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 				lowerCamelCase			(							__snake_case					):






      def __init__(      self  ,		__lowerCamelCase  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = False  ,		__lowerCamelCase = False  ,		__lowerCamelCase = None  ,		**__lowerCamelCase  ,		)     ->				Any:


          '''simple docstring'''







          super().__init__(
              __lowerCamelCase  ,		split=__lowerCamelCase  ,		features=__lowerCamelCase  ,		cache_dir=__lowerCamelCase  ,		keep_in_memory=__lowerCamelCase  ,		streaming=__lowerCamelCase  ,		num_proc=__lowerCamelCase  ,		**__lowerCamelCase  ,		)
          snake_case:						Optional[int]         =   path_or_paths if isinstance(__lowerCamelCase  ,		__lowerCamelCase						) else {self.split: path_or_paths}
          snake_case:						str         =   Csv(
              cache_dir=__lowerCamelCase  ,		data_files=__lowerCamelCase  ,		features=__lowerCamelCase  ,		**__lowerCamelCase  ,		)






      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          if self.streaming:
              snake_case:						Optional[int]         =   self.builder.as_streaming_dataset(split=self.split						)
          # Build regular (map-style) dataset
          else:
              snake_case:						Dict         =   None
              snake_case:						Union[str, Any]         =   None
              snake_case:						Dict         =   None
              snake_case:						Union[str, Any]         =   None

              self.builder.download_and_prepare(
                  download_config=__lowerCamelCase  ,		download_mode=__lowerCamelCase  ,		verification_mode=__lowerCamelCase  ,		base_path=__lowerCamelCase  ,		num_proc=self.num_proc  ,		)
              snake_case:						Union[str, Any]         =   self.builder.as_dataset(
                  split=self.split  ,		verification_mode=__lowerCamelCase  ,		in_memory=self.keep_in_memory						)
          return dataset





class 				lowerCamelCase			:






      def __init__(      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		**__lowerCamelCase  ,		)     ->				str:


          '''simple docstring'''







          if num_proc is not None and num_proc <= 0:
              raise ValueError(F"num_proc {num_proc} must be an integer > 0."						)

          snake_case:						Dict         =   dataset
          snake_case:						Dict         =   path_or_buf
          snake_case:						Optional[int]         =   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          snake_case:						Union[str, Any]         =   num_proc
          snake_case:						Tuple         =   """utf-8"""
          snake_case:						List[Any]         =   to_csv_kwargs






      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          snake_case:						str         =   self.to_csv_kwargs.pop("""path_or_buf"""  ,		__lowerCamelCase						)
          snake_case:						Optional[int]         =   self.to_csv_kwargs.pop("""header"""  ,		__lowerCamelCase						)
          snake_case:						List[str]         =   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:
                  snake_case:						int         =   self._write(file_obj=__lowerCamelCase  ,		header=__lowerCamelCase  ,		index=__lowerCamelCase  ,		**self.to_csv_kwargs						)
          else:
              snake_case:						Any         =   self._write(file_obj=self.path_or_buf  ,		header=__lowerCamelCase  ,		index=__lowerCamelCase  ,		**self.to_csv_kwargs						)
          return written






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase						)     ->				Tuple:


          '''simple docstring'''







          snake_case    , snake_case    , snake_case    , snake_case:						Any         =   args

          snake_case:						Union[str, Any]         =   query_table(
              table=self.dataset.data  ,		key=slice(__lowerCamelCase  ,		offset + self.batch_size						)  ,		indices=self.dataset._indices  ,		)
          snake_case:						Optional[Any]         =   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 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		**__lowerCamelCase						)     ->				int:


          '''simple docstring'''







          snake_case:						Optional[int]         =   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"""  ,		):
                  snake_case:						Union[str, Any]         =   self._batch_csv((offset, header, index, to_csv_kwargs)						)
                  written += file_obj.write(__lowerCamelCase						)

          else:
              snake_case    , snake_case:						Optional[int]         =   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



from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


__lowerCAmelCase    :  int										= {
    'id': '5733be284776f41900661182',
    'title': 'University_of_Notre_Dame',
    'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
    'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
    'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]},
}

@pytest.mark.parametrize(
    """task_cls"""			,	[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]			,	)
def   a_					(_lowerCAmelCase  :   List[Any]       )->				Tuple:
    snake_case:						Dict         =   task_cls()
    snake_case:						Any         =   asdict(_lowerCAmelCase       )
    snake_case:						Any         =   task_template_from_dict(_lowerCAmelCase       )
    assert task == reloaded
class 				lowerCamelCase			:






      def 			lowerCAmelCase_   (      self						)     ->				List[Any]:


          '''simple docstring'''







          snake_case:						str         =   LanguageModeling(text_column="""input_text"""						)
          assert {"input_text": "text"} == task.column_mapping






      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          snake_case:						List[Any]         =   Features({"""text""": Value("""string"""						)}						)
          snake_case:						Optional[int]         =   {"""text_column""": """input_text"""}
          snake_case:						List[Any]         =   LanguageModeling.from_dict(__lowerCamelCase						)
          assert "language-modeling" == task.task
          assert input_schema == task.input_schema





class 				lowerCamelCase			(							__snake_case					):






      def 			lowerCAmelCase_   (      self						)     ->				Optional[Any]:


