

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 	__magic_name__				(       snake_case							):






     def __init__(							self		,	_lowercase		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = False		,	_lowercase = False		,	_lowercase = None		,	**_lowercase		,	)->    Optional[int]:
         super().__init__(
             _lowercase		,	split=_lowercase		,	features=_lowercase		,	cache_dir=_lowercase		,	keep_in_memory=_lowercase		,	streaming=_lowercase		,	num_proc=_lowercase		,	**_lowercase		,	)
         UpperCamelCase_      =	path_or_paths if isinstance(_lowercase		,	_lowercase  ) else {self.split: path_or_paths}
         UpperCamelCase_      =	Csv(
             cache_dir=_lowercase		,	data_files=_lowercase		,	features=_lowercase		,	**_lowercase		,	)






     def   UpperCAmelCase_							(							self  )->    Dict:
         # Build iterable dataset
         if self.streaming:
             UpperCamelCase_      =	self.builder.as_streaming_dataset(split=self.split  )
         # Build regular (map-style) dataset
         else:
             UpperCamelCase_      =	None
             UpperCamelCase_      =	None
             UpperCamelCase_      =	None
             UpperCamelCase_      =	None

             self.builder.download_and_prepare(
                 download_config=_lowercase		,	download_mode=_lowercase		,	verification_mode=_lowercase		,	base_path=_lowercase		,	num_proc=self.num_proc		,	)
             UpperCamelCase_      =	self.builder.as_dataset(
                 split=self.split		,	verification_mode=_lowercase		,	in_memory=self.keep_in_memory  )
         return dataset






class 	__magic_name__				:






     def __init__(							self		,	_lowercase		,	_lowercase		,	_lowercase = None		,	_lowercase = None		,	**_lowercase		,	)->    int:
         if num_proc is not None and num_proc <= 0:
             raise ValueError(F"num_proc {num_proc} must be an integer > 0."  )

         UpperCamelCase_      =	dataset
         UpperCamelCase_      =	path_or_buf
         UpperCamelCase_      =	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         UpperCamelCase_      =	num_proc
         UpperCamelCase_      =	"utf-8"
         UpperCamelCase_      =	to_csv_kwargs






     def   UpperCAmelCase_							(							self  )->    int:
         UpperCamelCase_      =	self.to_csv_kwargs.pop("path_or_buf"		,	_lowercase  )
         UpperCamelCase_      =	self.to_csv_kwargs.pop("header"		,	_lowercase  )
         UpperCamelCase_      =	self.to_csv_kwargs.pop("index"		,	_lowercase  )

         if isinstance(self.path_or_buf		,	(str, bytes, os.PathLike)  ):
             with open(self.path_or_buf		,	"wb+"  ) as buffer:
                 UpperCamelCase_      =	self._write(file_obj=_lowercase		,	header=_lowercase		,	index=_lowercase		,	**self.to_csv_kwargs  )
         else:
             UpperCamelCase_      =	self._write(file_obj=self.path_or_buf		,	header=_lowercase		,	index=_lowercase		,	**self.to_csv_kwargs  )
         return written






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    Dict:
         UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	args

         UpperCamelCase_      =	query_table(
             table=self.dataset.data		,	key=slice(_lowercase		,	offset + self.batch_size  )		,	indices=self.dataset._indices		,	)
         UpperCamelCase_      =	batch.to_pandas().to_csv(
             path_or_buf=_lowercase		,	header=header if (offset == 0) else False		,	index=_lowercase		,	**_lowercase  )
         return csv_str.encode(self.encoding  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	**_lowercase  )->    int:
         UpperCamelCase_      =	0

         if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0		,	len(self.dataset  )		,	self.batch_size  )		,	unit="ba"		,	disable=not logging.is_progress_bar_enabled()		,	desc="Creating CSV from Arrow format"		,	):
                 UpperCamelCase_      =	self._batch_csv((offset, header, index, to_csv_kwargs)  )
                 written += file_obj.write(_lowercase  )

         else:
             UpperCamelCase_     ,					UpperCamelCase_      =	len(self.dataset  ), self.batch_size
             with multiprocessing.Pool(self.num_proc  ) as pool:
                 for csv_str in logging.tqdm(
                     pool.imap(
                         self._batch_csv		,	[(offset, header, index, to_csv_kwargs) for offset in range(0		,	_lowercase		,	_lowercase  )]		,	)		,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size		,	unit="ba"		,	disable=not logging.is_progress_bar_enabled()		,	desc="Creating CSV from Arrow format"		,	):
                     written += file_obj.write(_lowercase  )

         return written



import unittest

from transformers.image_processing_utils import get_size_dict






class 	__magic_name__				(       unittest.TestCase							):






     def   UpperCAmelCase_							(							self  )->    int:
         # Test a dict with the wrong keys raises an error
         UpperCamelCase_      =	{"wrong_key": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"height": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"width": 224, "shortest_edge": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         # Test a dict with the correct keys is returned as is
         UpperCamelCase_      =	{"height": 224, "width": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	_lowercase  )

         UpperCamelCase_      =	{"shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         UpperCamelCase_      =	{"longest_edge": 224, "shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"longest_edge": 224, "shortest_edge": 224}  )

         # Test a single int value which  represents (size, size)
         UpperCamelCase_      =	get_size_dict(224  )
         self.assertEqual(_lowercase		,	{"height": 224, "width": 224}  )

         # Test a single int value which represents the shortest edge
         UpperCamelCase_      =	get_size_dict(224		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         # Test a tuple of ints which represents (height, width)
         UpperCamelCase_      =	get_size_dict((150, 200)  )
         self.assertEqual(_lowercase		,	{"height": 150, "width": 200}  )

         # Test a tuple of ints which represents (width, height)
         UpperCamelCase_      =	get_size_dict((150, 200)		,	height_width_order=_lowercase  )
         self.assertEqual(_lowercase		,	{"height": 200, "width": 150}  )

         # Test an int representing the shortest edge and max_size which represents the longest edge
         UpperCamelCase_      =	get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224, "longest_edge": 256}  )

         # Test int with default_to_square=True and max_size fails
         with self.assertRaises(_lowercase  ):
             get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
