

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   __UpperCamelCase      (   lowercase  ):




	def __init__(						self		:  Tuple						,		lowerCAmelCase		:  NestedDataStructureLike[PathLike]						,		lowerCAmelCase		:  Optional[NamedSplit] = None						,		lowerCAmelCase		:  Optional[Features] = None						,		lowerCAmelCase		:  str = None						,		lowerCAmelCase		:  bool = False						,		lowerCAmelCase		:  bool = False						,		lowerCAmelCase		:  Optional[int] = None						,		**lowerCAmelCase		:  Optional[Any]						,		):


		'''simple docstring'''







		super().__init__(
		    lowerCAmelCase						,		split=lowerCAmelCase						,		features=lowerCAmelCase						,		cache_dir=lowerCAmelCase						,		keep_in_memory=lowerCAmelCase						,		streaming=lowerCAmelCase						,		num_proc=lowerCAmelCase						,		**lowerCAmelCase						,		)
		UpperCAmelCase_    			=     path_or_paths if isinstance(lowerCAmelCase						,		lowerCAmelCase							) else {self.split: path_or_paths}
		UpperCAmelCase_    			=     Csv(
		    cache_dir=lowerCAmelCase						,		data_files=lowerCAmelCase						,		features=lowerCAmelCase						,		**lowerCAmelCase						,		)




	def  __A   (						self		:  str							):


		'''simple docstring'''







		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=lowerCAmelCase						,		download_mode=lowerCAmelCase						,		verification_mode=lowerCAmelCase						,		base_path=lowerCAmelCase						,		num_proc=self.num_proc						,		)
			UpperCAmelCase_    			=     self.builder.as_dataset(
			    split=self.split						,		verification_mode=lowerCAmelCase						,		in_memory=self.keep_in_memory							)
		return dataset


class   __UpperCamelCase      :




	def __init__(						self		:  Any						,		lowerCAmelCase		:  Dataset						,		lowerCAmelCase		:  Union[PathLike, BinaryIO]						,		lowerCAmelCase		:  Optional[int] = None						,		lowerCAmelCase		:  Optional[int] = None						,		**lowerCAmelCase		:  Dict						,		):


		'''simple docstring'''







		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  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.to_csv_kwargs.pop("path_or_buf"						,		lowerCAmelCase							)
		UpperCAmelCase_    			=     self.to_csv_kwargs.pop("header"						,		lowerCAmelCase							)
		UpperCAmelCase_    			=     self.to_csv_kwargs.pop("index"						,		lowerCAmelCase							)

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




	def  __A   (						self		:  Dict						,		lowerCAmelCase		:  int							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_    			=     args

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




	def  __A   (						self		:  Dict						,		lowerCAmelCase		:  BinaryIO						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  str						,		**lowerCAmelCase		:  List[Any]							):


		'''simple docstring'''







		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(lowerCAmelCase							)

		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						,		lowerCAmelCase						,		lowerCAmelCase							)]						,		)						,		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(lowerCAmelCase							)

		return written


import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin

class   __UpperCamelCase      (   unittest.TestCase       ,       lowercase  ):




	def  __A   (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_tool("document-question-answering"							)
		self.tool.setup()
		UpperCAmelCase_    			=     load_tool("document-question-answering"						,		remote=lowerCAmelCase							)




	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.tool(lowerCAmelCase						,		"When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.remote_tool(lowerCAmelCase						,		"When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		self.tool(document=lowerCAmelCase						,		question="When is the coffee break?"							)




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.remote_tool(document=lowerCAmelCase						,		question="When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)