          '''simple docstring'''







          snake_case:						Optional[int]         =   sorted(["""pos""", """neg"""]						)






      def 			lowerCAmelCase_   (      self						)     ->				List[str]:


          '''simple docstring'''







          snake_case:						str         =   TextClassification(text_column="""input_text"""  ,		label_column="""input_label"""						)
          self.assertDictEqual({"""input_text""": """text""", """input_label""": """labels"""}  ,		task.column_mapping						)






      def 			lowerCAmelCase_   (      self						)     ->				Any:


          '''simple docstring'''







          snake_case:						Any         =   Features({"""text""": Value("""string"""						)}						)
          # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
          snake_case:						List[str]         =   Features({"""labels""": ClassLabel}						)
          snake_case:						Any         =   {"""text_column""": """input_text""", """label_column""": """input_labels"""}
          snake_case:						List[str]         =   TextClassification.from_dict(__lowerCamelCase						)
          self.assertEqual("""text-classification"""  ,		task.task						)
          self.assertEqual(__lowerCamelCase  ,		task.input_schema						)
          self.assertEqual(__lowerCamelCase  ,		task.label_schema						)






      def 			lowerCAmelCase_   (      self						)     ->				Optional[Any]:


          '''simple docstring'''







          snake_case:						str         =   TextClassification(text_column="""input_text"""  ,		label_column="""input_label"""						)
          self.assertEqual(task.label_schema["""labels"""]  ,		__lowerCamelCase						)
          snake_case:						Any         =   task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels						)}						)						)
          self.assertEqual(task.label_schema["""labels"""]  ,		ClassLabel(names=self.labels						)						)





class 				lowerCamelCase			(							__snake_case					):






      def 			lowerCAmelCase_   (      self						)     ->				Optional[Any]:


          '''simple docstring'''







          snake_case:						List[Any]         =   QuestionAnsweringExtractive(
              context_column="""input_context"""  ,		question_column="""input_question"""  ,		answers_column="""input_answers"""						)
          self.assertDictEqual(
              {"""input_context""": """context""", """input_question""": """question""", """input_answers""": """answers"""}  ,		task.column_mapping						)






      def 			lowerCAmelCase_   (      self						)     ->				Any:


          '''simple docstring'''







          snake_case:						Any         =   Features({"""question""": Value("""string"""						), """context""": Value("""string"""						)}						)
          snake_case:						Tuple         =   Features(
              {
                  """answers""": Sequence(
                      {
                          """text""": Value("""string"""						),
                          """answer_start""": Value("""int32"""						),
                      }						)
              }						)
          snake_case:						Tuple         =   {
              """context_column""": """input_input_context""",
              """question_column""": """input_question""",
              """answers_column""": """input_answers""",
          }
          snake_case:						Optional[Any]         =   QuestionAnsweringExtractive.from_dict(__lowerCamelCase						)
          self.assertEqual("""question-answering-extractive"""  ,		task.task						)
          self.assertEqual(__lowerCamelCase  ,		task.input_schema						)
          self.assertEqual(__lowerCamelCase  ,		task.label_schema						)





class 				lowerCamelCase			(							__snake_case					):






      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          snake_case:						Any         =   Summarization(text_column="""input_text"""  ,		summary_column="""input_summary"""						)
          self.assertDictEqual({"""input_text""": """text""", """input_summary""": """summary"""}  ,		task.column_mapping						)






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


          '''simple docstring'''







          snake_case:						List[Any]         =   Features({"""text""": Value("""string"""						)}						)
          snake_case:						Dict         =   Features({"""summary""": Value("""string"""						)}						)
          snake_case:						Tuple         =   {"""text_column""": """input_text""", """summary_column""": """input_summary"""}
          snake_case:						List[str]         =   Summarization.from_dict(__lowerCamelCase						)
          self.assertEqual("""summarization"""  ,		task.task						)
          self.assertEqual(__lowerCamelCase  ,		task.input_schema						)
          self.assertEqual(__lowerCamelCase  ,		task.label_schema						)





class 				lowerCamelCase			(							__snake_case					):






      def 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







          snake_case:						str         =   AutomaticSpeechRecognition(audio_column="""input_audio"""  ,		transcription_column="""input_transcription"""						)
          self.assertDictEqual({"""input_audio""": """audio""", """input_transcription""": """transcription"""}  ,		task.column_mapping						)






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


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   Features({"""audio""": Audio()}						)
          snake_case:						Union[str, Any]         =   Features({"""transcription""": Value("""string"""						)}						)
          snake_case:						Any         =   {
              """audio_column""": """input_audio""",
              """transcription_column""": """input_transcription""",
          }
          snake_case:						str         =   AutomaticSpeechRecognition.from_dict(__lowerCamelCase						)
          self.assertEqual("""automatic-speech-recognition"""  ,		task.task						)
          self.assertEqual(__lowerCamelCase  ,		task.input_schema						)
          self.assertEqual(__lowerCamelCase  ,		task.label_schema						)





class 				lowerCamelCase			(							__snake_case					):






