



import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Optional[int]     =					np.inf

			def set_batch_size(lowerCamelCase__   ) -> None:
						nonlocal batch_size
						if isinstance(lowerCamelCase__						,				lowerCamelCase__   ):
									A_     :						Optional[int]     =					min(lowerCamelCase__						,				config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS   )
						elif isinstance(lowerCamelCase__						,				lowerCamelCase__   ):
									A_     :						Dict     =					min(lowerCamelCase__						,				config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS   )
						elif isinstance(lowerCamelCase__						,				lowerCamelCase__   ) and feature.dtype == "binary":
									A_     :						Optional[int]     =					min(lowerCamelCase__						,				config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS   )

			_visit(lowerCamelCase__						,				lowerCamelCase__   )

			return None if batch_size is np.inf else batch_size







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def __init__(self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = False	,				lowercase = False	,				lowercase = None	,				**lowercase	,				):
				super().__init__(
				    lowercase	,				split=lowercase	,				features=lowercase	,				cache_dir=lowercase	,				keep_in_memory=lowercase	,				streaming=lowercase	,				num_proc=lowercase	,				**lowercase	,				)
				A_     :						Dict     =					path_or_paths if isinstance(lowercase	,				lowercase							) else {self.split: path_or_paths}
				A_     :						Tuple     =					_PACKAGED_DATASETS_MODULES["""parquet"""][1]
				A_     :						Optional[Any]     =					Parquet(
				    cache_dir=lowercase	,				data_files=lowercase	,				features=lowercase	,				hash=lowercase	,				**lowercase	,				)


	def    _a     (self							):
				# Build iterable dataset
				if self.streaming:
							A_     :						Dict     =					self.builder.as_streaming_dataset(split=self.split							)
				# Build regular (map-style) dataset
				else:
							A_     :						List[str]     =					None
							A_     :						Optional[Any]     =					None
							A_     :						int     =					None
							A_     :						Any     =					None

							self.builder.download_and_prepare(
							    download_config=lowercase	,				download_mode=lowercase	,				verification_mode=lowercase	,				base_path=lowercase	,				num_proc=self.num_proc	,				)
							A_     :						Optional[Any]     =					self.builder.as_dataset(
							    split=self.split	,				verification_mode=lowercase	,				in_memory=self.keep_in_memory							)
				return dataset







class      _lowerCAmelCase							:


	def __init__(self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				A_     :						Tuple     =					dataset
				A_     :						Dict     =					path_or_buf
				A_     :						Dict     =					batch_size or get_writer_batch_size(dataset.features							)
				A_     :						Dict     =					parquet_writer_kwargs


	def    _a     (self							):
				A_     :						Dict     =					self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

				if isinstance(self.path_or_buf	,				(str, bytes, os.PathLike)							):
							with open(self.path_or_buf	,				"""wb+"""							) as buffer:
										A_     :						int     =					self._write(file_obj=lowercase	,				batch_size=lowercase	,				**self.parquet_writer_kwargs							)
				else:
							A_     :						List[str]     =					self._write(file_obj=self.path_or_buf	,				batch_size=lowercase	,				**self.parquet_writer_kwargs							)
				return written


	def    _a     (self	,				lowercase	,				lowercase	,				**lowercase							):
				A_     :						List[str]     =					0
				A_     :						Optional[Any]     =					parquet_writer_kwargs.pop("""path_or_buf"""	,				lowercase							)
				A_     :						List[str]     =					self.dataset.features.arrow_schema

				A_     :						List[str]     =					pq.ParquetWriter(lowercase	,				schema=lowercase	,				**lowercase							)

				for offset in logging.tqdm(
				    range(0	,				len(self.dataset							)	,				lowercase							)	,				unit="""ba"""	,				disable=not logging.is_progress_bar_enabled()	,				desc="""Creating parquet from Arrow format"""	,				):
							A_     :						Tuple     =					query_table(
							    table=self.dataset._data	,				key=slice(lowercase	,				offset + batch_size							)	,				indices=self.dataset._indices if self.dataset._indices is not None else None	,				)
							writer.write_table(lowercase							)
							written += batch.nbytes
				writer.close()
				return written




'''simple docstring'''




import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						Dict     =					10


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4]
				A_     :						List[str]     =					[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				A_     :						Tuple     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						List[Any]     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						Any     =					"""It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
				A_,   A_     :						Optional[int]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					""""""
				A_,   A_     :						Union[str, Any]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						List[Any]     =					(
				    """It was the year of Our Lord one thousand seven hundred and """
				    """seventy-five\n\nSpiritual revelations were conceded to England """
				    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
				)
				A_,   A_     :						int     =					process_story(lowercase							)

				A_     :						List[Any]     =					[
				    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
				    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
				]
				self.assertEqual(lowercase	,				lowercase							)

				A_     :						Tuple     =					["""It was the best of times."""]
				self.assertEqual(lowercase	,				lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					torch.tensor([1, 2, 3, 4]							)
				A_     :						Union[str, Any]     =					torch.tensor([1, 1, 1, 1]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				0							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
				A_     :						List[str]     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				23							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Optional[Any]     =					torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
				A_     :						Tuple     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				1							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						List[str]     =					101
				A_     :						str     =					torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
				A_     :						int     =					torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

				A_     :						Optional[Any]     =					compute_token_type_ids(lowercase	,				lowercase							)
				np.testing.assert_array_equal(lowercase	,				lowercase							)