""						,		num_examples=100					)
								_lowerCAmelCase			:			Tuple        =					SplitInfo(name="""test"""						,		num_examples=100					)
								_lowerCAmelCase			:			Dict        =					SplitDict()
								split_dict.add(_UpperCAmelCase					)
								split_dict.add(_UpperCAmelCase					)
								_lowerCAmelCase			:			str        =					DatasetInfo(splits=_UpperCAmelCase					)

								with tempfile.TemporaryDirectory() as tmp_dir:
												_lowerCAmelCase			:			int        =					ReaderTest(_UpperCAmelCase						,		_UpperCAmelCase					)

												_lowerCAmelCase			:			List[str]        =					[
												    {"""filename""": os.path.join(_UpperCAmelCase						,		"""train"""					)},
												    {"""filename""": os.path.join(_UpperCAmelCase						,		"""test"""					), """skip""": 10, """take""": 10},
												]
												_lowerCAmelCase			:			int        =					Dataset(**reader.read_files(_UpperCAmelCase						,		original_instructions="""train+test[10:20]"""					)					)
												self.assertEqual(dset.num_rows						,		110					)
												self.assertEqual(dset.num_columns						,		1					)
												del dset






@pytest.mark.parametrize("""in_memory"""					,			[False, True]				)
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[str]					,			UpperCamelCase_       :       Union[str, Any]				):
				'''simple docstring'''
				_lowerCAmelCase			:			str        =					arrow_file
				with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
								_lowerCAmelCase			:			Dict        =					ArrowReader.read_table(UpperCamelCase_					,			in_memory=UpperCamelCase_				)
				assert table.shape == dataset.data.shape
				assert set(table.column_names				) == set(dataset.data.column_names				)
				assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict




@pytest.mark.parametrize("""in_memory"""					,			[False, True]				)
def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       int					,			UpperCamelCase_       :       Optional[int]				):
				'''simple docstring'''
				_lowerCAmelCase			:			Union[str, Any]        =					arrow_file
				_lowerCAmelCase			:			Any        =					ArrowReader(""""""					,			UpperCamelCase_				)
				with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
								_lowerCAmelCase			:			Optional[Any]        =					reader.read_files([{"""filename""": filename}]					,			in_memory=UpperCamelCase_				)
				assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
				_lowerCAmelCase			:			Union[str, Any]        =					dataset_kwargs["""arrow_table"""]
				assert table.shape == dataset.data.shape
				assert set(table.column_names				) == set(dataset.data.column_names				)
				assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict




def 							_UpperCAmelCase     ():
				'''simple docstring'''
				assert ReadInstruction("""train"""					,			to=10					,			unit="""abs"""				).to_spec() == "train[:10]"
				assert ReadInstruction("""train"""					,			from_=-80					,			to=10					,			unit="""%"""				).to_spec() == "train[-80%:10%]"

				_lowerCAmelCase			:			int        =					"""train+test"""
				assert ReadInstruction.from_spec(UpperCamelCase_				).to_spec() == spec_train_test

				_lowerCAmelCase			:			Any        =					"""train[2:10]"""
				assert ReadInstruction.from_spec(UpperCamelCase_				).to_spec() == spec_train_abs

				_lowerCAmelCase			:			Optional[Any]        =					"""train[15%:-20%]"""
				assert ReadInstruction.from_spec(UpperCamelCase_				).to_spec() == spec_train_pct

				_lowerCAmelCase			:			Optional[int]        =					"""train[:10%](closest)"""
				assert ReadInstruction.from_spec(UpperCamelCase_				).to_spec() == "train[:10%]"

				_lowerCAmelCase			:			Any        =					"""train[:10%](pct1_dropremainder)"""
				assert ReadInstruction.from_spec(UpperCamelCase_				).to_spec() == spec_train_pct_rounding

				_lowerCAmelCase			:			List[str]        =					"""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
				assert ReadInstruction.from_spec(UpperCamelCase_				).to_spec() == spec_train_test_pct_rounding




def 							_UpperCAmelCase     ():
				'''simple docstring'''
				_lowerCAmelCase			:			Dict        =					"""dummy"""
				_lowerCAmelCase			:			Optional[Any]        =					[SplitInfo(name="""train"""					,			num_examples=100				)]
				_lowerCAmelCase			:			Tuple        =					"""train[:33%]"""
				_lowerCAmelCase			:			Union[str, Any]        =					"""arrow"""
				_lowerCAmelCase			:			List[Any]        =					"""prefix"""

				_lowerCAmelCase			:			str        =					make_file_instructions(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				)
				assert isinstance(UpperCamelCase_					,			UpperCamelCase_				)
				assert file_instructions.num_examples == 33
				assert file_instructions.file_instructions == [
				    {"filename": os.path.join(UpperCamelCase_					,			F"{name}-train.arrow"				), "skip": 0, "take": 33}
				]

				_lowerCAmelCase			:			List[str]        =					[SplitInfo(name="""train"""					,			num_examples=100					,			shard_lengths=[10] * 10				)]
				_lowerCAmelCase			:			int        =					make_file_instructions(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				)
				assert isinstance(UpperCamelCase_					,			UpperCamelCase_				)
				assert file_instructions.num_examples == 33
				assert file_instructions.file_instructions == [
				    {"filename": os.path.join(UpperCamelCase_					,			F"{name}-train-00000-of-00010.arrow"				), "skip": 0, "take": -1},
				    {"filename": os.path.join(UpperCamelCase_					,			F"{name}-train-00001-of-00010.arrow"				), "skip": 0, "take": -1},
				    {"filename": os.path.join(UpperCamelCase_					,			F"{name}-train-00002-of-00010.arrow"				), "skip": 0, "take": -1},
				    {"filename": os.path.join(UpperCamelCase_					,			F"{name}-train-00003-of-00010.arrow"				), "skip": 0, "take": 3},
				]





@pytest.mark.parametrize("""name, expected_exception"""					,			[(None, TypeError), ("""""", ValueError)]				)
def 							_UpperCAmelCase     (UpperCamelCase_       :       str					,			UpperCamelCase_       :       int				):
				'''simple docstring'''
				_lowerCAmelCase			:			Optional[int]        =					[SplitInfo(name="""train"""					,			num_examples=100				)]
				_lowerCAmelCase			:			List[str]        =					"""train"""
				_lowerCAmelCase			:			str        =					"""arrow"""
				_lowerCAmelCase			:			Dict        =					"""prefix_path"""
				with pytest.raises(UpperCamelCase_				):
								_lowerCAmelCase			:			str        =					make_file_instructions(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_					,			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