      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   sorted(["""pos""", """neg"""]						)






      def 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







          snake_case:						Dict         =   AudioClassification(audio_column="""input_audio"""  ,		label_column="""input_label"""						)
          self.assertDictEqual({"""input_audio""": """audio""", """input_label""": """labels"""}  ,		task.column_mapping						)






      def 			lowerCAmelCase_   (      self						)     ->				Optional[int]:


          '''simple docstring'''







          snake_case:						Optional[Any]         =   Features({"""audio""": Audio()}						)
          snake_case:						int         =   Features({"""labels""": ClassLabel}						)
          snake_case:						int         =   {
              """audio_column""": """input_image""",
              """label_column""": """input_label""",
          }
          snake_case:						Optional[Any]         =   AudioClassification.from_dict(__lowerCamelCase						)
          self.assertEqual("""audio-classification"""  ,		task.task						)
          self.assertEqual(__lowerCamelCase  ,		task.input_schema						)
          self.assertEqual(__lowerCamelCase  ,		task.label_schema						)






      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          snake_case:						List[str]         =   AudioClassification(audio_column="""input_audio"""  ,		label_column="""input_label"""						)
          self.assertEqual(task.label_schema["""labels"""]  ,		__lowerCamelCase						)
          snake_case:						int         =   task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels						)}						)						)
          self.assertEqual(task.label_schema["""labels"""]  ,		ClassLabel(names=self.labels						)						)





class 				lowerCamelCase			(							__snake_case					):






      def 			lowerCAmelCase_   (      self						)     ->				List[Any]:


          '''simple docstring'''







          snake_case:						Dict         =   sorted(["""pos""", """neg"""]						)






      def 			lowerCAmelCase_   (      self						)     ->				List[Any]:


          '''simple docstring'''







          snake_case:						Optional[Any]         =   ImageClassification(image_column="""input_image"""  ,		label_column="""input_label"""						)
          self.assertDictEqual({"""input_image""": """image""", """input_label""": """labels"""}  ,		task.column_mapping						)






      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          snake_case:						Dict         =   Features({"""image""": Image()}						)
          snake_case:						str         =   Features({"""labels""": ClassLabel}						)
          snake_case:						List[str]         =   {
              """image_column""": """input_image""",
              """label_column""": """input_label""",
          }
          snake_case:						Any         =   ImageClassification.from_dict(__lowerCamelCase						)
          self.assertEqual("""image-classification"""  ,		task.task						)
          self.assertEqual(__lowerCamelCase  ,		task.input_schema						)
          self.assertEqual(__lowerCamelCase  ,		task.label_schema						)






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


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   ImageClassification(image_column="""input_image"""  ,		label_column="""input_label"""						)
          self.assertEqual(task.label_schema["""labels"""]  ,		__lowerCamelCase						)
          snake_case:						str         =   task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels						)}						)						)
          self.assertEqual(task.label_schema["""labels"""]  ,		ClassLabel(names=self.labels						)						)





class 				lowerCamelCase			(							__snake_case					):






      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          snake_case:						List[str]         =   DatasetInfo(task_templates=QuestionAnsweringExtractive()						)
          snake_case:						int         =   Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}  ,		info=__lowerCamelCase						)
          assert isinstance(dataset.info.task_templates  ,		__lowerCamelCase						)
          assert len(dataset.info.task_templates						) == 1

          def keep_task(__lowerCamelCase						):
              return x

          def dont_keep_task(__lowerCamelCase						):
              snake_case:						Union[str, Any]         =   deepcopy(__lowerCamelCase						)
              snake_case:						int         =   0
              return out

          snake_case:						int         =   dataset.map(__lowerCamelCase						)
          assert mapped_dataset.info.task_templates == dataset.info.task_templates
          # reload from cache
          snake_case:						Optional[Any]         =   dataset.map(__lowerCamelCase						)
          assert mapped_dataset.info.task_templates == dataset.info.task_templates

          snake_case:						Optional[int]         =   dataset.map(__lowerCamelCase						)
          assert mapped_dataset.info.task_templates == []
          # reload from cache
          snake_case:						Union[str, Any]         =   dataset.map(__lowerCamelCase						)
          assert mapped_dataset.info.task_templates == []






      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          snake_case:						Any         =   Features({"""text""": Value("""string"""						), """label""": ClassLabel(names=("""pos""", """neg""")						)}						)
          snake_case:						Optional[int]         =   TextClassification(text_column="""text"""  ,		label_column="""label"""						)
          snake_case:						str         =   DatasetInfo(features=__lowerCamelCase  ,		task_templates=__lowerCamelCase						)
          snake_case:						Optional[Any]         =   Dataset.from_dict({"""text""": ["""A sentence."""], """label""": ["""pos"""]}  ,		info=__lowerCamelCase						)

          def process(__lowerCamelCase						):
              return example

          snake_case:						Optional[Any]         =   dataset.remove_columns("""label"""						)
          snake_case:						Tuple         =   modified_dataset.map(__lowerCamelCase						)
          assert mapped_dataset.info.task_templates == []


